Airway sensing, including airflow sensing

Abstract

A medical device may include an acoustic sensor. The acoustic sensor is configured to sense upper airway airflow of a patient to generate a signal. The medical device also may include a control portion configured to determine respiratory information based on the signal. The medical device also may include a stimulation element configured to apply electrical stimulation to an upper airway patency-related tissue of a patient based on the respiratory information.

Description

1618.294.1 1 11AIRWAY SENSING, INCLUDING AIRFLOW SENSINGBackground

[0001] A significant portion of the population suffers from various forms of sleep- related issues, some of which may involve sleep disordered breathing (SDB) and / or other conditions. In some patients, external breathing therapy devices and / or mere surgical interventions may fail to treat the sleep disordered breathing behavior.Brief Description of the Drawings

[0002] FIG. 1 A is a diagram schematically representing an example method and / or example device for acoustic sensing of upper airway airflow.

[0003] FIG. 1 B is a diagram schematically representing an example method and / or example device for acoustic sensing of upper airway airflow and accelerometer sensing of respiration.

[0004] FIG. 2A is a diagram schematically representing an example method and / or example device in relation to a target tissue.

[0005] FIG. 2B is a diagram including a front view schematically representing a patient’s body including example implantable components and example external elements of example methods and / or example devices.

[0006] FIG. 2C is a block diagram of a control portion.

[0007] FIG. 3A is a diagram schematically representing an example implantable medical device (IMD) including an acoustic sensor.

[0008] FIG. 3B is a diagram schematically representing an example IMD including an acoustic sensor adjacent to a window.

[0009] FIG. 3C is a diagram schematically representing an example IMD including an acoustic sensor within a header.

[0010] FIG. 3D is a diagram schematically representing an example IMD including an acoustic sensor and an antenna.

[0011] FIG. 3E is a diagram schematically representing an example IMD including an acoustic sensor attached to a lead.1618.294.1 1 12

[0012] FIG. 4A is a diagram schematically representing an example electrical assembly including a power element, a coil, and a printed circuit board assembly (PC BA).

[0013] FIG. 4B is a diagram schematically representing an example device including the electrical assembly of FIG. 4A.

[0014] FIG. 4C is a diagram schematically representing another example device including the electrical assembly of FIG. 4A.

[0015] FIGS. 5A-5G illustrate cross-sectional views of example devices.

[0016] FIG. 6 is a chart illustrating an example sensor signal and a filtered sensor signal.

[0017] FIG. 7 is a chart illustrating an example intensity signal based on the filtered sensor signal of FIG. 6.

[0018] FIG. 8A is a flow diagram illustrating an example method for acoustically sensing respiration.

[0019] FIG. 8B is a flow diagram illustrating an example method for acoustically sensing sleep disordered breathing (SDB) events.

[0020] FIG. 8C is a flow diagram illustrating an example method for acoustically sensing snoring and SDB events.

[0021] FIG. 9 is a flow diagram illustrating another example method for acoustically sensing respiration.

[0022] FIG. 10 is a diagram schematically representing an example method and / or example device for acoustic sensing of respiration.

[0023] FIG. 11 is a flow diagram illustrating another example method for acoustically sensing respiration.

[0024] FIG. 12 includes charts illustrating example filtered sensor signals.

[0025] FIG. 13A is a chart illustrating a filtered sensor signal indicative of breathing sounds.

[0026] FIG. 13B is a chart illustrating an example envelope of the filtered sensor signal of FIG. 13A.

[0027] FIG. 13C is a chart illustrating an example acoustic sensor signal spectrum.1618.294.1 1 13

[0028] FIGS. 14A-14D are diagrams schematically representing example devices for detecting respiratory information.

[0029] FIGS. 15A and 15B are flow diagrams illustrating example methods for determining respiratory information.

[0030] FIG. 15C is a flow diagram illustrating an example method for applying stimulation.

[0031] FIG. 16A includes charts illustrating a filtered sensor signal and a signal indicative of heart beat sounds.

[0032] FIGS. 16B-16D are diagrams schematically representing example devices for detecting cardiac information.

[0033] FIGS. 17A-17I are diagrams schematically representing example devices for detecting respiratory information and cardiac information.

[0034] FIG. 18A is a diagram illustrating an example deployment of an IMD in a neck region.

[0035] FIG. 18B is a diagram illustrating example implant locations for an IMD in a head-and-neck region.

[0036] FIGS. 19A-19D are diagrams, including side views, illustrating example IMDs for sensing and / or stimulation.

[0037] FIG. 20A is a diagram schematically representing an example device including a cuff configuration implanted relative to a nerve.

[0038] FIG. 20B is a sectional view schematically representing the implanted device of FIG. 20A.

[0039] FIG. 21 A is a diagram schematically representing another example device including a cuff configuration implanted relative to a nerve.

[0040] FIG. 21 B is a sectional view schematically representing the implanted device of FIG. 21A.

[0041] FIG. 22A is a diagram including a side view schematically representing a stimulation lead comprising an example stimulation portion including an anchor structure.

[0042] FIGS. 22B and 22C are each a sectional view schematically representing an example implementation of the example stimulation portion of FIG. 22A.1618.294.1 1 14

[0043] FIG. 23A is a diagram including a side view schematically representing an example stimulation portion and / or sensor portion including an anchor structure.

[0044] FIGS. 23B and 23C each are a diagram including a side view schematically representing an example anchor element.

[0045] FIGS. 24A-24C are each a diagram including a side view schematically representing an example portion of a stimulation lead and / or sensor lead including an anchor structure.

[0046] FIG. 24D is a sectional view schematically representing an example stimulation portion and / or sensor portion including an anchor structure.

[0047] FIGS. 24E and 24F are each a diagram schematically representing an example stimulation lead and / or sensor lead including anchor portions.

[0048] FIGS. 25A and 25B are diagrams including a top view and a side view, respectively, schematically representing example anchor structures.

[0049] FIGS. 25C-25E are each a side view schematically representing a portion of an example stimulation lead and / or sensor lead including tines as a fixation structure.

[0050] FIGS. 25F-25H are each a diagram including a side plan view schematically representing an example stimulation lead and / or sensor lead including example tines as part of a fixation structure.

[0051] FIGS. 26A-26C are each a diagram including a sectional view of an example paddle-style stimulation portion including example anchor portions.

[0052] FIGS. 27A, 27D and FIGS. 27B, 270 are diagrams including a top plan view and bottom plan view (respectively) of an example paddle-style stimulation portion including example anchor portions.

[0053] FIG. 27E is a diagram including a side plan view of an example connector portion including a helical-shaped anchor structure.

[0054] FIGS. 28A and 28B are block diagrams schematically representing example control portions.

[0055] FIG. 280 is a block diagram schematically representing an example user interface.1618.294.1 1 15

[0056] FIG. 29 is a block diagram schematically representing example communication arrangements between an IMD and external devices.

[0057] FIG. 30 is a block diagram illustrating an example device for detecting respiration and / or applying stimulation based on the detected respiration.

[0058] FIGS. 31 A and 31 B are diagrams including graphs schematically representing example filtered, sensed acceleration signals for two different sensor orientations.

[0059] FIG. 32A and 32B are diagrams schematically representing example methods for computing stimulation timings based on an accelerometer sensor and / or an acoustic sensor.

[0060] FIGS. 33A and 33B are diagrams schematically representing example methods for computing stimulation timings based on an accelerometer sensor and / or an acoustic sensor.

[0061] FIG. 34A and 34B are diagrams schematically representing example methods for computing stimulation timings based on an accelerometer sensor switching between a low frequency mode and a high frequency mode.

[0062] FIGS. 35A and 35B are diagrams schematically representing example methods for computing stimulation timings based on an accelerometer sensor switching between a low frequency mode and a high frequency mode.

[0063] FIGS. 36A and 36B are diagrams schematically representing an example sleep-wake determination portion and example method of sleep-wake determination, respectively.Detailed Description

[0064] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense. It is to be understood that features of the various examples1618.294.1 1 16 described herein may be combined, in part or whole, with each other, unless specifically noted otherwise.

[0065] Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another example includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another example. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0066] At least some examples of the present disclosure are directed to devices for diagnosis, therapy, and / or other care of medical conditions. At least some examples may comprise implantable devices and / or methods comprising use of implantable devices. However, in some examples, the methods and / or devices may comprise at least some external components. In some examples, a therapeutic medical device may comprise a combination of implantable components and external components. At least some examples include implantable medical devices including an acoustic sensor for sensing airway airflow (e.g., upper airway airflow) of a patient. The acoustic sensor may sense a pressure signal (or predominately a pressure signal or predominately a nonacceleration signal) using a microphone, such as a microelectromechanical system (MEMS), an optical mechanism, a piezoelectric crystal, or a piezoelectric film. In at least some examples, regarding the pressure signal, the acoustic signal may comprise vibration of surrounding tissue, which is induced by sound pressure waves. In some examples, the acoustic sensor may be understood as sensing vibration due to an acoustic signal using an accelerometer, such as a MEMS accelerometer or a piezoelectric accelerometer having a bandwidth greater than about 50 hertz.

[0067] An acoustic sensor utilizing an accelerometer configured for sensing an acoustic signal is referred to herein as an acoustic signal sensing accelerometer. An acoustic signal sensing accelerometer is distinguished from an accelerometer configured to sense motion, rotation, activity, body position, and / or posture, which1618.294.1 1 17 has a bandwidth less than about 10 hertz, in some examples. The acoustic sensor signal may be used to detect respiration, including an expiratory phase, an inspiratory phase, a start and stop of the expiratory phase, and a start and stop of the inspiratory phase, among other respiratory waveform features, morphology, etc. The acoustic sensor signal may also be used to detect heart beat sounds, from which heart rate and heart rate variability may be determined. The acoustic sensor signal may also be used to detect and / or monitor sleep disordered breathing (SDB) events, asthma, chronic obstructive pulmonary disease (COPD), or other disorders.

[0068] At least some of the example devices and / or example methods may relate to sleep disordered breathing (SDB) care, which may include monitoring, diagnosis, evaluation, and / or treatment, which may include stimulation in some examples. At least some examples include implantable medical devices (IMDs) including housings having shapes and features configured for fixation to specific tissues within a patient, configured for ease of access and delivery, and / or configured for effective therapy (e.g., stimulation). At least some example implantable medical devices include structures and configurations within a housing of the IMDs which are conducive to reducing a size of the IMDs and / or to implementing the above-mentioned sizes and / or shapes facilitating fixation. At least some example implantable medical devices include structures and configurations regarding external surfaces of a housing of the IMDs which are conducive to delivery and / or securely fixating the housing relative to anchoring tissues. Among other target tissues for stimulation and / or sensing, at least some target tissues include tissues of the head and / or neck regions which include nerves, muscles, and / or other tissues (e.g., tendons, bones, cartilage, etc.) related to treating sleep disordered breathing such as, but not limited to, obstructive sleep apnea. These target tissues may directly or indirectly relate to promoting upper airway patency. Other target tissues also may include those tissues relating to treating pelvic disorders such as (but not limited to) treating urinary and / or fecal incontinence. In some examples, target tissues may generally comprise any nerves and / or muscles suitable for peripheral tissue stimulation therapies.1618.294.1 1 18

[0069] At least some upper airway patency-related tissues may comprise a hypoglossal nerve, genioglossus muscle, infrahyoid muscles (e.g., infrahyoid strap), and / or infrahyoid muscle (IHM)-innervating nerves (e.g., which may include the ansa cervicalis nerve loop and / or its branches), among other nerves and / or muscles. In some examples, the electrical stimulation may be applied to other nerves and / or muscles (e.g., phrenic nerve, diaphragm muscle) which may generally contribute to alleviating sleep disordered breathing such as via initiating, modulating, etc. various general breathing functions, reflexes, etc., but without directly controlling upper airway patency tissues (e.g., genioglossus muscle, infrahyoid strap muscles, etc.).

[0070] These examples, and additional examples, are further described in association with at least FIGS. 1 A-36B.

[0071] FIG. 1 A is a block diagram schematically representing an example arrangement 20A (an example device and / or example method) including an implantable medical device (IMD) 22A in operable relation to an upper airway 30 of a patient. In some examples, the IMD 22A may include an acoustic sensing element 24, such that the IMD 22A may be in an acoustic sensing relation with the upper airway 30. The upper airway 30 facilitates airflow 32 for respiration of a patient. In the upper airway of a patient during respiration, in some examples, the airflow can be characterized in three primary regimes: turbulent airflow 34, transitional airflow 36, and laminar airflow 38. These types of airflow depend on the velocity, the size of the airway, and the overall dynamics of airflow through the respiratory system.

[0072] Turbulent airflow 34 is chaotic and irregular, with random fluctuations in air pressure and velocity. The air moves in swirls and eddies, creating vortices. Turbulent airflow 34 typically occurs in areas of the upper airway where the flow velocity is high and the airway diameter is large. In the upper airway, turbulence is most commonly found in the nasal passages, pharynx, and larynx (e.g., 3161 , 3162, and 3164 of FIG. 18B). T urbulence is more likely to occur when the air is moving rapidly (high velocity), especially when it encounters an abrupt change in airway diameter or direction. In turbulent airflow, the Reynolds number, a dimensionless number that predicts whether flow is laminar or turbulent, is high.1618.294.1 1 19One example Reynolds threshold is typically about 4,000, above which turbulence is more likely. Turbulence increases resistance to airflow and can cause noisy breathing (such as snoring or stridor). Turbulent airflow is less efficient for gas exchange compared to laminar airflow 38 due to the chaotic nature of the movement.

[0073] Transitional airflow 36 is a mix of laminar airflow 38 and turbulent airflow 34. The air moves in a more structured way than in turbulence, but with some irregularities. Transitional airflow 36 often transitions from laminar to turbulent flow or vice versa. Transitional airflow 36 is commonly found in areas where the airway size is intermediate, such as in the trachea or bronchi. Transitional airflow 36 can occur when air velocity is moderate and the airway diameter is neither too large nor too small. The Reynolds number in this region typically falls between about 2,000 and about 4,000. While transitional airflow 36 is more efficient than turbulent airflow 34, transitional airflow still presents more resistance than purely laminar airflow 38. Transitional airflow 36 may occur when airflow changes speed, direction, or interacts with the walls of the airways in an irregular manner. Transitional airflow 36 is less stable than laminar airflow 38, making transitional airflow more prone to developing into turbulence if conditions change.

[0074] Laminar airflow 38 is smooth, with the air moving in parallel layers, with little to no mixing between them. The velocity of the air is slower and more uniform than in turbulent airflow 34. Laminar airflow 38 generally occurs in the lower parts of the airways, such as the smaller bronchioles, where the diameter is smaller and the velocity is lower. In the upper airway, laminar airflow 38 may occur in areas like the nose during normal breathing when air moves slowly and smoothly. Laminar airflow 38 happens at low airflow velocities and in smaller diameter airways. The Reynolds number is low (typically less than about 2,000), indicating smooth, ordered flow. Laminar airflow 38 is highly efficient and causes minimal resistance. Laminar airflow 38 is ideal for gas exchange as the smooth movement of air allows for optimal contact with the airway walls and alveolar surface for oxygen exchange.

[0075] The type of airflow that predominates in a patient's upper airway can influence how easily air moves in and out of the lungs, and can have clinical1618.294.1 1 110 implications, especially in conditions like asthma, chronic obstructive pulmonary disease (COPD), or upper airway obstructions (e.g., obstructive sleep apnea (OSA)). Acoustic sensing element 24 may sense turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38. In some examples, acoustic sensing element 24 may include a microphone, which may include a microelectromechanical system (MEMS), an optical mechanism, a piezoelectric crystal, or a piezoelectric film, or an acoustic signal sensing accelerometer to sense acoustic energy from upper airway 30 airflow 32.

[0076] As used herein, acoustic energy is the energy carried by sound waves as they travel through a medium, such as air, water, or tissues of a patient. Sound waves within the patient cause vibrations in the cells and fluids through which they pass. These vibrations (e.g., rhythmic pressure changes) through air, fluids, and / or tissues may be detected by acoustic sensing element 24. In some instances, the rhythmic pressure changes may sometimes be referred to as sound pressure waves.

[0077] FIG. 1 B is a block diagram schematically representing an example arrangement 20B (an example device and / or example method). Arrangement 20B is similar to arrangement 20A of FIG. 1 A, except that arrangement 20B may include an implantable medical device 22B further including an accelerometer sensing element 28 (e.g., accelerometer) for detecting respiration 40 by sensing frequencies less than about 10 hertz (e.g., by sensing motion and / or rotation as opposed to sensing an acoustic signal). Respiration 40 may include inhalation 42 (e.g., an inspiratory phase) and exhalation 44 (e.g., an expiratory phase). In some examples, the detection of respiration 40 via the accelerometer sensing element 28 may be based, at least in part, on sensing respiratory movement (e.g., rotational movement) at a respiratory body portion caused by breathing and / or attempted breathing. In some examples, the respiratory body portion may comprise a chest (e.g., chest wall), abdomen (e.g., abdominal wall), neck (e.g., neck tissue), and / or other body portion exhibiting respiratory movement indicative of respiration or attempted respiration.

[0078] The accelerometer sensing element 28 may be securable to a patient to provide a sensor signal indicative of respiration 40 of a patient. In one example,1618.294.1 1 11 1 accelerometer sensing element 28 includes an implantable accelerometer to sense changes in acceleration indicative of respiration of the patient. Features may be extracted from the accelerometer signal to determine each respiratory phase of the patient. In one example, a midpoint of each respiratory phase (e.g., inspiratory phase 42 and / or expiratory phase 44) of the patient may be determined based on the sensor signal from accelerometer sensing element 28. In some examples, a midpoint of each respiratory phase is determined based on zero crossings of the sensor signal as described in U.S. Patent Application No. 18 / 287,205, filed October 17, 2023, and entitled “RESPIRATION SENSING”; and PCT Application No. PCT / US24 / 27579, filed May 3, 2024, and entitled “RESPIRATION SENSING”; which are hereby incorporated by reference herein in their entireties. As further described below at least with reference to FIGS. 31 A and 31 B, a zero crossing refers to a portion of a respiratory waveform (obtained via a filtered accelerometer signal) at which an amplitude changes from (e.g., crosses over) a negative amplitude to a positive amplitude (or vice versa) with the amplitude having a value of zero at the crossing point. In some examples, the midpoint may sometimes be referred to as a midpoint of a slope of an inspiratory phase or a midpoint of a slope of an expiratory phase of the respiratory waveform. The extracted features (e.g., midpoints) may be used by a control portion to control (e.g., synchronize, trigger, etc.) the delivery of therapy (e.g., stimulation) relative to the appropriate respiratory phase of the patient, for diagnostic storage and / or display, and / or for other purposes.

[0079] In one example, accelerometer sensing element 28 may include a single axis accelerometer having one measurement axis to provide one corresponding sensor signal. In other examples, accelerometer sensing element 28 may include a multiple axis accelerometer having more than one measurement axis, such as at least two orthogonal measurement axes to provide two corresponding sensor signals or three axes (e.g., a three axis accelerometer) to provide three corresponding sensors signals. Each sensor signal may be filtered by linear filters (e.g., low pass, high pass, band pass) or non-linear filters (e.g., median filter) to recover the low-frequency respiration signal while rejecting measurement noise, muscle noise, cardiac noise, and / or other noise. In some examples, the1618.294.1 1 112 sensor signals from each axis may be combined to form one signal including the information from each axis, such as via a root sum of squares.

[0080] In some examples, at least some of the example methods (and / or example devices) of the present disclosure regarding respiratory determinations may be performed (or comprise) a single three axis accelerometer sensor. In some examples, the single three axis accelerometer is an implantable element. In some examples, the single three axis accelerometer may be external to the patient’s body.

[0081] In some examples, the accelerometer sensing element 28 may be used to delineate respiration 40 (e.g., detecting respiratory phases but not distinguishing between inhalation and exhalation), while the acoustic sensing element 24 may be used to identify the inspiratory phases and the expiratory phases in the delineated respiration sensed by the accelerometer sensing element. In some examples, the acoustic sensing element 24 may be used in combination with the accelerometer sensing element 28 to detect respiration in response to the accelerometer sensor signal being insufficient (e.g., having a confidence factor below a specified threshold) for independently distinguishing inhalation 42 from exhalation 44. In some examples, once inhalation 42 and exhalation 44 phases of the accelerometer sensor signal are identified using the acoustic sensing element 24, the acoustic sensing element 24 may be disabled (e.g., turned off). For example, the acoustic sensing element 24 may be turned on to assign the correct inhalation phases and / or exhalation phases to the accelerometer sensor signal and then turned off, such that respiration (including inhalation and / or exhalation phases) may thereafter be determined via only the accelerometer sensor signal. In some examples, if the accelerometer sensor signal quality or confidence factor drops, the acoustic sensing element 24 may be turned on again. Alternatively, the acoustic sensing element 24 may be periodically turned on (e.g., within a range between about every 8 to 40 breaths) to confirm inhalation and / or exhalation phases of the accelerometer sensor signal. Sensing respiration via an accelerometer sensor and / or in combination with an acoustic sensor is further described below with reference to FIGS. 30- 35B.1618.294.1 1 113

[0082] In some examples, in which acoustic sensing element 24 is an acoustic signal sensing accelerometer element, accelerometer sensing element 28 may be provided by a different mode of acoustic sensing element 24 such that accelerometer sensing element 28 may be excluded. In these examples, acoustic sensing element 24 may be operated in a first mode (e.g., bandwidth greater than about 50 hertz) to sense acoustic signals and in a second mode (e.g., bandwidth less than about 10 hertz) to sense motion, rotation, activity, body position, and / or posture. The acoustic sensing element 24 may be switched between the first mode and the second mode based on a selected schedule, which may include a fixed schedule or a variable schedule, or based on a specified criteria (e.g., confidence factor).

[0083] FIG. 2A is a block diagram schematically representing an example arrangement 50 (an example device and / or example method) including an implantable medical device (IMD) 52 in operable relation to target tissue(s) 60. In some examples, the IMD 52 may include a sensing element 54 (e.g., an acoustic sensing element 24 of FIGS. 1 A-1 B and / or an accelerometer sensing element 28 of FIG. 1 B), a stimulation element 56, and / or other element 58 (or function), such that the IMD 52 may be in sensing relation, stimulating relation, and / or other relation with the target tissue(s) 60. In some examples, the IMD 52 may comprise multiple components with the sensing element 54 and stimulation element 56 being present in a single component or being present in separate components, which are in communication with each other. At least some further example IMDs are described later in association with at least FIGS. 3A-5G, 18A, and 19A-21 B.

[0084] FIG. 2B is a block diagram schematically representing a patient’s body 100, including example target portions 110-134 at which at least some example sensing element(s), stimulation element(s), and / or other elements may be employed to implement at least some examples of the present disclosure. As shown in FIG. 2B, the patient’s body 100 includes a head-and-neck portion 110, including head 1 12 and a neck region 1 14, which includes an upper neck region, a mid neck region, and a lower neck region. The upper neck region (which sometimes may be called a head-and-neck transition region) may include a mandible region. The lower neck region (which sometimes may be called a neck-1618.294.1 1 114 and-torso transition region) may include a clavicle region. The mid neck region extends between the upper neck region and the lower neck region. Head-and- neck portion 110 includes cranial tissue, nerves, etc., and upper airway 116 (e.g., nerves, muscles, tissues), etc. which primarily extends through and within the neck 1 14. As further shown in FIG. 2B, the patient’s body 100 includes a torso 120, which includes various organs, muscles, nerves, other tissues, such as but not limited to those in pectoral region 122 (e.g., lungs 126, cardiac 127), abdomen 124, and / or pelvic region 129 (e.g., urinary or bladder), anal, reproductive, etc.). As further shown in FIG. 2B, the patient’s body 100 includes limbs 130, such as arms 132 and legs 134.

[0085] It will be understood that various sensing elements and / or stimulation elements as described throughout the various examples of the present disclosure may be deployed within the various regions of the patient’s body 100 to sense and / or otherwise diagnose, monitor, treat various physiologic conditions such as, but not limited to those examples described below in association with FIGS. 3A- 36B. In some such examples, a stimulation element 117 may be located in or near the upper airway 1 16 for treating sleep disordered breathing and / or be located near other nerves and / or muscles in other locations (e.g., torso, other) for treating sleep disordered breathing. In some examples, a sensing element 128 (e.g., an acoustic sensing element 24 of FIGS. 1 A-1 B and / or an accelerometer sensing element 28 of FIG. 1 B) may be located anywhere within the neck 114, head 1 12, and / or torso 120 (or other body regions) to sense physiologic information for providing patient care (e.g., SDB, other). Sleep disordered breathing (SDB) may comprise obstructive sleep apnea (OSA), central sleep apnea (OSA), mixed sleep apnea, and / or other conditions. Moreover, in some examples regardless of their location (e.g., neck 1 14, torso 120, other), the stimulation element 1 17 and / or sensing element 128 may be used to provide patient care for other conditions, at least some of which are later identified below.

[0086] In some examples, at least a portion of the stimulation element 1 17 may include (i.e., form) part of an implantable device (e.g., IMD or IMD portion), such as an implantable pulse generator (IPG). The implantable device may comprise one or more components such as, but not limited to, the stimulation element 117.1618.294.1 1 115In some examples, the IPG may comprise a full-sized IMD which is chronically implantable in the torso 120 (e.g., pectoral region 122) but otherwise generally considered too large for implantation in the head-and-neck region 1 10. In some examples, the IPG may comprise a smaller-sized IPG which is sized and shaped to be chronically implanted in the head-and-neck region 110 or neck region 1 14 alone (e.g., lower neck region, mid neck region, and / or upper neck region), such as in locations, spaces, etc. (e.g., near larynx) in which a full-sized IPG would not reasonably fit. In some examples in which the IMD (e.g., IPG) is generally chronically implanted in the neck region 1 14, a portion of the IPG or the entire IPG may be chronically implanted at a transition of a lower portion of the neck 114 and an upper portion of the torso 120, such as (but not limited to) at or near a clavicle, manubrium, sternum, etc. In some examples, the smaller-sized IPG may sometimes be referred to as a microstimulator.

[0087] In some examples, the neck 114 may comprise submandibular locations in an anterior cervical region and includes locations superior to the clavicle. However, as noted above, in some examples, the neck 1 14 may comprise locations at or near transitions of the lower portion of the neck 114 and the upper portion of the torso 120.

[0088] The implantable components (e.g., IPG, other) may include a stimulation circuit, a control circuit, a power supply (e.g., non-rechargeable, rechargeable), communication elements (e.g., receivers, transmitters, transceivers), and / or other components. In some examples, the stimulation element 1 17 may also include a stimulation electrode and / or stimulation lead connected to the implantable pulse generator.

[0089] Further details regarding the location, structure, operation, and / or use of the sensing element 128, external element(s) 150, and / or stimulation element 1 17 are described below in association with FIGS. 2C-36B.

[0090] In some examples, at least a portion of the stimulation element 1 17 may include (e.g., form) part of an external device such as, but not limited to, the external device including a pulse generator (e.g., stimulation circuitry, control circuitry), power supply (e.g., rechargeable, non-rechargeable), and / or other components. Accordingly, in some examples, the external device may1618.294.1 1 116 sometimes be referred to as an external component. In some examples, a portion of the stimulation element 1 17 may be implantable and a portion of the stimulation element 117 may be external to the patient. In some such examples, the implantable portion of the stimulation element 117 may sometimes be referred to as a passive stimulation element at least because such implantable portion does not store power and / or does not generate a stimulation signal (and / or related control signals), but instead wirelessly receives such power and / or stimulation signal from an external component located externally of the patient. However, in some examples, the passive stimulation element 117 may receive such power and / or stimulation signal from another implantable portion of the medical device.

[0091] Accordingly, as further shown in FIG. 2B, the various sensing element(s) 128 and / or stimulation element(s) 1 17 implanted in the patient’s body may be in wireless communication (e.g., connection 137) with at least one external element 150. For example, sensing element 128 may include an acoustic sensor configured to wirelessly transmit the sensed acoustic signal to external element 150 (e.g., to a control portion of external element 150) or may include an accelerometer sensor configured to wirelessly transmit the sensed accelerometer signal to external element 150. The external element 150 may then use the sensed acoustic signal (and / or sensed accelerometer signal) to wirelessly control the stimulation element 117. In some examples, the sensing portion 171 of external element 150 may comprise an acoustic sensor (and / or an accelerometer sensor) instead of an implanted acoustic sensing element (and / or an implanted accelerometer sensor) 128. However, in some examples, an externally located acoustic (and / or accelerometer) sensor (e.g., 171 in FIG. 2B) may be provided in addition to an implanted acoustic (and / or accelerometer) sensor, whereby the externally-located acoustic (and / or accelerometer) sensor may operate in a complementary relation to the implanted acoustic (and / or accelerometer) sensor or independent of the implanted acoustic (and / or accelerometer) sensor.

[0092] As further shown in FIG. 2B, in some examples, the external element(s) 150 may be implemented via a wide variety of formats such as, but not limited to, at least one of the formats 151 including a patient support 152 (e.g., bed, chair, sleep mat, other), wearable elements 154 (e.g., finger, wrist, head, neck, shirt),1618.294.1 1 117 noncontact elements 156 (e.g., watch, camera, mobile device, bedside appliance, other), and / or other elements 158.

[0093] As further shown in FIG. 2B, in some examples, the external element(s) 150 may include one or more different modalities 170 such as (but not limited to) a sensing portion 171 , stimulation portion 172, power portion 174, communication portion 176, and / or other portion 178. The different portions 171 , 172, 174, 176, 178 may be combined into a single physical structure (e.g., package, arrangement, assembly), may be implemented in multiple different physical structures, and / or with just some of the different portions 171 , 172, 174, 176, 178 combined together in a single physical structure.

[0094] In some examples, the external stimulation portion 172 and / or implantable portions of stimulation element 1 17 may include at least some of substantially the same features and attributes of at least the stimulation arrangements, as further described below in association with FIGS. 2C-36B.

[0095] In some examples, the external power portion 174 and / or power components associated with stimulation element 117 (e.g., implantable portions) may include at least some of substantially the same features and attributes of at least the stimulation arrangements, as further described throughout the examples of the present disclosure. In some such examples, the respective power portion, components, etc. may include a rechargeable power element (e.g., supply, battery, circuitry elements) and / or non-rechargeable power elements (e.g., battery). In some examples, the external power portion 174 may include a power source by which a power component of the stimulation element 117 (e.g., implantable portions) may be recharged.

[0096] In some examples, the implantable components of (and / or associated with) stimulation element 117 which comprise power elements may receive power from (or via) external power portion 174 of external element 150 but not store the received power. Instead, the received power may be used immediately (or with minor delay) as part of transmitting a stimulation signal (received from or via stimulation portion 172) via stimulation element 1 17 to target tissues within the patient’s body.1618.294.1 1 118

[0097] In some examples, the wireless communication portion 176 (e.g., connection or link at 137) may be implemented via various forms of radio frequency communication and / or other forms of wireless communication, such as (but not limited to) magnetic induction telemetry, Bluetooth (BT), Bluetooth Low Energy (BLE), near infrared (NIF), near-field protocols, Wi-Fi, Ultra-Wideband (UWB), ultrasonic waves, and / or other short range or long range wireless communication protocols suitable for use in communicating between implanted components and external components in a medical device environment.

[0098] Examples are not so limited as expressed by other portion 178 via which other aspects of implementing medical care may be embodied in external element(s) 150 to relate to the various implanted and / or external components described above.

[0099] FIG. 2C schematically represents a control portion 190, which may include at least some of substantially the same features and attributes as the control portion 4500 in FIG. 28A described below. The control portion 190 may be used to implement at least some of the various example devices and / or example methods of the present disclosure as described herein. In some examples, the control portion 190 may form part of, and / or be in communication with, the sensing element 128 and / or the stimulation element 1 17 in FIG. 2B, external element(s) 150, and / or other medical device (or portions thereof), as further described below.

[0100] In some examples, the example devices (e.g., elements, components, portions, etc.) and / or example methods of FIGS. 1 A-2C may be implemented via, and / or comprise at least some of substantially the same features as, the example devices and / or example methods further described below in association with at least FIGS. 3A-36B.

[0101] FIG. 3A is a diagram schematically representing an example IMD 500a. The IMD 500a includes a housing 502a and a lead 506 coupled to the housing 502a. In this example, the IMD 500a includes an acoustic sensor 520 (e.g., microphone, piezoelectric sensor, acoustic signal sensing accelerometer) arranged in and / or integrated into the housing 502a. In some examples, acoustic sensor 520 may be an example implementation of acoustic sensing element 241618.294.1 1 119 of FIGS. 1A-1 B, sensing element 54 of FIG. 2A, or sensing element 128 of FIG. 2B. In some examples, acoustic sensor 520 may be included as part of an electrical assembly 1000 of FIG. 4A described below. In some examples where acoustic sensor 520 is an acoustic signal sensing accelerometer, the accelerometer may include an accelerometer proof mass that stays relatively motionless while the accelerometer body moves with housing 502a due to acoustic energy from airway turbulence, thereby producing a signal proportional to acceleration of the housing 502a. Lead 506 may electrically couple at least one electrode (e.g., for sensing and / or stimulation) to circuitry within housing 502a as further described below in association with at least FIGS. 18A and 19A-19C. However, in some examples, IMD 500a may omit the lead 506, thereby providing a leadless IMD.

[0102] The housing 502a may include an enclosure 504a including, in some examples, a metal, such as titanium, stainless steel, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol. In some examples, the enclosure 504a may include a non-magnetic material, such as a thermoplastic polymer (e.g., silicone, polysulfone, liquid-crystal polymer (LCP), polyether ketone (PEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU)), a thermoset material (e.g., epoxy), a blend polymer material (e.g., polyetheretherketone (PEEK)), a ceramic material (e.g., glass, aluminum oxide (AI2O3), zirconium oxide (ZO2)), or a combination thereof.

[0103] In some examples, enclosure 504a may enclose air between the inner sidewalls of the enclosure 504a and the acoustic sensor 520 (and other components (e.g., control portion, stimulation circuitry, power element, etc.) within the enclosure 504a) (e.g., as further described below with reference to FIG. 5A). In some examples, the acoustic sensor 520 (and other components within the enclosure 504a) may be encapsulated within a potting material (e.g., epoxy, polyurethane, liquid crystal polymer (LCP), or silicone, etc.), such that the potting material completely fills any empty space between the acoustic sensor 520 and inner sidewalls of the enclosure 504a (e.g., as further described below with reference to FIG. 5B). In some examples, the potting material may provide the enclosure 504a, (e.g., as further described below with reference to FIG. 5D). In1618.294.1 1 120 this way, acoustic energy transferred (e.g., from tissue of a patient) to the enclosure 504a is further transferred via the air or potting material to the acoustic sensor 520, which can generate a sensor signal corresponding to the acoustic energy. The sensor signal, which may correspond to physiologic information, may include respiratory information, cardiac information, and / or other information about the patient. The use of potting material between the inner sidewalls of the enclosure 504a and the acoustic sensor 520 may improve the transfer of acoustic energy to the acoustic sensor compared to examples having air (e.g., no internal potting material) between the inner sidewalls of the enclosure 504a and the acoustic sensor 520.

[0104] FIG. 3B is a diagram schematically representing an example IMD 500b. The IMD 500b includes a housing 502b and a lead 506 coupled to the housing 502b. In this example, the housing 502b may include an enclosure 504b including a metal (e.g., titanium, stainless steel, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol) or a non-magnetic material, such as a thermoplastic polymer (e.g., silicone, polysulfone, liquid-crystal polymer (LCP), polyether ketone (PEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU)), a thermoset material (e.g., epoxy), a blend polymer material (e.g., PEEK), a ceramic material (e.g., glass, aluminum oxide (AI2O3), zirconium oxide (ZO2)), or combination thereof. The housing 502b may also include a window 522 extending through the enclosure 504b. An acoustic sensor 520 (e.g., microphone, piezoelectric sensor, acoustic signal sensing accelerometer) may be arranged within the housing 502b adjacent (e.g., directly adjacent) to the window 522. In some examples, the window 522 may form a flexible membrane. In some examples where acoustic sensor 520 is an acoustic signal sensing accelerometer, the accelerometer may be mounted on the flexible membrane, which moves due to acoustic energy from airway turbulence, thereby producing a signal proportional to acceleration of the flexible membrane.

[0105] The material forming window 522 may mimic the density and / or consistency of the surrounding tissue and match the impedance of the surrounding tissue to minimize acoustic energy scattering. In some examples, the window 522 may include a metal (e.g., titanium, stainless steel, MP35N,1618.294.1 1 121 nickel-alloys, Pt- 1 r, or nitinol). In some examples, the metal window 522 may have a thickness (between an inner wall and an outer wall) within a range between about 0.05 millimeters and about 0.2 millimeters, such as about 0.1 millimeters, which may be less than a thickness of the walls of enclosure 504b. In some examples, the window 522 may include a polymeric material. In some examples, the polymeric window 522 may have a thickness (between an inner wall and an outer wall) within a range between about 0.4 millimeters and about 0.6 millimeters, such as about 0.5 millimeters.

[0106] In some examples, the acoustic sensor 520 (and other components within the enclosure 504b) may not be encapsulated within a potting material, such that air fills any empty space between the acoustic sensor 520 and the inner sidewalls of the enclosure 504b and the window 522 (e.g., as further described below with reference to FIG. 5A). In this way, acoustic energy transferred (e.g., from tissue of a patient) to the window 522 is further transferred via the air within the enclosure 504b to the acoustic sensor 520, which can generate a sensor signal corresponding to the acoustic energy. In some examples, the acoustic sensor 520 may be encapsulated by the material (e.g., silicone) of window 522. In this example, the acoustic sensor 520 may be coated in parylene to improve the hermeticity of the IMD 500b. The parylene may be applied to the acoustic sensor 520 via chemical vapor deposition. The parylene coating may prevent damage to the acoustic sensor 520 by preventing fluid ingress.

[0107] While the window 522 is illustrated on a major face of the housing 502b, in some examples, the window 522 may be arranged on a side face (e.g., perpendicular to the major face) of the housing 502b. While the window 522 is illustrated as having a rectangular shape, in some examples, the window 522 may have any suitable geometric shape (e.g., square, circular, elliptical, hexagonal, triangular, etc.), non-geometric shape, or asymmetrical shape.

[0108] FIG. 3C is a diagram schematically representing an example IMD 500c. The IMD 500c includes a housing 502c and a lead 506 coupled to the housing 502c. In this example, the housing 502c may include an enclosure 504c including a metal (e.g., titanium, stainless steel, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol) or a non-magnetic material, such as a thermoplastic polymer1618.294.1 1 122(e.g., silicone, polysulfone, liquid-crystal polymer (LCP), polyether ketone (PEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU)), a thermoset material (e.g., epoxy), a blend polymer material (e.g., PEEK), a ceramic material (e.g., glass, aluminum oxide (AI2O3), zirconium oxide (ZO2)), or combination thereof. The housing 502c may also include a header 524 attached to the enclosure 504c. The lead 506 may be attached to the header 524. An acoustic sensor 520 may be arranged within the header 524.

[0109] The material forming header 524 may mimic the density and / or consistency of the surrounding tissue and match the impedance of the surrounding tissue to minimize acoustic energy scattering. In some examples, the header 524 may include a potting material, such as epoxy, polyurethane, liquid crystal polymer (LCP), or silicone, that encapsulates the acoustic sensor 520 (and other components within the header 524). In this way, acoustic energy transferred (e.g., from tissue of a patient) to the header 524 is further transferred via the potting material to the acoustic sensor 520, which can generate a sensor signal corresponding to the acoustic energy. In this example, the acoustic sensor 520 (and other components within the header 524) may be coated in parylene to improve the hermeticity of the IMD 500c.

[0110] FIG. 3D is a diagram schematically representing an example IMD 500d. The IMD 500d includes a housing 502d and a lead 506 coupled to the housing 502d. In this example, the housing 502d may include an enclosure 504d including a metal (e.g., titanium, stainless steel, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol) or a non-magnetic material, such as a thermoplastic polymer (e.g., silicone, polysulfone, liquid-crystal polymer (LCP), polyether ketone (PEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU)), a thermoset material (e.g., epoxy), a blend polymer material (e.g., PEEK), a ceramic material (e.g., glass, aluminum oxide (AI2O3), zirconium oxide (ZO2)), or combination thereof. The housing 502d may also include a portion 526 (e.g., end cap) attached to the enclosure 504d. Unlike in IMD 500c of FIG. 3C where the lead is connected to the header 524, in IMD 500d the lead 506 may be attached to the enclosure 504d. An acoustic sensor 520 and an antenna 521 may be arranged within the portion 526, which is not directly connected to a lead.1618.294.1 1 123

[0111] The material forming portion 526 may be made of a non-magnetic material and mimic the density and / or consistency of the surrounding tissue and match the impedance of the surrounding tissue to minimize acoustic energy scattering. In addition, in examples where enclosure 504d is a metal enclosure, by arranging the antenna 520 within the non-magnetic portion 526 of the housing 502d rather than within the metal enclosure 504d, the magnetic field generated by the antenna 521 is not redirected or attenuated by the metal enclosure 504d, thereby improving coupling with an external device (e.g., external charger 4670, mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660 of FIG. 29). In some examples, the portion 526 may include a potting material, such as epoxy, polyurethane, liquid crystal polymer (LOP), or silicone, that encapsulates the acoustic sensor 520 and the antenna 521 . In this way, acoustic energy transferred (e.g., from tissue of a patient) to the portion 526 is further transferred via the potting material to the acoustic sensor 520, which can generate a sensor signal corresponding to the acoustic energy. In some examples, the antenna 521 may be used for wireless power transfer (WPT), inductive communications (e.g., inductive telemetry), and / or radio frequency (RF) communications (e.g., RF telemetry) with such communications including data, control, therapy (e.g., stimulation, sensing) and / or other types of signals, information, etc. In some examples, the acoustic sensor 520 and the antenna 521 may be coated in parylene to improve the hermeticity of the IMD 500d.

[0112] FIG. 3E is a diagram schematically representing an example IMD 500e. The IMD 500e includes a housing 502e and a lead 506 coupled to the housing 502e. In this example, the IMD 500e includes an acoustic sensor 520 (e.g., microphone, piezoelectric sensor, acoustic signal sensing accelerometer) integrated into and / or attached to the lead 506. The acoustic sensor 520 may be arranged anywhere along the lead 506 between the housing 502e and a distal end of the lead. By arranging acoustic sensor 520 along the lead 506, the acoustic sensor 520 may be arranged at a different location from the housing 502e (e.g., such as closer to the upper airway of the patient or near hard tissues, such as bone or cartilage). In this example, the acoustic sensor 520 may be1618.294.1 1 124 encapsulated within a potting material (e.g., epoxy, polyurethane, liquid crystal polymer (LCP), or silicone, etc.) and / or the acoustic sensor 520 may be coated with parylene.

[0113] The housing 502e may include an enclosure 504e including, in some examples, a metal, such as titanium, stainless steel, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol. In some examples, the enclosure 504e may include a non-magnetic material, such as a thermoplastic polymer (e.g., silicone, polysulfone, liquid-crystal polymer (LCP), polyether ketone (PEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU)), a thermoset material (e.g., epoxy), a blend polymer material (e.g., PEEK), a ceramic material (e.g., glass, aluminum oxide (AI2O3), zirconium oxide (ZO2)), or a combination thereof.

[0114] FIGS. 4A-5G address various example electrical assemblies, devices, etc., which in some examples may comprise at least some of substantially the same features as, or may comprise an example implementation of, the various examples described in association with at least FIGS. 1 A-3E.

[0115] FIG. 4A is a diagram schematically representing an example electrical assembly 1000 for a device (e.g., an IMD). Electrical assembly 1000 includes a coil 1004, a power element 1010, a printed circuit board assembly (PCBA) 1020, and a ferrite sheet 1028. In some examples, the coil 1004 may be used as an antenna for wireless power transfer (WPT), inductive communications (e.g., inductive telemetry), and / or radio frequency (RF) communications (e.g., RF telemetry) with such communications including data, control, therapy (e.g., stimulation, sensing) and / or other types of signals, information, etc. The coil 1004 may be used for inductive wireless power transfer and / or inductive communications at a first frequency at or below the self-resonance frequency of the coil. The same coil 1004 may be used as an antenna for RF communications at a second frequency above the self-resonance frequency of the coil. In some examples, the second frequency may be greater than at least ten times the first frequency. In some examples, the coil 1004 may be used for inductive wireless power transfer and / or inductive communications at a first frequency within a range, for example, between about 9 kilohertz and about 50 megahertz, such as1618.294.1 1 1256.78 megahertz or 13.56 megahertz. In some examples, the same coil 1004 may be used for RF communications at a second frequency within a range, for example, between about 100 megahertz and about 5 gigahertz, such as 400 megahertz or 2.4 gigahertz.

[0116] In some examples, the power element 1010 may be a liquid electrolyte battery (e.g., lithium-ion battery), a solid-state battery, a supercapacitor, or other suitable component configured to store energy that may be used to power the electrical assembly 1000 for a device. In some examples, the solid-state battery may include a thin-film solid-state electrolyte, such as (but not limited to) a lithium phosphorus oxynitride (LiPON) material.

[0117] The time required to recharge the power element 1010 of the electrical assembly 1000 is based upon the power element technology. For example, given a supercapacitor, a solid-state battery, and a liquid electrolyte battery each having the same energy capacity, in some examples the supercapacitor may be recharged from a 10 percent charge to a 90 percent charge faster than the solid- state battery, and the solid-state battery may be recharged from a 10 percent charge to a 90 percent charge faster than the liquid electrolyte battery. For example, when a supercapacitor is used as the power element 1010, the electrical assembly 1000 may be rapidly recharged from about a 10 percent charge to about a 90 percent charge by an external charger (e.g., 4670 of FIG. 29) in under about 90 seconds for example. When a solid-state battery is used as the power element 1010, the electrical assembly 1000 may be quickly recharged from about a 10 percent charge to about a 90 percent charge by an external charger in under about 10 minutes for example. When a liquid electrolyte battery is used as the power element 1010, the electrical assembly 1000 may be recharged from about a 10 percent charge to about a 90 percent charge by an external charger in about 20 to 30 minutes for example.

[0118] In examples in which a solid-state battery is used as the power element 1010, the power element 1010 and thus the electrical assembly 1000 may be made smaller since solid state batteries are more energy dense than supercapacitors and liquid electrolyte batteries. Supercapacitors and solid-state batteries are safer than liquid electrolyte batteries, since there is little risk of a1618.294.1 1 126 liquid electrolyte leaking and the risk of fire may be reduced. Supercapacitors can withstand more charge and discharge cycles (e.g., hundreds of thousands) than solid state batteries before degrading (e.g., storing less energy), and solid- state batteries can withstand more charge and discharge cycles (e.g., about 5000) than liquid electrolyte batteries (e.g., about 1000) before degrading. Supercapacitors have an additional benefit over both solid-state batteries and liquid electrolyte batteries in that supercapacitors do not contain any toxic metals (e.g., lithium) that may involve more special handling, sealing, etc. to permit use within a patient. In some examples, the power element 1010 may include two or more power storage technologies, such as a supercapacitor paired with a solid- state battery.

[0119] In the example illustrated in FIG. 4A, power element 1010 comprises a one-half obround shape (e.g., a first portion including a rectangular cuboid shape and a second portion with a semi-circular shape). In some examples, power element 1010 may have another suitable shape, such as a rectangular cuboid, a cylinder, etc. In this example, the coil 1004 is wrapped around (e.g., surrounding) the ferrite sheet 1028 and PCBA 1020 and includes a first end 1006 and a second end 1008 electrically coupled to the PCBA 1020. The ferrite sheet 1028 may cover the electrical components 1024 (e.g., semiconductor dies, passive components, traces, etc.) of the PCBA 1020 to shield the electrical components from electromagnetic interference (e.g., due to coil 1004, power element 1010, and / or external sources). In some examples, the coil 1004 may be wrapped around (e.g., surrounding) power element 1010 (e.g., around the rectangular cuboid portion). In some examples, the coil 1004 may be wrapped around (e.g., surrounding) both the power element 1010 (e.g., around the rectangular cuboid portion) and the PCBA 1020.

[0120] Power element 1010 includes a first terminal 1012 (e.g., positive terminal) and a second terminal 1014 (e.g., negative terminal) electrically coupled to the PCBA 1020. PCBA 1020 includes a printed circuit board (PCB) 1022 supporting a plurality of components 1024 (e.g., including an acoustic sensor). Components 1024 may include a control portion (e.g., 190 of FIG. 2C), a stimulation element (e.g., 56 of FIG. 2A or 117 of FIG. 2B), a sensing element1618.294.1 1 127(e.g., acoustic sensing element 24 of FIGS. 1 A-1 B, accelerometer sensing element 28 of FIG. 1 B, sensing element 54 of FIG. 2A, or sensing element 128 of FIG. 2B), an application specific integrated circuit (ASIC), a controller, a filter (e.g., 2006 described below with reference to FIG. 10), and / or other components or circuitry. In the example illustrated in FIG. 4A, the power element 1010 and the PCBA 1020 are laterally aligned. In some examples, the power element 1010 and the PCBA 1020 may have another suitable arrangement, such as the PCBA 1020 being perpendicular to the power element 1010. In some examples, the coil 1004 may be spaced apart from the power element 1010 by a distance as indicated at 101 1 to increase the wireless power transfer (WPT) efficiency. In some examples, distance 101 1 may be greater and about 1 millimeter, such as greater than about 2 millimeters.

[0121] FIG. 4B is a diagram schematically representing an example device 1040a including the electrical assembly 1000 of FIG. 4A. Device 1040a may include at least some of substantially the same features and attributes of at least the devices previously described in association with FIGS. 1 A-4A. Device 1040a includes a housing 1042a enclosing an electrical assembly 1000 of FIG. 4A (not visible in FIG. 4B). Housing 1042a includes an enclosure 1044a and a lead connector portion 1046 (e.g., flex connector including strain relief features) sealed (e.g., hermetically sealed) to the enclosure 1044a. The connector portion 1046 is coupled to a lead 1047. Lead 1047 may electrically couple at least one electrode (e.g., for sensing and / or stimulation) to electrical assembly 1000 within housing 1042a as further described below in association with at least FIGS. 18A and 19A-19C.

[0122] Enclosure 1044a includes a length (e.g., along the major axis) indicated at 1048, a width (e.g., along the minor axis) indicated at 1050, and a height (e.g., thickness) indicated at 1052. In some examples, the length 1048 of the enclosure 1044a is less than or equal to about 4 centimeters, the width 1050 of the enclosure 1044a is less than or equal to about 1.5 centimeters, and the height 1052 of the enclosure 1044a is less than or equal to about 2.5 centimeters. In some examples, a volume enclosed by the enclosure 1044a is within a range between about 0.5 cubic centimeters and about 5 cubic centimeters. In some1618.294.1 1 128 examples, a volume enclosed by the enclosure 1044a is within a range between about 5 cubic centimeters and about 20 cubic centimeters. In some examples, the enclosure 1044a is sized and / or shaped for implantation into a patient via a single incision having a length within a range between about 0.5 centimeters and about 3 centimeters. In some examples, the enclosure 1044a is sized and / or shaped for implantation into a patient via a single incision having a length within a range between about 0.5 centimeters and about 8 centimeters. In some examples, the enclosure 1044a is sized and / or shaped for percutaneous implantation into a patient. In some examples, the enclosure 1044a is sized and / or shaped to be implanted within a head-and-neck region of a patient. For example, enclosure 1044a may include a cross-sectional area (e.g., width 1050 times height 1052) less than about 1.5 square centimeters, a major axis (e.g., length 1048) less than about 1 .5 centimeters, and a volume less than about 3.0 cubic centimeters.

[0123] FIG. 4G is a diagram schematically representing another example device 1040b including the electrical assembly 1000 of FIG. 4A. Device 1040b may include at least some of substantially the same features and attributes of at least the devices previously described in association with FIGS. 1 A-4B. Device 1040b includes a housing 1042b enclosing an electrical assembly 1000 of FIG. 4A (not visible in FIG. 4G). Housing 1042b includes an enclosure 1044b, a window 1054 extending through the enclosure 1044b, and a lead connector portion 1046 (e.g., flex connector including strain relief features) sealed (e.g., hermetically sealed) to the enclosure 1044b. The connector portion 1046 is coupled to a lead 1047. In some examples, window 1054 may include at least some of substantially the same features and attributes as window 522 previously described in association with FIG. 3B.

[0124] In this example, housing 1042b may include a metal enclosure 1044b (e.g., stainless steel, titanium, MP35N, nickel-alloy, platinum-iridium alloy (Pt-lr), or nitinol) and a window 1054 extending through the metal enclosure 1044b. The coil 1004 and the acoustic sensor (e.g., one of components 1024) may be arranged within the housing 1042b adjacent to the window 1054. The window 1054 may include a ceramic material (e.g., alumina oxide or zirconia) or another1618.294.1 1 129 non-magnetic material, such as a thermoplastic polymer (e.g., silicone, polysulfone), a thermoset material, a blend polymer material (e.g., PEEK), or a combination thereof. Thus, by arranging the coil 1004 adjacent to the window 1054, the magnetic field generated by the coil 1004 is not redirected or attenuated by the metal enclosure 1044b, thereby improving coupling with an external device (e.g., external element 150 in FIG. 2B and / or external charger 4670, mobile device 4620, remote control 4640, clinician programmer 4650, or patient management tool 4660 of FIG. 29). In addition, by arranging the acoustic sensor (e.g., on PCBA 1020) adjacent to the window 1054, acoustic coupling to the acoustic sensor may be improved compared to if the acoustic sensor were adjacent to an inner sidewall of the enclosure 1044b. While the window 1054 is illustrated on a major face of the housing 1042b, in some examples, the window 1054 may be arranged on a side face (e.g., perpendicular to the major face) of the housing 1042b. In some examples, the window 1054 may be brazed to the metal enclosure 1044b.

[0125] FIGS. 5A-5G illustrate cross-sectional views of example devices 1200a- 1200g, respectively. In some examples, the example devices 1200a-1200g may include at least some of substantially the same features and attributes of, or may comprise an example implementation of, at least the devices described in association with FIGS. 1 A-4C. As illustrated in FIG. 5A, device 1200a includes a printed circuit board assembly (PCBA) 1020 and a housing 1202. PCBA 1020 includes a printed circuit board (PCB) 1022 and components 1024a and 1024b mounted on the PCB. While only two components 1024a and 1024b are shown in FIGS. 5A-5G, devices 1200a-1200g may include any number of components 1024, such three, four, five, or more. In some examples, component 1024b is an acoustic sensor (e.g., 24 of FIGS. 1 A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, or 520 of FIGS. 3A-3D). In this example, the housing 1202 encloses air 1204 between an inner wall 1203 of the housing and the acoustic sensor 1024b.

[0126] The device 1200b of FIG. 5B is similar to the device 1200a of FIG. 5A, except that device 1200b includes a potting material 1206 filling all space between the inner wall 1203 of the housing 1202 and the acoustic sensor 1024b, such as all space between the PCBA 1020 and the inner walls of the housing1618.294.1 1 1301202. In some examples, the potting material 1206 may include epoxy, polyurethane, liquid crystal polymer (LCP), or silicone. By filling the space between the inner wall 1203 of the housing 1202 and the acoustic sensor 1024b with a potting material, the transfer of acoustic energy from the housing 1202 to the acoustic sensor 1024b may be improved compared to device 1200a of FIG. 5A where the space is filled with air.

[0127] The device 1200c of FIG. 5C is similar to the device 1200b of FIG. 5B, except that in device 1200c the potting material 1206 fills a first portion of the space between the inner wall 1203 of the housing 1202 and the PCBA 1020 (e.g., including a control portion, such as 1024a), and air 1207 fills a second portion of the space between the inner wall 1203 of the housing 1202 and the PCBA 1020 (e.g., including the acoustic sensor 1024b). In some examples, the potting material 1206 may include epoxy, polyurethane, liquid crystal polymer (LCP), or silicone. By filling the space between (e.g., directly between) the inner wall 1203 of the housing 1202 and the acoustic sensor 1024b with air while the remaining portion of the space within the housing is filled with a potting material, the transfer of acoustic energy from the housing 1202 to the acoustic sensor 1024b may be improved compared to device 1200a of FIG. 5A due to the smaller volume of air 1207 within the housing 1202 of device 1200c.

[0128] The device 1200d of FIG. 5D is similar to the device 1200b of FIG. 5B, except that in device 1200d the potting material 1206 provides the housing for the device, such that device 1200d does not include the separate housing 1202 enclosing the potting material 1206. By encapsulating the acoustic sensor 1024b with a potting material and not an additional enclosure, the transfer of acoustic energy from the patient through the potting material 1206 to the acoustic sensor 1024b may be improved compared to device 1200a of FIG. 5A where the space is filled with air or the device 1200b of FIG. 5B where an additional material (e.g., 1202) encloses the potting material.

[0129] The device 1200e of FIG. 5E is similar to the device 1200d of FIG. 5D, except that device 1200e includes both a first potting material 1206 encapsulating the PCBA 1020 (including the acoustic sensor and the control portion) and a second potting material 1209 encapsulating the first potting material 1206. The1618.294.1 1 131 first potting material 1206 and the second potting material 1209 provide the housing for the device, such that device 1200e does not include the separate housing 1202 enclosing the potting material 1209. In some examples, the second potting material 1209 may be different from the first potting material 1206 and may include epoxy, polyurethane, liquid crystal polymer (LCP), or silicone. By encapsulating the acoustic sensor 1024b with a first potting material and a second potting material, the transfer of acoustic energy from the patient through the potting material 1206 and 1209 to the acoustic sensor 1024b may be improved compared to device 1200a of FIG. 5A where the space is filled with air.

[0130] The device 1200f of FIG. 5F is similar to the device 1200d of FIG. 5D, except that device 1200f includes both a first potting material 1206 encapsulating a first portion of the PCBA 1020 (e.g., including acoustic sensor 1024b) and a second potting material 1209 encapsulating a second portion of the PCBA 1020 (e.g., including a control portion, such as 1024a). The first potting material 1206 and the second potting material 1209 provide the housing for the device, such that device 1200f does not include the separate housing 1202 enclosing the potting material 1206 and 1209. In some examples, the second potting material 1209 may be the same or different from the first potting material and may include epoxy, polyurethane, liquid crystal polymer (LCP), or silicone. By encapsulating the acoustic sensor 1024b with a first potting material and the control portion with a second potting material, the first potting material 1206 may be selected to improve the transfer of acoustic energy from the patient through the potting material 1206 to the acoustic sensor 1024b (e.g., by matching the impedance of the potting material to the surrounding tissue), while the second potting material 1209 may be selected for hermetic performance, mechanical rigidity, and / or ease of manufacturing.

[0131] The device 1200g of FIG. 5G is similar to the device 1200a of FIG. 5A, except that in device 1200g the acoustic sensor 1024b directly contacts (e.g., is directly coupled to, is directly attached to, is pressed against) an inner wall 1203 of the housing 1202. An electrical connector 1208 (e.g., wires, ribbon connector, etc.) electrically connects the acoustic sensor 1024b to the PCBA 1020. By directly contacting the acoustic sensor 1024b to an inner wall 1203 of the housing1618.294.1 1 1321202 (or window, flexible membrane, header portion, or end cap or other portion as previously described in association with at least FIGS. 3A-3D and 4A-4C), the transfer of acoustic energy from the housing 1202 to the acoustic sensor 1024b may be improved compared to devices 1200a, 1200b, and 1200c of FIG. 5A-5C with air or potting material between the inner wall 1203 of the housing 1202 and the acoustic sensor 1024b.

[0132] FIG. 6 is a chart 1400 illustrating an example sensor signal 1402 and a filtered sensor signal 1404. Chart 1400 includes time in minutes on the x-axis and amplitude on the y-axis. Sensor signal 1402 may be obtained via an acoustic sensor, such as acoustic sensor 24 of FIGS. 1 A-1 B, sensor 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-5G. The sensor signal 1402 may represent an upper airway airflow 32 of a patient (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIGS. 1 A-1 B). The sensor signal 1402 may be band pass filtered. For example, the sensor signal 1402 may be band pass filtered via a high pass filter with a high pass frequency within a range between about 20 hertz and about 200 hertz and a low pass filter with a low pass frequency within a range between about 200 hertz and about 2000 hertz. The filter may reject at least one of ambient sound in the patient’s environment (e.g., environmental noise), snoring, speech, muscle motion (e.g., due to patient movement), and / or cardiac-based motion (e.g., heart sounds). In some examples, secondary filters may be used to filter sensor signal 1402 to detect and / or measure motion, snoring, or cardiac-based motion.

[0133] The filtered signal 1404 may be indicative of respiratory information of the patient. As shown in FIG. 6, different portions of the filtered sensor signal 1404 may indicate different respiratory phases. Filtered sensor signal 1404 may include first portions as indicated for example at 1406 including a first average parameter (e.g., median or mean), second portions as indicated for example at 1408 including a second average parameter (e.g., median or mean), and third portions as indicated for example at 1410 including a third average parameter (e.g., median or mean). The first average parameter is less than the second average parameter, and the third average parameter is less than the second average parameter. In addition, the first portions as indicated for example at 14061618.294.1 1 133 include a first maximum value (e.g., first peak), the second portions as indicated for example at 1408 include a second maximum value (e.g., second peak), and the third portions as indicated for example at 1410 include a third maximum value (e.g., third peak). The first maximum value is less than the second maximum value, and the third maximum value is less than the second maximum value. In some examples, each first portion 1406 of the filtered sensor signal 1404 corresponds to an inspiratory phase, each second portion 1408 of the filtered sensor signal 1404 corresponds to an expiratory phase, and each third portion 1410 of the filtered sensor signal 1404 corresponds to an expiratory pause phase. Accordingly, by analyzing the filtered sensor signal 1404 as described below in association with at least FIGS. 8A-1 1 , each respiratory phase of a patient may be determined.

[0134] In some examples, each respiratory phase of a patient may be determined by taking the absolute value or square of the filtered sensor signal 1404 such that all data is positive. Properties of the positive data may then be captured to determine the respiratory phase information. In some examples, a sliding window may be used to track respiratory phase changes as the average amplitude increases or decreases within the positive data. In some examples, the integral of the positive data may be used to determine amplitude (and thus respiratory phase transitions) by calculating the area under the curve. In some examples, a history buffer of phase transition timings may be saved and assigned to respiratory phases based on similar logic as described below with reference to FIG. 7 regarding threshold crossings. Accordingly, in some examples, each inspiratory phase 1406, expiratory phase 1408, and expiratory pause phase 1410 of filtered sensor signal 1404 may be determined without generating an intensity signal, which is described below with reference to FIG. 7.

[0135] As further described below, in some examples the respiratory phase determination may be used to time electrical stimulation applied to tissue within the patient in a closed loop mode of operation. In some such examples, this timing may comprise timing the stimulation to coincide solely with just one of the respiratory phases (e.g., inspiration only, expiration only, expiratory pause only) or to coincide with a combination of at least a portion of multiple respiratory1618.294.1 1 134 phases. In some examples, the respiratory phase determination may also be used to detect sleep disordered breathing (SDB) events, which in some examples may be used to implement closed loop therapy in which the detected SDB events are used as feedback to trigger and / or otherwise modulate stimulation of target tissues.

[0136] In some examples, the respiratory phase determination may also be used to detect snoring, which in some examples may be used to implement closed loop therapy in which the snoring is used as feedback to trigger and / or otherwise modulate stimulation of target tissues. During snoring, the amplitude of the sensor signal may increase for both inhalation and exhalation. However, snoring may be more noticeable during inhalation. For example, due to snoring, the amplitude of the sensor signal may be increased by up to about 2 times during exhalation but up to about 10 times during inhalation. Accordingly, by comparing the amplitude of the sensor signal during inhalation to the amplitude of the sensor signal during exhalation, snoring may be detected when the amplitude of the sensor signal during inhalation is substantially greater (e.g., 2x, 3x, 4x, etc.) than the amplitude of the sensor signal during exhalation.

[0137] FIG. 7 is a chart 1450 illustrating an example intensity signal 1452 based on the filtered sensor signal 1404 of FIG. 6. Chart 1450 includes time in minutes on the x-axis and intensity on the y-axis. The intensity signal 1452 may be generated by extracting the envelope of the filtered sensor signal 1404. In some examples, the intensity signal 1452 may be generated by taking an absolute value of the filtered sensor signal 1404 and applying an about 0.5 to 5 hertz (e.g., 5 hertz) low pass filter to generate the intensity signal. Alternatively, in some examples, the intensity signal 1452 may be generated by taking an absolute value of the filtered sensor signal 1404 and applying an about 0.5 to 5 hertz (e.g., 5 hertz) low pass filter and an about 1 second median filter to generate the intensity signal. In the above examples, taking the absolute value of the filtered sensor signal 1404 may be replaced, in some examples, by taking the square of the filtered sensor signal 1404. Alternatively, in some examples, the intensity signal 1452 may be generated by taking the Hilbert transform of the sensor signal 1402, taking an absolute value or square of the transformed sensor signal,1618.294.1 1 135 implementing local peak finding (e.g., within a range between about 1 millisecond and about 1000 milliseconds), and interpolating between the local peaks to generate the intensity signal 1452.

[0138] Intensity signal 1452 may include first portions as indicated for example at 1456 corresponding to first portions 1406 of filtered sensor signal 1404 of FIG. 6, second portions as indicated for example at 1458 corresponding to second portions 1408 of filtered sensor signal 1404 of FIG. 6, and third portions as indicated for example at 1460 corresponding to third portions 1410 of filtered sensor signal 1404 of FIG. 6. Each first potion 1456 of the intensity signal includes a first average parameter (e.g., median or mean), each second portion 1458 includes a second average parameter (e.g., median or mean), and each third portion 1460 includes a third average parameter (e.g., median or mean). The first average parameter is less than the second average parameter, and the third average parameter is less than the second average parameter. In addition, the first portions 1456 each include a first maximum value (e.g., first peak pi) , the second portions 1458 each include a second maximum value (e.g., second peak P2), and the third portions 1460 each include a third maximum value (e.g., third peak). The first maximum value is less than the second maximum value, and the third maximum value is less than the second maximum value.

[0139] In some examples, each first portion 1456 of the intensity signal 1452 corresponds to an inspiratory phase, each second portion 1458 of the intensity signal 1452 corresponds to an expiratory phase, and each third portion 1460 of the intensity signal 1452 corresponds to an expiratory pause phase. Accordingly, by analyzing the intensity signal 1452 as described below in association with at least FIGS. 8A-1 1 , each respiratory phase of a patient may be determined. As further described below, in some examples the respiratory phase determination may be used to time electrical stimulation applied to tissue within the patient in a closed loop mode of operation. In some examples, the respiratory phase determination may also be used to detect sleep disordered breathing events.

[0140] As shown in FIG. 7, the intensity signal may be compared to a threshold 1462. Portions of the intensity signal 1452 below the threshold 1462 may be determined to correspond to portions 1460 (e.g., an expiratory pause phase),1618.294.1 1 136 while portions of the intensity signal 1452 above the threshold 1462 may be determined to correspond to portions 1456 (e.g., an inspiratory phase) or to portions 1458 (e.g., an expiratory phase). An intensity parameter indicative of a respiratory phase may be determined based on the intensity signal 1452 of FIG. 7 or based on the filtered sensor signal 1404 of FIG. 6. In some examples, the intensity parameter may include at least one of a mean, median, area, peak, or shape. For example, each portion 1456 of the intensity signal 1452 may have a smaller mean, smaller median, smaller area, lower peak, and / or more rounded shape than each portion 1458 of the intensity signal 1452, which may have a larger mean, larger median, larger area, higher peak, and / or a more pointed shape. Each portion 1460 of the intensity signal 1452 has a smaller mean, smaller median, smaller area, lower peak, and / or flatter shape than each portion 1456 and each portion 1458 of the intensity signal 1452. Thus, as further described below with reference to at least FIGS. 8A-11 , by using one or a combination of these intensity parameters, each portion 1456 may be determined to correspond to an inspiratory phase, each portion 1458 may be determined to correspond to an expiratory phase, and each portion 1460 may be determined to correspond to an expiratory pause phase.

[0141] In some examples, each portion 1456 and each portion 1458 may have a similar intensity parameter (e.g., mean, median, area, peak, and / or shape) such that each portion 1456 and 1458 may not be individually distinguishable as an inspiratory phase or an expiratory phase. In these examples, however, each region 1460 may still be identified as an expiratory pause phase since the intensity parameter (e.g., mean, median, area, peak, and / or shape) of each region 1460, even in these examples, should be distinguishable from each region 1456 and 1458. Accordingly, in these examples, each region 1460 may be identified as an expiratory pause phase, and with the expiratory pause phase identified, it is expected that the following respiratory phase should be an inspiratory phase followed by an expiratory phase. In this way, the inspiratory phases and the expiratory phases may be distinguished from each other based on the identified expiratory pause phases.1618.294.1 1 137

[0142] In some examples, a confidence factor for the intensity signal 1452 may be determined based on the consistency of the intensity parameter(s) (e.g., mean, median, area, peak, and / or shape) of each region 1456 and 1458 compared to the intensity parameter(s) of region 1460. For example, a relatively high confidence factor may be determined in response to the intensity parameter(s) of regions 1456 and regions 1458 being consistent (e.g., regions 1456 are smaller than regions 1458) for at least a selectable percentage of the time (e.g., within a range between about 70 percent and about 100 percent of the time) for a selectable previous period. In some examples, the selectable previous period may be within a range between about 30 seconds and about 3600 seconds. In some examples, the selectable previous period may be within a range between about 5 respiratory periods and about 50 respiratory periods. A relatively low confidence factor may be determined in response to the intensity parameter(s) of regions 1456 and regions 1458 being inconsistent (e.g., regions 1456 are not smaller than regions 1458) for at least a selectable percentage of the time (e.g., within a selectable range between about 50 percent and about 100 percent) for at least the selectable previous period.

[0143] In some examples, a baseline or nominal intensity 1464 may be determined by calculating a rolling average parameter (e.g., rolling mean or rolling median) of a selected number of prior (e.g., most recent) portions 1456 and 1458 of the intensity signal 1452 as further described below with reference to at least FIGS. 8A-1 1. The baseline 1464 may be used to determine whether a current respiratory phase corresponds to an inspiratory phase (e.g., less than baseline 1464) or an expiratory phase (e.g., greater than baseline 1464) as further described below.

[0144] In some examples, the start and end of an inspiratory phase, the start and end of an expiratory phase, and the start and end of an expiratory pause phase may be determined based on the intensity signal 1452. For example, at time ti, where the intensity signal crosses the threshold 1462 from below the threshold to above the threshold, a start of an inspiratory phase may be detected. At time ta, where the intensity signal crosses the threshold 1462 from above the threshold to below the threshold, an end of the inspiratory phase may be1618.294.1 1 138 detected. At time ts, where the intensity signal crosses the threshold 1462 from below the threshold to above the threshold, a start of an expiratory phase may be detected. At time t4, where the intensity signal crosses the threshold 1462 from above the threshold to below the threshold, an end of the expiratory phase and the beginning of an expiratory pause phase may be detected. At time ts, where the intensity signal crosses the threshold 1462 from below the threshold to above the threshold, an end of the expiratory pause phase and a start of the next inspiratory phase may be detected.

[0145] In some examples, each inspiratory phase, expiratory phase, and expiratory pause phase may be determined based on the shortest length of time between threshold 1462 crossings of the intensity signal 1452. The time between threshold crossings of the intensity signal 1452 during the transition from an inspiratory phase to an expiratory phase is typically substantially shorter than: (1 ) the time between threshold crossings of the intensity signal 1452 during an inspiratory phase; (2) the time between threshold crossings of the intensity signal 1452 during an expiratory phase; and / or (3) the time between threshold crossings of the intensity signal 1452 during the transition from an expiratory phase to an inspiratory phase (which typically includes an expiratory pause phase). For example, as shown in FIG. 7, the time between the threshold crossings at t2 and t3 indicates a transition from an inspiratory phase (indicated by portion 1456) to an expiratory phase (indicated by portion 1458), while the time between the threshold crossings at ti and t2 indicates an inspiratory phase, the time between the threshold crossings at t3 and t4 indicates an expiratory phase, and the time between threshold crossings at t4 and ts indicates a transition from an expiratory phase to an inspiratory phase including an expiratory pause phase. Since fe minus t2 is substantially less than (e.g., less than about 50 percent of) each of t2 minus ti , t4 minus ts, and / or fe minus t4, the time between t2 and ts can be identified as the transition between an inspiratory phase and an expiratory phase. With the transition between the inspiratory phase and the expiratory phase identified, the inspiratory phase, the expiratory phase, and the expiratory pause phase may each be identified, since each transition between an inspiratory phase and an expiratory phase is followed by an expiratory phase, an expiratory pause phase,1618.294.1 1 139 and a subsequent inspiratory phase. After the subsequent inspiratory phase, the next transition between the inspiratory phase and an expiratory phase having the shortest length of time between threshold crossings may again be identified.

[0146] FIG. 8A is a flow diagram illustrating an example method 1600 for acoustically sensing respiration. In some examples, method 1600 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a- 1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 1600 may be implemented by a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0147] As illustrated in FIG. 8A at 1602, method 1600 includes receiving a sensor signal (e.g., 1402 of FIG. 6) based on acoustically sensing (e.g., via sensor 24 of FIGS. 1A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-5G) an upper airway airflow parameter (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ). At 1604, method 1600 includes determining an intensity parameter (e.g., area, peak, and / or shape) indicative of a respiratory phase based on the sensor signal (e.g., determining a portion 1406, 1408, or 1410 of a filtered sensor signal 1404 of FIG. 6 and / or determining a portion 1456, 1458, or 1460 of an intensity signal 1452 of FIG. 7). At 1606, method 1600 includes determining a baseline (e.g., 1464, mean or median of portions 1456 and 1458 of intensity signal 1452 of FIG. 7) of the intensity parameters from a selected number (e.g., within a range between about 4 and about 20) of most recent intensity parameters. The baseline provides a moving threshold that may be used to determine whether a current intensity parameter corresponds to an inspiratory phase (e.g., below the baseline) or to an expiratory phase (e.g., above the baseline).

[0148] At 1608, method 1600 includes determining whether the most recent intensity parameter (e.g., current intensity parameter) is greater than the baseline.1618.294.1 1 140At 1610, method 1600 includes in response to the most recent intensity parameter being greater than the baseline, identifying the most recent intensity parameter as indicative of an expiratory phase. At 1612, method 1600 includes in response to the most recent intensity parameter being less than the baseline, identifying the most recent intensity parameter as indicative of an inspiratory phase. At 1614, method 1600 may include predicting a future respiratory phase based on the most recent intensity parameter (e.g., identified as indicative of an expiratory phase or an inspiratory phase). For example, if the most recent intensity parameter is indicative of an inspiratory phase, the next intensity parameter may be predicted to be an expiratory phase, and if the most recent intensity parameter is indicative of an expiratory phase, the next intensity parameter may be predicted to be an inspiratory phase.

[0149] Further, the start and / or end of the next inspiratory and / or expiratory phase may be predicted based on the start and / or end of the most recent respiratory phase and / or a selected number of previous respiratory phases. A respiratory rate may also be determined from a selected number of previous respiratory phases. The respiratory rate may be used to predict a future respiratory phase (e.g., start and / or end) based on the most recent respiratory phase. At 1616, method 1600 may include stimulating (e.g., via a stimulation element 117 of FIG. 2B; at least one electrode 3030 of FIG. 18A; at least one electrode 3230A, 3230B, 3295 of FIGS. 19A-19D; at least one electrode 3532 of FIGS. 20A-20B; or at least one electrode 3644 of FIGS. 21 A-21 B) a target tissue (e.g., upper airway patency-related tissue) timed relative to the predicted future respiratory phase. For example, stimulation may be timed to be applied during each inspiratory phase and not applied during each expiratory phase (and / or expiratory pause phase) in a closed loop mode of operation where stimulation is timed relative to sensed respiration. However, in some examples, stimulation may be timed to be applied during at least a portion of an expiratory pause phase, such as a small period of time (e.g., 200 to 300 milliseconds) starting prior to onset of the inspiratory phase to ensure that the upper airway is open (e.g., patent) prior to the onset of the inspiratory phase. Moreover, in some examples, for at least some target tissues (e.g., nerves and / or muscles) which may1618.294.1 1 141 contribute to upper airway patency indirectly (e.g., glossopharyngeal nerve, internal superior laryngeal nerve (iSLN), phrenic nerve, diaphragm muscle, etc.), the stimulation may be timed to be applied during at least a portion of the expiratory phase and / or expiratory pause phase, whether or not stimulation is applied during at least a portion of the inspiratory phase.

[0150] FIG. 8B is a flow diagram illustrating an example method 1700 for acoustically sensing sleep disordered breathing (SDB) events. In some examples, method 1700 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A- 3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 1700 may be implemented by a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0151] As illustrated in FIG. 8B at 1702, method 1700 includes receiving a sensor signal (e.g., 1402 of FIG. 6) based on acoustically sensing (e.g., via sensor 24 of FIGS. 1A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-5G) an upper airway airflow parameter (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ). At 1704, method 1700 includes determining an intensity parameter (e.g., area, peak, and / or shape) indicative of a respiratory phase based on the sensor signal (e.g., determining a portion 1406, 1408, or 1410 of a filtered sensor signal 1404 of FIG. 6 and / or determining a portion 1456, 1458, or 1460 of an intensity signal 1452 of FIG. 7). At 1706, method 1700 includes determining a first average parameter (e.g., median or mean) of the intensity parameters from a first selected number (e.g., within a range between about 1 and about 5) of most recent intensity parameters. At 1708, method 1700 includes determining a second average parameter (e.g., median or mean) of the intensity parameters from a second selected number (e.g., within a range between about 6 and about 20) of most recent intensity1618.294.1 1 142 parameters where the second selected number is greater than the first selected number.

[0152] At 1710, method 1700 includes determining whether the first average parameter is less than the second average parameter minus a selectable value. In response to the first average parameter not being less than (e.g., being greater than) the second average parameter minus the selectable value, method 1700 includes determining a sleep disordered breathing (SDB) event has not occurred. In this case, at 1712, method 1700 may include maintaining parameters of electrical stimulation to target tissue in response to determining an SDB event has not occurred. In response to the first average parameter being less than the second average parameter minus the selectable value, at 1714, method 1700 includes determining a SDB event has occurred. The selectable value may be selected to prevent incorrect determinations of SDB events. At 1716, method 1700 may include reporting the SDB event to an external device in response to determining an SDB event has occurred. At 1718, method 1700 may include adjusting parameters (e.g., pulse amplitude, pulse width, pulse rate, etc.) of electrical stimulation to target tissue in response to determining an SDB event has occurred (e.g., to prevent further SDB events).

[0153] An apnea hypopnea index (AHI) may be determined based on a number of sleep disordered breathing events per hour. In some examples, the AHI may be transmitted to an external device (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). In some examples, electrical stimulation (e.g., applied to stimulation element 56 of FIG. 2A or 1 17 of FIG. 2B) may be adjusted based on the AHI. In some example methods of the present disclosure, other disease burden indicator(s) may be used to indicate the impact of obstructed airflows in the upper airway, which may be used in addition to, or instead of, the apnea-hypopnea index. In some such examples, the disease burden indicator(s) may comprise a respiratory disturbance index, blood oxygen desaturation (e.g., oxygen desaturation index), etc. In some such examples, indicating a disease burden via blood oxygen desaturation may be implemented per a hypoxic burden parameter, which indicates a depth (e.g., severity) and / or1618.294.1 1 143 duration of oxygen desaturation associated with specific obstructive sleep apnea events. In at least some these examples implementing the hypoxic burden parameter, the depth (e.g., severity) and / or duration of oxygen desaturation (associated with specific obstructive sleep apnea events) is measured according an area under a curve (representing an oxygen desaturation signal over time) relative to a baseline (of oxygen desaturation).

[0154] FIG. 8C is a flow diagram 1720 illustrating an example method for acoustically sensing snoring and SDB events, including differentiating between snoring, SDB events, and normal breathing. In some examples, method 1720 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 1720 may be implemented by a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0155] As illustrated in FIG. 80 at 1722, method 1720 includes receiving a sensor signal (e.g., 1402 of FIG. 6) based on acoustically sensing (e.g., via sensor 24 of FIGS. 1 A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-5G) an upper airway airflow parameter (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ). At 1724, method 1720 includes detecting an expiratory phase (e.g., 1408 of FIG. 6 and / or 1458 of FIG. 7) and an inspiratory phase (e.g., 1406 of FIG. 6 and / or 1456 of FIG. 7) based on the sensor signal. In some examples, the expiratory phase and the inspiratory phase may be detected according to method 1600 as described with reference to FIG. 8A or as further described below with reference to at least FIGS. 9-1 1 . At 1726, method 1720 includes determining an average amplitude of the expiratory phase and an average amplitude of the inspiratory phase. The average amplitude for the expiratory phase and the average amplitude for the inspiratory phase may be determined based on absolute value measurements, square of1618.294.1 1 144 measurements, or peak-to-peak amplitude measurements of the filtered sensor signal. In some examples, the average amplitude for the expiratory phase and the average amplitude for the inspiratory phase may each be a running average over a selected number (e.g., within a range between about 1 and about 5) of most recent expiratory and inspiratory phases, respectively.

[0156] At 1728, method 1720 determines whether the inspiratory phase amplitude is greater than three times the expiratory phase amplitude. In response to the inspiratory phase amplitude being greater than three times the expiratory phase amplitude, then at 1730 method 1720 determines that a snoring event is occurring. In response to the inspiratory phase amplitude not being greater than three times the expiratory phase amplitude, then at 1732 method 1720 determines whether the inspiratory phase amplitude is less than one-third times the expiratory phase amplitude. In response to the inspiratory phase amplitude being less than one-third times the expiratory phase amplitude, at 1734 method 1720 determines a SDB (e.g., apnea) event is occurring. In response to the inspiratory phase amplitude not being less than one-third times the expiratory phase amplitude, at 1736 method 1720 determines that normal breathing is occurring.

[0157] FIG. 9 is a flow diagram illustrating another example method 1800 for acoustically sensing respiration. In some examples, method 1800 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a- 1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 1800 may be implemented by a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0158] As illustrated in FIG. 9 at 1802, method 1800 includes receiving a sensor signal (e.g., 1402 of FIG. 6) based on sensing (e.g., via sensor 24 of FIGS. 1 A- 1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-1618.294.1 1 1455G) an upper airway airflow parameter (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ). At 1804, method 1800 includes generating an intensity signal (e.g., 1452 of FIG. 7) based on the sensor signal. At 1806, method 1800 includes determining an area of the intensity signal corresponding to a respiratory phase for each portion of the intensity signal (e.g., 1456 and 1458 of FIG. 7) that exceeds a selectable threshold (e.g., 1462 of FIG. 7). At 1808, method 1800 includes determining a baseline (e.g., mean or median) of the areas from a selected number (e.g., within a range between about 4 and about 20) of most recent determined areas.

[0159] At 1810, method 1800 includes determining whether the most recent determined area is greater than the baseline. In response to the most recent determined area being greater than the baseline (e.g., YES in FIG. 9), at 1812 method 1800 includes identifying the most recent determined area as corresponding to expiration (e.g., an expiratory phase). In response to the most recent determined area being less than the baseline (e.g., NO in FIG. 9), at 1814 method 1800 includes identifying the most recent determined area as corresponding to inspiration (e.g., an inspiratory phase). At 1816, method 1800 includes applying electrical stimulation to target tissue timed relative to a predicted next respiratory phase. For example, stimulation may be timed to be applied during each inspiratory phase and not applied during each expiratory phase (and / or expiratory pause phase) in a closed loop mode of operation where stimulation is timed relative to sensed respiration. However, in some examples in which stimulation is timed to coincide solely with the inspiratory phase, the stimulation also may include a short pre-inspiratory stimulation portion in which stimulation is applied for a few hundred (e.g., 200, 300) milliseconds during the expiratory pause phase immediately preceding the inspiratory phase (and continued into and through the inspiratory phase) to help ensure the upper airway is open prior to negative pressure being applied during the inspiratory phase.

[0160] The following FIGS. 10-1 1 address various example methods, devices, etc., which in some examples may comprise at least some of substantially the same features as, or may comprise an example implementation of, the various examples described in association with at least FIGS. 6-9.1618.294.1 1 146

[0161] FIG. 10 is a diagram schematically representing an example method and / or example device 2000 for acoustic sensing of respiration. Example method and / or example device 2000 may include an acoustic sensor 2002 and an acoustic engine 2003. In some examples, method and / or device 2000 including acoustic sensor 2002 and acoustic engine 2003 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). In some examples, acoustic engine 2003 may be implemented by a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0162] Acoustic engine 2003 may include a filter 2006, an intensity signal generator 2020, a threshold generator 2024, a comparator 2028, an integral calculator 2038, a rolling average parameter calculator 2042, and a respiratory phase identifier 2046. Acoustic sensor 2002 may include sensor 24 of FIGS. 1 A- 1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, or 1024b of FIGS. 5A-5G. Acoustic sensor 2002 may sense an upper airway airflow parameter (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ) to generate a sensor signal as indicated at 2004. In some examples, sensor signal 2004 may correspond to sensor signal 1402 of FIG. 6.

[0163] In some examples, the acoustic sensor 2002 may be switched on and off (e.g., via a control portion) based on a duty cycle. In some examples, a sampling rate of the acoustic sensor may be set (e.g., via a control portion) at less than two times a bandwidth of the sensor signal. In some examples, in an open loop mode of operation where stimulation is not timed relative to sensed respiration, the acoustic sensor 2002 may be set to operate at a first sampling rate between predicted crossings of an intensity signal from below to above a threshold and from above to below the threshold (e.g., as described below with reference to comparator 2028), and set to operate at a second sampling rate greater than the1618.294.1 1 147 first sampling rate during the predicated crossing of the intensity signal from below to above the threshold and from above to below the threshold. In some examples, the first sampling rate equals 0, such that samples are obtained only during predicted crossings of an intensity signal from below to above a threshold and from above to below the threshold. In this way, by not continuously sampling acoustic sensor 2002, the power consumption of the acoustic sensor 2002 may be reduced.

[0164] The sensor signal at 2004 is input to filter 2006. Filter 2006 may include a band pass filter 2008, which may include a high pass filter 2010 and a low pass filter 2012. The filter 2006 may include an analog filter 2014 (e.g., hardware, application specific integrated circuit (ASIC), etc.), a digital filter 2016 (e.g., digital signal processer), and / or a combination thereof. Digital filter 2016 may include a linear filter (e.g., infinite impulse response (HR) filter or a finite impulse response (FIR) filter) or a nonlinear filter (e.g., running median filter or Kalman filter). Filter 2006 may filter the sensor signal to reject at least one of ambient sound in the patient’s environment, snoring, speech, muscle motion, and / or cardiac-based motion. In some examples, the band pass filter 2008 may include an about 200 hertz high pass filter 2010 and an about 2000 hertz low pass filter 2012. Filter 2006 outputs the filtered sensor signal as indicated at 2018. In some examples, filtered sensor signal 2018 may correspond to filtered sensor signal 1404 of FIG. 6.

[0165] Intensity signal generator 2020 receives the filtered sensor signal 2018 and generates an intensity signal as indicated at 2022 from the filtered sensor signal 2018. Intensity signal generator 2020 may extract an envelope of the filtered sensor signal 2018 to generate the intensity signal 2022. In some examples, intensity signal generator 2020 takes an absolute value of the filtered sensor signal 2018 and applies an about 0.5 to 5 hertz (e.g., 5 hertz) low pass filter to generate the intensity signal 2022. Alternatively, in some examples, intensity signal generator 2020 takes an absolute value of the filtered sensor signal 2018 and applies an about 0.5 to 5 hertz (e.g., 5 hertz) low pass filter and an about 1 second median filter to generate the intensity signal 2022. In the above examples, intensity signal generator 2020 may take the square of the1618.294.1 1 148 filtered sensor signal 2018 in place of taking the absolute value of the filtered sensor signal 2018, in some examples. Alternatively, in some examples, the intensity signal 2022 may be generated by taking the Hilbert transform of the sensor signal 2004 (bypassing filter 2006), taking an absolute value or square of the transformed sensor signal, implementing local peak finding (e.g., within a range between about 1 millisecond and about 1000 milliseconds), and interpolating between the local peaks to generate the intensity signal 2022. In some examples, intensity signal 2022 may correspond to intensity signal 1452 of FIG. 7.

[0166] Threshold generator 2024 sets a threshold as indicated at 2026 based on the intensity signal 2022. In some examples, threshold generator 2024 sets the threshold 2026 as a minimum of the intensity signal over a selectable previous period plus a selectable positive value (e.g., a selectable positive constant or a selectable percentage value). In some examples, the selectable previous period may be within a range between about 10 seconds and about 50 seconds. In some examples, the selectable previous period may be within a range between about 2 respiratory periods and about 10 respiratory periods. In some examples, the threshold generator 2024 sets the threshold 2026 as a minimum of the intensity signal over a previous period within a range between about 4 integral calculations and about 20 integral calculations (e.g., as described below with reference to integral calculator 2038) plus the selectable positive value. The selectable positive value may be selected such that the threshold 2026 is above portions of the intensity signal indicative of expiratory pause phases, but below portions of the intensity signal indicative of inspiratory and expiratory phases. In some examples, the threshold generator 2024 sets the threshold 2026 as the rolling baseline or nominal intensity 1464 (FIG. 7) of the intensity signal minus a multiple (e.g., 1 , 2, 3, etc.) of the rolling standard deviation of the intensity signal. In some examples, the threshold generator 2024 sets the threshold 2026 by detecting troughs of the intensity signal over a selectable previous period and setting the threshold as the lowest value of the troughs over the selectable previous period plus a selectable positive value. In some examples, the selectable previous period is within a range between about 10 seconds and about1618.294.1 1 14960 seconds, within a range between about 2 respiratory periods and about 10 respiratory periods, or within a range between about 10 troughs and about 100 troughs. In some examples, the threshold 2026 may correspond to threshold 1462 of FIG. 7.

[0167] Comparator 2028 compares the intensity signal 2022 to the threshold 2026 to determine when the intensity signal 2022 crosses the threshold 2026. In response to each crossing of the intensity signal 2022 from below to above the threshold as indicated at 2030, comparator 2028 outputs an integral calculation start signal as indicated at 2034. In response to each crossing of the intensity signal 2022 from above to below the threshold as indicated at 2032, comparator 2028 outputs an integral calculation stop signal as indicated at 2036.

[0168] Integral calculator 2038 receives the intensity signal 2022 and calculates a current integral value 2040 of the intensity signal between each start signal 2034 and respective stop signal 2036. Thus, integral calculator 2038 calculates the area under of curve of the intensity signal for each portion (e.g., corresponding to a respiratory phase) of the intensity signal above the threshold 2026. Accordingly, the current integral value 2040 corresponds to a most recent respiratory phase.

[0169] Rolling average parameter calculator 2042 receives the current integral value 2040 and generates a rolling average parameter 2044 (e.g., rolling mean or rolling median) based on a selected number of previous integral values 2040. In some examples, for detecting sleep disordered breathing (SDB) events, rolling average parameter calculator 2042 may determine a rolling average parameter of the integral values from a most recent first selected number of integral calculations within a range between about 1 integral calculation and about 5 integral calculations. In some examples, rolling average parameter calculator 2042 determines a baseline or nominal intensity value as a rolling average parameter of the integral values from a most recent second selected number of integral calculations within a range between about 4 integral calculations and about 20 integral calculations. Alternatively or in addition, in some examples, rolling average parameter calculator 2042 determines a baseline or nominal intensity value as a rolling average parameter of the integral values from a prior1618.294.1 1 150 first selected number of integral calculations within a range between about 4 integral calculations and about 20 integral calculations while excluding a most recent third selected number of integral calculations within a range between about 1 integral calculation and about 5 integral calculations. In some examples, the rolling average parameter 2044 may correspond to baseline 1464 of FIG. 7.

[0170] Respiratory phase identifier 2046 determines inspiration 2050 and expiration 2060 based on the current integral value 2040 and the rolling average parameter 2044. Respiratory phase identifier 2046 may determine inspiration start 2052 and inspiration end 2054. Likewise, respiratory phase identifier 2046 may determine expiration start 2062 and expiration end 2064. The expiration end 2064 may correspond to a start of an expiratory pause phase, and the inspiration start 2052 may correspond to an end of an expiratory pause phase.

[0171] Respiratory phase identifier 2046 may compare the current integral value 2040 to the rolling average parameter 2044 (e.g., baseline or nominal intensity). In response to the current integral value being greater than the rolling average 2044, the most recent respiratory phase is determined to be an expiratory phase 2060 (e.g., 1458 of FIG. 7). In response to the current integral value being less than the rolling average 2044, the most recent respiratory phase is determined to be an inspiratory phase 2050 (e.g., 1456 of FIG. 7).

[0172] At each crossing of the intensity signal 2022 from below to above the threshold 2026 (e.g., as indicated at 2030), respiratory phase identifier 2046 may determine whether the crossing corresponds to a start 2052 of an inspiratory phase 2050 (e.g., at time ti of FIG. 7) (or an end of an expiratory pause phase) or a start 2062 of an expiratory phase 2060 (e.g., at time t3 of FIG. 7). At each crossing of the intensity signal 2022 from above to below the threshold 2026 (e.g., as indicated at 2032), respiratory phase identifier 2046 may determine whether the crossing corresponds to an end 2054 of the inspiratory phase 2050 (e.g., at time t2 of FIG. 7) or an end 2064 of the expiratory phase 2060 (e.g., at time t4 of FIG. 7) (or a start of an expiratory pause phase). In some examples, if the current integral value is determined to correspond to an inspiratory phase 2050, respiratory phase identifier 2046 can predict the next respiratory phase will be an expiratory phase 2060. Therefore, the next crossing of the intensity signal 20221618.294.1 1 151 from below to above the threshold 2026 can be determined (e.g., predicted) to be a start 2062 of the expiratory phase 2060 and the next crossing of the intensity signal 2022 from above to below the threshold 2026 can be determined (e.g., predicted) to be an end 2064 of the expiratory phase 2060 (and a start of the expiratory pause phase). Likewise, if the current integral value is determined to correspond to an expiratory phase 2060, respiratory phase identifier 2046 can predict the next respiratory phase will be an inspiratory phase 2050. Therefore, the next crossing of the intensity signal 2022 from below to above the threshold 2026 can be determined (e.g., predicted) to be a start 2052 of the inspiratory phase 2052 (and an end of the expiratory pause phase) and the next crossing of the intensity signal 2022 from above to below the threshold 2026 can be determined (e.g., predicted) to be an end 2054 of the inspiratory phase 2050.

[0173] FIG. 1 1 is a flow diagram illustrating another example method 2200 for acoustically sensing respiration. In some examples, method 2200 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a- 1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 2200 may be implemented by a control portion (e.g., 190 of FIG. 2C, 2000 of FIG. 10, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0174] As illustrated in FIG. 1 1 at 2202, method 2200 includes sensing, via an implantable acoustic sensor (e.g., sensor 24 of FIGS. 1 A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, 1024b of FIGS. 5A-5G, or 2002 of FIG. 10), airway airflow (e.g., turbulent airflow 34, transitional airflow 36, and / or laminar airflow 38 of FIG. 1 ) of a patient to generate a sensor signal (e.g., 1402 of FIG. 6, 2004 of FIG. 10). At 2204, method 2200 includes filtering (e.g., via filter 2006 of FIG. 10) the sensor signal. At 2206, method 2200 includes generating (e.g., via intensity signal generator 2020 of FIG. 10) an intensity signal (e.g., 1452 of FIG. 7, 2022 of FIG. 10) from the filtered signal (e.g., 1404 of FIG. 6, 2018 of FIG. 10).1618.294.1 1 152

[0175] At 2208, method 2200 includes comparing (e.g., via comparator 2028 of FIG. 10) the intensity signal to a threshold (e.g., 1462 of FIG. 7, 2026 of FIG. 10). At 2210, method 2200 includes at each crossing of the intensity signal from below to above the threshold (e.g., as indicated 2030 of FIG. 10), starting (e.g., via start signal 2034 of FIG. 10) an integral calculation (e.g., via integral calculator 2038 of FIG. 10) of the intensity signal. At 2212, method 2200 includes at each crossing of the intensity signal from above to below the threshold (e.g., as indicated at 2032 of FIG. 10), stopping (e.g., via stop signal 2036 of FIG. 10) the integral calculation to provide a current integral value (e.g., 2040 of FIG. 10) corresponding to a most recent respiratory phase.

[0176] At 2214, method 2200 includes calculating (e.g., via rolling average parameter calculator 2042 of FIG. 10) a rolling average parameter (e.g., 1464 of FIG. 7, 2044 of FIG. 10) of the integral values from a selected number of most recent integral calculations. At 2216, method 2200 includes determining (e.g., via respiratory phase identifier 2046 of FIG. 10) the most recent respiratory phase is an expiratory phase (e.g., 1458 of FIG. 7, 2060 of FIG. 10) in response to the current integral value being greater than the rolling average parameter. At 2218, method 2200 includes determining the most recent respiratory phase is an inspiratory phase (e.g., 1456 of FIG. 7, 2050 of FIG. 10) in response to the current integral value being less than the rolling average parameter. At 2220, method 2200 includes applying electrical stimulation, via an implantable stimulation element (e.g., 56 of FIG. 2A or 1 17 of FIG. 2B), to an upper airway patency- related tissue (e.g., nerves and / or muscles) of a patient based on the inspiratory phase or expiratory phase. In some examples, the electrical stimulation may be applied to other nerves and / or muscles (e.g., phrenic nerve, diaphragm muscle) which may generally contribute to alleviating sleep disordered breathing such as via initiating, modulating, etc. various general breathing functions, reflexes, etc., but without directly controlling upper airway patency tissues (e.g., genioglossus muscle, infrahyoid strap muscles, etc.).

[0177] FIG. 12 includes charts 2300 illustrating example filtered sensor signals. The acoustic sensor signal 1402 of FIG. 6 may be filtered to generate the filtered sensor signals of charts 2300. A low pass (e.g., 200 hertz) filtered sensor signal1618.294.1 1 153 is indicated at 2302, a band pass (e.g., 200-2000 hertz) filtered sensor signal is indicated at 2304, and a band pass (e.g., 200-2000 hertz) filtered sensor signal times 50 is indicated at 2306 (e.g., signal 2304 times 50). Signal 2304 is indicative of breathing sounds, which are about one to two orders of magnitude lower than signal 2302, which is indicative of heartbeat sounds. Signal 2306 highlights the difference between the magnitudes of signals 2302 and 2304. Accordingly, heartbeat sounds, which are indicated by each pair of peaks 2308 may be detected from the acoustic sensor signal, as further described below with reference to at least FIG. 16A.

[0178] FIG. 13A is a chart 2350 illustrating filtered sensor signals indicative of breathing sounds. The acoustic sensor signal 1402 of FIG. 6 may be filtered to generate the filtered sensor signals of chart 2350. A band pass (e.g., 200-2000 hertz) filtered sensor signal sampled at 48000 hertz is indicated at 2352 and a high pass (e.g., 200 hertz) filtered sensor signal sampled at 800 hertz is indicated 2354. Both signals 2352 and 2354 are indicative of breathing sounds. For signal 2354, however, due to the lower sampling rate (e.g., 800 hertz), which is a subNyquist sampling rate, the frequency spectrum (e.g., as illustrated by chart 2380 of FIG. 13C) is aliased such that 600-1000 hertz (e.g., 2386 of FIG. 13C) and 1400-1800 hertz (e.g., 2390 of FIG. 13C) components are filtered out by the high pass (e.g., 200 hertz) filter due to aliasing and 1000-1400 hertz (e.g., 2388 of FIG. 13C) components fold over into the 200-400 hertz frequency range. In addition, for signal 2354 sampled at the lower sampling rate, a low pass filter may be excluded since the highest frequency represented in the signal will be one half of the sampling rate (e.g., 400 hertz for a 800 hertz sampling rate). Accordingly, the lower sampling rate of the acoustic signal (e.g., 800 hertz) is sufficient for determining respiration information as long as the sampling rate is high enough to filter out less than about 200 hertz high amplitude signal components while capturing a sufficient portion of the about 200-600 hertz (e.g., 2384 of FIG. 13C) band of interest. By reducing the sampling rate, less power may be consumed by the IMD (e.g., due to less data to process in an analog to digital converter and / or in a microcontroller unit (MCU) or ASIC).1618.294.1 1 154

[0179] In some examples, the lower sampling rate of the acoustic signal may be 1000 hertz and the acoustic signal may be high pass filtered at about 200 hertz to capture respiratory sounds within a range between about 200 hertz and about 800 hertz. In this case, due to the lower sampling rate of 1000 hertz, which is a sub-Nyquist sampling rate, the frequency spectrum is aliased such that 800-1200 hertz components are filtered out by the high pass filter due to aliasing and 1200- 1800 hertz components fold over into the 200-800 hertz frequency range.

[0180] FIG. 13B is a chart 2360 illustrating an example envelope 2362 of the filtered sensor signal 2352 or 2354 of FIG. 13A. The envelope 2362 may be determined using, for example, an envelope detector, a rectifier, or a square law detector to determine the amplitude of the filtered acoustic signal. In some examples, the envelope 2362 corresponds to the intensity signal 1452 of FIG. 7. The envelope 2362 may be detected using discrete components, separate integrated circuits (ICs), and / or integrated into an application specific integrated circuit (ASIC) as described below with reference to at least FIGS. 14A-14D. By detecting the peaks and valleys of the envelope 2362, respiratory phase information (e.g., inspiration, expiration, expiration pause) can be determined as previously described above with reference to FIGS. 6-1 1 and further described below with reference to FIGS. 14A-15C.

[0181] FIG. 13C is a chart 2380 illustrating an example acoustic sensor signal spectrum. The example acoustic sensor signal spectrum is plotted as an amplitude in arbitrary units (a. u.) on a decibel scale versus frequency in hertz. Frequency components 2382 between about 20 hertz and about 200 hertz may be used to detect cardiac information (e.g., heart beat sounds). Frequency components 2384 between about 200 hertz and about 600 hertz may be used to detect respiration information (e.g., inhalation, exhalation, expiratory pause). Frequency components 2386 are between about 600 hertz and about 1000 hertz. Frequency components 2388 are between about 1000 hertz and about 1400 hertz. Frequency components 2390 are between about 1400 hertz and about 1800 hertz. As shown in chart 2380, high amplitude signal content less than about 100 hertz is about two orders of magnitude higher than the 200-600 hertz1618.294.1 1 155 components 2384. Signal content greater than 600 hertz is at least two orders of magnitude lower than the 200-400 hertz components.

[0182] In examples where the low pass filter is excluded as described above with reference to FIG. 13A, unwanted noise from other parts of the frequency spectrum may reduce the signal to noise ratio (SNR) and / or interfere with the filtered signal used to detect respiratory information. As described above, when the sampling frequency is 800 hertz and the signal is filtered by a 200 hertz high pass filter, the 1000-1400 hertz frequency range 2388 folds over into the 200-400 hertz frequency range, which is passed by the high pass filter. If there is any noise in this 1000-1400 hertz frequency range, it may corrupt the underlying respiratory signal. In a sleep environment, however, presence of high pitch noises (e.g., greater than about 1000 hertz) is unlikely. In addition, tissues and the construction of the IMD housing may filter out higher frequency acoustic content, and may dampen these noise sources. Accordingly, in some examples, the IMD housing material, wall thickness, shape, and inside construction may be selected to differentially filter high frequency acoustic content such that an antialiasing low pass filter may be excluded.

[0183] FIG. 14A is a diagram schematically representing an example device 2400 for detecting respiratory information. In some examples, device 2400 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2400 may include a digital sensor 2402 and a microcontroller unit (MCll) 2404. MCU 2404 may include a low pass filter 2412, a downsample element 2406, a high pass filter 2408, and a respiratory detection element 2410. In some examples, MCU 2404 may be implemented by a component 1024a of FIG. 5A- 5G. An output of digital sensor 2402 is communicatively coupled to an input of low pass filter 2412 of MCU 2404 through a communication path 2403. An output of low pass filter 2412 is communicatively coupled to an input of downsample element 2406 through a communication path 2413. In some examples, low pass filter 2412 may be excluded, such that an output of digital sensor 2402 is1618.294.1 1 156 communicatively coupled to an input of downsample element 2406. An output of downsample element 2406 is communicatively coupled to an input of high pass filter 2408 through a communication path 2407. An output of high pass filter 2408 is communicatively coupled to an input of respiratory detection element 2410 through a communication path 2409.

[0184] In some examples, digital sensor 2402 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2402 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2402 may include a high sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2402 outputs the digital sensor signal on communication path 2403. In some examples, digital sensor 2402 may comprise a three axis acoustic signal sensing accelerometer and may output a single digital sensor signal that is a combination (e.g., root mean squared) of the sensor signals from the three axes.

[0185] Low pass filter 2412 receives the digital sensor signal on digital signal path 2403 and low pass filters the digital sensor signal. Low pass filter 2412 may include an anti-aliasing low pass filter. Downsample element 2406 receives the low pass filtered digital sensor signal (or the digital sensor signal on communication path 2403 if low pass filter 2412 is excluded) and downsamples the digital sensor signal from the high sample rate to a low sample rate (LSR), such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The LSR is an integer divisor of the HSR. Downsample element 2406 outputs the downsampled digital sensor signal on communication path 2407.

[0186] High pass filter 2408 receives the downsampled digital sensor signal on communication path 2407 and high pass filters the downsampled digital sensor signal. In some examples, high pass filter 2408 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 2001618.294.1 1 157 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2408 outputs the high pass filtered digital sensor signal on communication path 2409. In some examples, the high pass filtered digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0187] Respiratory detection element 2410 receives the high pass filtered digital sensor signal on communication path 2409 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the high pass filtered digital sensor signal. In some examples, respiratory detection element 2410 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the high pass filtered digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0188] By implementing device 2400 using digital sensor 2402 and MCU 2404, device 2400 incorporates flexibility for adjusting the device’s functionality after the device is manufactured. For example, the sampling rate of digital sensor 2402 may be selected and / or updated, the cutoff frequency of low pass filter 2412 and / or the cutoff frequency of high pass filter 2408 may be selected and / or updated, and / or the sampling rate of downsample element 2406 may be selected and / or updated. In addition, respiratory detection element 2410 may be updated. Each of these values and / or processes might be programmable in MCU 2404 enabling the maximum design flexibility compared to the devices described below with reference to FIGS. 14B-14D.

[0189] FIG. 14B is a diagram schematically representing an example device 2420 for detecting respiratory information. In some examples, device 2420 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2420 may include an analog sensor 2422 and a MCU or application specific integrated circuit (ASIC) 2424. MCU or ASIC 2424 may include a hardware (e.g., analog) low pass filter 2426, an analog to digital conversion element 2428, a high1618.294.1 1 158 pass filter 2408, and a respiratory detection element 2410. In some examples, MCll or ASIC 2424 may be implemented by a component 1024a of FIG. 5A-5G. An output of analog sensor 2422 is communicatively coupled to an input of hardware low pass filter 2426 through an analog signal path 2423. An output of hardware low pass filter 2426 is communicatively coupled to an input of analog to digital conversion element 2428 through an analog signal path 2427. In some examples, hardware low pass filter 2426 may be excluded, such that an output of analog sensor 2422 is communicatively coupled to an input of analog to digital conversion element 2428. An output of analog to digital conversion element 2428 is communicatively coupled to an input of high pass filter 2408 through a communication path 2429. An output of high pass filter 2408 is communicatively coupled to an input of respiratory detection element 2410 through a communication path 2409.

[0190] In some examples, analog sensor 2422 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2422 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2422 outputs the analog sensor signal on analog signal path 2423. In some examples, analog sensor 2422 may comprise a three axis acoustic signal sensing accelerometer and may output a single analog sensor signal that is a combination (e.g., root mean squared) of the sensor signals from the three axes.

[0191] Hardware low pass filter 2426 receives the analog sensor signal on analog signal path 2423 and low pass filters the analog sensor signal. In some examples, the hardware (analog) low pass filter 2426 includes a cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. Hardware low pass filter 2426 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into the MCU or ASIC 2424. Hardware low pass filter 2426 outputs the low pass filtered analog sensor signal on analog signal path 2427.1618.294.1 1 159

[0192] Analog to digital conversion element 2428 receives the low pass filtered analog sensor signal (or analog sensor signal directly from analog sensor 2422 if hardware low pass filter 2426 is excluded) and converts the analog sensor signal to a digital sensor signal. In some examples, a sampling rate of the analog to digital conversion element 2428 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2428 outputs the digital sensor signal on communication path 2429.

[0193] High pass filter 2408 receives the digital sensor signal on communication path 2429 and high pass filters the digital sensor signal. In some examples, high pass filter 2408 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2408 outputs the high pass filtered digital sensor signal on communication path 2409. In some examples, the high pass filtered digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0194] Respiratory detection element 2410 receives the high pass filtered digital sensor signal on communication path 2409 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the high pass filtered digital sensor signal. In some examples, respiratory detection element 2410 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the high pass filtered digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0195] Compared to device 2400 of FIG. 14A, device 2420 of FIG. 14B provides a tradeoff between power consumption (which might be higher in device 2400) and programmability. Analog sensor 2422 may consume less power than digital sensor 2402, but the analog to digital conversion element 2428 is added in device 2420 to support the analog sensor.

[0196] FIG. 14C is a diagram schematically representing an example device 2440 for detecting respiratory information. In some examples, device 2440 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS.1618.294.1 1 1601 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2440 includes an analog sensor 2422, a hardware band pass filter 2446, an analog to digital conversion element 2448, and a respiratory detection element 2410. An output of analog sensor 2422 is communicatively coupled to an input of hardware band pass filter 2446 through an analog signal path 2423. An output of hardware band pass filter 2446 is communicatively coupled to an input of analog to digital conversion element 2448 through an analog signal path 2447. An output of analog to digital conversion element 2448 is communicatively coupled to an input of respiratory detection element 2410 through a communication path 2449.

[0197] In some examples, analog sensor 2422 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2422 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2422 outputs the analog sensor signal on analog signal path 2423. In some examples, analog sensor 2422 may comprise a three axis acoustic signal sensing accelerometer and may output a single analog sensor signal that is a combination (e.g., root mean squared) of the sensor signals from the three axes.

[0198] Hardware band pass filter 2446 receives the analog sensor signal on analog signal path 2423 and band pass filters the analog sensor signal. In some examples, the hardware (analog) band pass filter 2446 includes a lower cutoff frequency within a range between about 80 hertz and about 300 hertz, such as about 200 hertz, and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. The higher cutoff frequency may be between 2 to 10 times higher than the lower cutoff frequency. Hardware band pass filter 2446 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into a MCU or ASIC. Hardware band1618.294.1 1 161 pass filter 2446 outputs the band pass filtered analog sensor signal on analog signal path 2447.

[0199] Analog to digital conversion element 2448 receives the band pass filtered analog sensor signal on analog signal path 2447 and converts the analog sensor signal to a digital sensor signal. In some examples, a sampling rate of the analog to digital conversion element 2448 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2448 outputs the digital sensor signal on communication path 2449. In some examples, the digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0200] Respiratory detection element 2410 receives the digital sensor signal on communication path 2449 and determines respiratory information (e.g., inhalation, exhalation) of the patient based on the digital sensor signal. In some examples, respiratory detection element 2410 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0201] Device 2440 of FIG. 14C includes hardware filtering (e.g., analog filtering), which can be faster and more efficient (e.g., lower power consumption) than the digital filtering of device 2400 of FIG. 14A and device 2420 of FIG. 14B. In device 2440, however, programmability may be limited compared to the devices 2400 and 2420. If signal processing changes are desired in device 2440, hardware changes are needed, whereas in devices 2400 and 2420 signal processing changes may be implemented without impacting the hardware design.

[0202] FIG. 14D is a diagram schematically representing an example device 2460 for detecting respiratory information. In some examples, device 2460 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2460 includes an analog sensor 2422, a hardware band pass filter 2446, an envelope detector or rectifier or square law detector 2468, an analog to digital1618.294.1 1 162 conversion element 2470, and a respiratory detection element 2410. An output of analog sensor 2422 is communicatively coupled to an input of hardware band pass filter 2446 through an analog signal path 2423. An output of hardware band pass filter 2446 is communicatively coupled to an input of envelope detector or rectifier or square law detector 2468 through an analog signal path 2447. An output of envelope detector or rectifier or square law detector 2468 is communicatively coupled to an input of analog to digital conversion element 2470 through an analog signal path 2469. An output of analog to digital conversion element 2470 is communicatively coupled to an input of respiratory detection element 2410 through a communication path 2471 .

[0203] In some examples, analog sensor 2422 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2422 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2422 outputs the analog sensor signal on analog signal path 2423. In some examples, analog sensor 2422 may comprise a three axis acoustic signal sensing accelerometer and may output a single analog sensor signal that is a combination (e.g., root mean squared) of the sensor signals from the three axes.

[0204] Hardware band pass filter 2446 receives the analog sensor signal on analog signal path 2423 and band pass filters the analog sensor signal. In some examples, the hardware (analog) band pass filter 2446 includes a lower cutoff frequency within a range between about 80 hertz and about 300 hertz, such as about 200 hertz, and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. The higher cutoff frequency may be between 2 to 10 times higher than the lower cutoff frequency. Hardware band pass filter 2446 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into a MCll or ASIC. Hardware band pass filter 2446 outputs the band pass filtered analog sensor signal on analog signal path 2447.1618.294.1 1 163

[0205] Envelope detector or rectifier or square law detector 2468 receives the band pass filtered analog sensor signal on analog signal path 2447 and detects the envelope (e.g., amplitude) of the filtered analog sensor signal. Envelope detector or rectifier or square law detector 2468 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into a MCU or ASIC. Envelope detector or rectifier or square law detector 2468 outputs the analog envelope signal on analog signal path 2469.

[0206] Analog to digital conversion element 2470 receives the analog envelope signal on analog signal path 2469 and converts the analog envelope signal to a digital envelope signal. In some examples, a sampling rate of the analog to digital conversion element 2470 is within a range between about 10 hertz and about 200 hertz, such as 50 hertz. Analog to digital conversion element 2470 outputs the digital envelope signal on communication path 2471. In some examples, the digital envelope signal corresponds to envelope 2362 of FIG. 13B or intensity signal 1452 of FIG. 7.

[0207] Respiratory detection element 2410 receives the digital envelope signal on communication path 2471 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the digital envelope signal. In some examples, respiratory detection element 2410 determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-11 .

[0208] Device 2460 of FIG. 14D takes device 2440 of FIG. 14C one step further with a hardware envelope detector (e.g., analog envelope detector) and may provide additional power savings. Device 2460 may also support a lower sampling rate than devices 2400, 2420, and 2440 of FIGS. 14A-14C, such as within a range between about 25 hertz and about 200 hertz. Programmability of device 2460, however, may be more limited compared to devices 2400, 2420, and 2440.

[0209] The following FIGS. 15A-15C address various example methods, devices, etc., which in some examples may comprise at least some of substantially the same features as, or may comprise an example implementation1618.294.1 1 164 of, the various examples described in association with at least FIGS. 6-8B and 12-14D.

[0210] FIG. 15A is a flow diagram illustrating an example method 2600 for determining respiratory information. In some examples, method 2600 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a- 1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 2600 may be implemented by a control portion (e.g., 190 of FIG. 2C, 2000 of FIG. 10, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0211] At 2602, method 2600 includes detecting peaks (e.g., pi, p?) of the intensity signal (e.g., 1452 of FIG. 7 or 2362 of FIG. 13B) above a threshold (e.g., 1462 of FIG. 7 or 2026 of FIG. 10). For example, the maximum value (e.g., peak) within each region 1456 (e.g., pi) and 1458 (e.g., P2) of the intensity signal 1452 of FIG. 7 may be detected. At 2604, method 2600 includes determining respiratory information (e.g., inhalation, exhalation, expiratory pause) based on the detected peaks. In some examples, the peaks of the intensity signal may be used to identify each inspiratory phase and / or each expiratory phase since the time between the peak of an expiratory phase (e.g., regions 1458) followed by the peak of an inspiratory phase (e.g., regions 1456) is typically longer than the time between the peaks of an inspiratory phase followed by the peak of an expiratory phase (e.g., due to an expiratory pause phase).

[0212] FIG. 15B is a flow diagram illustrating an example method 2610 for determining respiratory information. In some examples, method 2610 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a- 1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control1618.294.1 1 1654640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 2610 may be implemented by a control portion (e.g., 190 of FIG. 2C, 2000 of FIG. 10, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0213] At 2612, method 2610 includes determining midpoints of the intensity signal (e.g., 1452 of FIG. 7 or 2362 of FIG. 13B) above a threshold (e.g., 1462 of FIG. 7 or 2026 of FIG. 10). For example, the midpoint (e.g., rm) of each region 1456 (e.g., t2 minus one half of t2 minus ti or ti plus one half of t2 minus ti) and the midpoint (e.g., m2) of each region 1458 (e.g., t4 minus one half of t4 minus fe or t3 plus one half of t4 minus ts) of the intensity signal 1452 of FIG. 7 may be determined. At 2614, method 2610 includes determining respiratory information (e.g., inhalation, exhalation, expiratory pause) based on the determined midpoints. In some examples, the midpoints of regions 1456 and 1458 of the intensity signal may be used to identify each inspiratory phase and / or each expiratory phase since the time between the midpoint of an expiratory phase (e.g., regions 1458) followed by the midpoint of an inspiratory phase (e.g., regions 1456) is typically longer than the time between the midpoint of an inspiratory phase followed by the midpoint of an expiratory phase (e.g., due to an expiratory pause phase).

[0214] FIG. 15C is a flow diagram illustrating an example method 2620 for applying stimulation. In some examples, method 2620 may be implemented by an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B) and / or by an external element (e.g., 150 of FIG. 2B or mobile device 4620, remote control 4640, clinician programmer 4650, patient management tool 4660, or external charger 4670 of FIG. 29). Method 2620 may be implemented by a control portion (e.g., 190 of FIG. 2C, 2000 of FIG. 10, 4500 of FIG. 28A, or 4520 of FIG. 28B) of an implantable and / or external element.

[0215] At 2622, method 2620 includes predicting a future inspiratory phase. In some examples, predicting a future inspiratory phase includes predicting a peak1618.294.1 1 166 or midpoint of a future inspiratory phase based on previous peaks (from method 2600 of FIG. 15A) or midpoints (from method 2610 of FIG. 15B) of each respiratory phase. In some examples, the peak or midpoint of a future inspiratory phase may be predicted based on one or more previous respiratory cycles and the respiratory rate of one or more previous respiratory cycles. For example, the peak or midpoint of a future inspiratory phase may be predicted based on the mean of previous peaks or midpoints, the median of previous peaks or midpoints, a linear extrapolation of previous peaks or midpoints, or a non-linear extrapolation of previous peaks or midpoints. In some examples, the peak or midpoint of a future inspiratory phase may be predicted based on similarity (e.g., crosscorrelation) with a reference respiratory morphology.

[0216] With the peak or midpoint of the future inspiratory phase predicted, the beginning of the future inspiratory phase may be predicted by subtracting a first predetermined interval (e.g., a first time period measured in seconds and / or milliseconds) from the predicted peak or midpoint and the end of the future inspiratory phase may be predicted by adding a second predetermined interval (e.g., a second time period measured in seconds and / or milliseconds) to the predicted peak or midpoint.

[0217] At 2624, method 2620 includes applying stimulation. In some examples, an electrode (e.g., stimulation element 117 of FIG. 2 B ; at least one electrode 3030 of FIG. 18A; at least one electrode 3230A, 3230B, 3295 of FIGS. 19A-19D; at least one electrode 3532 of FIGS. 20A-20B; or at least one electrode 3644 of FIGS. 21A-21 B) may be used to deliver electrical stimulation to target tissue (e.g., upper airway patency-related tissue) of the patient. In some examples, the electrical stimulation may be applied based on the predicted peak or midpoint of the future inspiratory phase. The electrical stimulation may be applied with a time or ratio-based offset from the predicted peak or midpoint of the future inspiratory phase. For example, the electrical stimulation may be applied starting relative to the predicted peak or midpoint by a first predetermined interval (e.g., first time period) and ending after the predicted midpoint by a second predetermined interval (e.g., second time period). The first predetermined interval may be selected based on a preselected, or computed, relative position (e.g., 20 percent1618.294.1 1 167 or 30 percent) within the mean or median of previous inspiratory phase durations plus optionally an absolute additional amount (e.g., 200 milliseconds or 300 milliseconds) to ensure the stimulation is started just prior to the inspiratory phase. The second predetermined interval may be selected based on a preselected, or computed, relative position within the mean or median of previous inspiratory phase durations plus optionally an absolute additional amount to ensure the stimulation is ended at or just after the inspiratory phase. The relative position may be responsive to optimize therapy based on detected sleep disordered breathing like obstructive sleep apnea, central sleep apnea, multipletype sleep apneas, etc., and may be moved forwards or backwards from the relative position to optimize the efficacy of the stimulation.

[0218] In some examples, the stimulation may be applied to target tissues, such as but not limited to an upper airway patency-related tissue and / or other tissues to treat sleep disordered breathing (SDB) behavior. At least some such upper airway patency-related tissues may comprise a hypoglossal nerve, genioglossus muscle, infrahyoid muscles (e.g., infrahyoid strap), and / or infrahyoid muscle (IHM)-innervating nerves (e.g., which may include the ansa cervicalis nerve loop and / or its branches), among other nerves and / or muscles. In some examples, the electrical stimulation may be applied to other nerves and / or muscles (e.g., phrenic nerve, diaphragm muscle) to alleviate sleep disordered breathing such as via initiating, modulating, etc. various general breathing functions, reflexes, etc., but without directly controlling upper airway patency tissues (e.g., genioglossus muscle, infrahyoid strap muscles, etc.).

[0219] In some examples the stimulation may be applied based on an autotitration parameter by which an intensity of stimulation therapy may be automatically titrated (i.e., adjusted) to be more intense (e.g., higher amplitude, greater frequency, and / or greater pulse width) or to be less intense within a treatment period. In some such examples, such auto-titration may be implemented based on sleep quality, which may be obtained via sensed physiologic information, in some examples. It will be understood that such examples may be employed with synchronizing stimulation to sensed respiratory information (i.e., closed loop stimulation) or may be employed without1618.294.1 1 168 synchronizing stimulation to sensed respiratory information (i.e., open loop stimulation). In some examples, at least some aspects of the auto-titration parameter may comprise, and / or may be implemented, via at least some of substantially the same features and attributes as described in Christopherson et al., US 8,938,299, SYSTEM FOR TREATING SLEEP DISORDERED BREATHING, issued January 20, 2015, and which is hereby incorporated by reference in its entirety.

[0220] In some examples, the stimulation may be applied based on an “off period” function by which a user or clinician may adjust the time that stimulation will remain off and which may be expressed as a percentage of the previous “on period.” In some examples, the “off period” for stimulation coincides with the expiratory phase. In some examples, the “on period” may sometimes be referred to as a stimulation period and the “off period” may sometimes be referred to a non-stimulation period. In some examples, a stimulation cycle may comprise one stimulation period followed by one non-stimulation period. In some examples, the stimulation cycle may have a duration corresponding to a duration of a respiratory cycle.

[0221] In some examples, the stimulation may be applied based on a “maximum stimulation” function which may be used by a patient or clinician to adjust a maximum time for an “on period” of stimulation for a given stimulation cycle, after which an “off period” takes place. The “on period” may extend for a selectable, predetermined period of time. In some examples, the “on period” for stimulation coincides with the inspiratory phase.

[0222] A respiratory period may be calculated based on the peaks or midpoints of expiratory phases and / or inspiratory phases by calculating the difference between each pair of subsequent expiratory phase peaks or midpoints and / or each pair of subsequent inspiratory phase peaks or midpoints. In other words, in one example, the respiratory period equals the peak or midpoint of an inspiratory phase minus the peak or midpoint of an immediately previous inspiratory phase or equals a running average of the peak or midpoint of an inspiratory phase minus the peak or midpoint of an immediately previous inspiratory phase for N previous respiratory periods, where “N” is an integer number of respiratory periods. In1618.294.1 1 169 another example, the respiratory period equals the peak or midpoint of an expiratory phase minus the peak or midpoint of an immediately previous expiratory phase or equals a running average of the peak or midpoint of an expiratory phase minus the peak or midpoint of an immediately previous expiratory phase for N previous respiratory periods. A respiratory rate may be calculated as 1 divided by the calculated respiratory period or 1 divided by a running average of N previous respiratory periods. A duration of an expiratory to inspiratory half cycle may be calculated as the peak or midpoint of an inspiratory phase minus the peak or midpoint of the immediately previous expiratory phase or calculated as a running average of the peak or midpoint of an inspiratory phase minus the peak or midpoint of the immediately previous expiratory phase over N previous respiratory periods. A duration of an inspiratory to expiratory half cycle may be calculated as the peak or midpoint of an expiratory phase minus the peak or midpoint of the immediately previous inspiratory phase or calculated as a running average of the peak or midpoint of an expiratory phase minus the peak or midpoint of the immediately previous inspiratory phase over N previous respiratory periods. An inspiration to expiration (l / E) ratio may be calculated by dividing the duration of the inspiratory to expiratory half cycle by the duration of the expiratory to inspiratory half cycle.

[0223] Based on the peak or midpoint of one or more previous expiratory phases and / or one or more previous inspiratory phases, the peak or midpoint of a future inspiratory phase may be predicted. Based on the predicted midpoint of the future inspiratory phase, a duration of a stimulation period (e.g., continuous pulsed stimulation) may be determined. The stimulation period may begin at the beginning of the inspiratory phase or just prior to the inspiratory phase. The stimulation period may end at the end of the inspiratory phase or just after the inspiratory phase. It will be understood that the stimulation period may be followed by a non-stimulation period, as part of a stimulation protocol of a series of consecutive stimulation cycles, with each stimulation cycle including a stimulation period and a non-stimulation period. As apparent from the present context, each stimulation period (of a stimulation cycle) is synchronized relative to a particular portion of the respiratory waveform, e.g., inspiratory phase,1618.294.1 1 170 according to the various examples described below and throughout examples of the present disclosure.

[0224] In one example, the stimulation period is started relative to the predicted peak or midpoint of the inspiratory phase by a first predetermined (e.g., fixed) interval, such as a fixed time period measured in seconds and / or milliseconds. In other examples, the stimulation period is started an amount of time prior to the predicted peak or midpoint of the inspiratory phase computed as a percentage of the respiratory period. For example, the stimulation period may start about 2 seconds prior to the predicted peak or midpoint, where 2 seconds comprises 50 percent of a respiratory period of 4 seconds. In other examples, the stimulation period is started an amount of time prior to the predicted peak or midpoint of the inspiratory phase computed as a percentage of the respiratory rate plus a predetermined (e.g., fixed) amount of time (e.g., 100 milliseconds, 200 milliseconds, etc.). In other examples, the stimulation period is started an amount of time prior to the predicted peak or midpoint of the inspiratory phase computed as a percentage of a duration of the expiratory to inspiratory half cycle (e.g., as a percentage of peak or midpoint of an inspiratory phase minus the peak or midpoint of the immediately previous expiratory phase). For example, the stimulation period may start about 1 second prior to the predicted peak or midpoint, where 1 second comprises 33 percent of a duration of the expiratory to inspiratory half cycle of 3 seconds. In other examples, the stimulation period is started an amount of time prior to the predicted peak or midpoint of the inspiratory phase computed as a percentage of a duration of the inspiratory to expiratory half cycle (e.g., as a percentage of the peak or midpoint of an expiratory phase minus the peak or midpoint of the immediately previous inspiratory phase). For example, the stimulation period may start about 1 second prior to the predicted peak or midpoint, where 1 second comprises 66 percent of a duration of the expiratory to inspiratory half cycle of 1.5 second. In other examples, the stimulation period is started an amount of time prior to the predicted peak or midpoint of the inspiratory phase computed as a percentage of the respiration period (R). For example, the amount of time may comprise about 2 seconds, which is about 50 percent of a respiratory period (R) of about 4 seconds.1618.294.1 1 171

[0225] In some examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by a second predetermined (e.g., fixed) interval as previously described, such as a fixed time period measured in seconds and / or milliseconds. In other examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by an amount of time computed as a percentage of the respiratory rate. In other examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by an amount of time computed as a percentage of the respiratory rate plus a predetermined amount of time. In other examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by an amount of time computed as a percentage of a duration of the expiratory to inspiratory half cycle. In other examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by an amount of time computed as a percentage of a duration of the inspiratory to expiratory half cycle. In other examples, the stimulation period ends after the predicted peak or midpoint of the inspiratory phase by an amount of time computed as a percentage of the respiration period. In yet other examples, the stimulation period may start and / or end based on a combination of any of the above examples. In some of these examples, calculating a stimulation period (e.g., duration, start time, stop time) also may be performed without using a respiratory period in some examples or without using a respiratory rate in some examples.

[0226] In some examples, via the above-described arrangement in association with at least FIG. 15C in which peaks or midpoints of respiratory phases (e.g., inspiratory, expiratory) may be used to calculate a respiratory period and / or predict a peak or midpoint of a future inspiratory phase, such calculations, predictions, etc. may be performed without identifying an onset of expiration. Similarly, calculation of a stimulation period (e.g., duration, start time, stop time) also may be performed without identifying an onset of expiration.

[0227] FIG. 16A includes charts 2700 illustrating a filtered acoustic sensor signal 2702 and a signal 2704 indicative of heart beat sounds. In some examples, filtered acoustic sensor signal 2702 is generated by filtering acoustic sensor signal 1402 of FIG. 6. In some examples, the acoustic sensor signal may be low1618.294.1 1 172 pass filtered with a low pass frequency of about 200 hertz to generate the filtered acoustic sensor signal 2702. In some examples, the acoustic sensor signal may be band pass filtered via a low pass filter with a low pass frequency of about 200 hertz and a high pass filter with a high pass frequency of about 20 hertz to generate the filtered acoustic sensor signal 2702. The signal 2704 may be generated by taking the absolute value or square of the filtered acoustic sensor signal 2702 and determining a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. With signal 2704 generated, the peaks of the signal indicated at 2706 correspond to heart beats. Therefore, based on an acoustic sensor signal, heart beats and associated parameters (e.g., heart rate, heart rate variability, etc.) may be determined.

[0228] The heart rate and heart rate variability parameters may be used for a wide variety of purposes such as, but not limited to, determining sleep-wake status (e.g., various sleep onset determinations), sleep stage detection or sleep stage related information, timing stimulation relative to respiration, determining disease burden, determining arousals, etc. In some such examples, the determination of disease burden may comprise detection of sleep disordered breathing events, which may be used in determining, assessing, etc. therapy outcomes such as, but not limited to, AHI, as well as titrating stimulation parameters, adjusting sensitivity of sensing the physiologic information, etc.

[0229] FIG. 16B is a diagram schematically representing an example device 2800 for detecting cardiac information. In some examples, device 2800 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A- 1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B- 4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2800 may include a digital sensor 2802 and a microcontroller unit (MCU) 2804.1618.294.1 1 173MCU 2804 may include a downsample element 2806, a low pass filter 2808, and a cardiac detection element 2810. In some examples, MCU 2804 may further include a low pass filter 2812. In some examples, MCU 2804 may be implemented by a component 1024a of FIG. 5A-5G. An output of digital sensor 2802 is communicatively coupled to an input of downsample element 2806 of MCU 2804 through a communication path 2803. Alternatively, the output of digital sensor 2802 is communicatively coupled to an input of low pass filter 2812 through a communication path 281 1 , and an output of low pass filter 2812 is communicatively coupled to the input of downsample element 2806 through a communication path 2813. An output of downsample element 2806 is communicatively coupled to an input of low pass filter 2808 through a communication path 2807. An output of low pass filter 2808 is communicatively coupled to an input of cardiac detection element 2810 through a communication path 2809.

[0230] In some examples, digital sensor 2802 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2802 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2802 may include a high sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2802 outputs the digital sensor signal on communication path 2803. Alternatively, if MCU includes low pass filter 2812, digital sensor 2802 outputs the digital sensor signal on communication path 281 1.

[0231] Low pass filter 2812 may include an anti-aliasing low pass filter. Downsample element 2806 receives the digital sensor signal on communication path 2803 or the low pass filtered digital sensor signal from low pass filter 2812 on communication path 2813 and downsamples the digital sensor signal from the high sample rate to a low sample rate (LSR), such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The1618.294.1 1 174LSR is an integer divisor of the HSR. Downsample element 2806 outputs the downsampled digital sensor signal on communication path 2807.

[0232] Low pass filter 2808 receives the downsampled digital sensor signal on communication path 2807 and low pass filters the downsampled digital sensor signal. In some examples, low pass filter 2808 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2808 outputs the low pass filtered digital sensor signal on communication path 2809. In some examples, the low pass filtered digital sensor signal may correspond to signal 2702 of FIG. 16A.

[0233] Cardiac detection element 2810 receives the low pass filtered digital sensor signal on communication path 2809 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal. In some examples, cardiac detection element 2810 takes the absolute value or square of the low pass filtered digital sensor signal on communication path 2809 and determines a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. In some examples, the moving average of the signal may correspond to signal 2704 of FIG. 16A. With the moving average of the signal generated, cardiac detection element 2810 may detect the peaks (e.g., 2706 of FIG. 16A) of the signal, which correspond to heart beats. The peaks may be detected by thresholding, a derivative method, determining local maxima peaks within a time window, implementing a wavelet transform, and / or another suitable method.

[0234] By implementing device 2800 using digital sensor 2802 and MCU 2804, device 2800 incorporates flexibility for adjusting the device’s functionality after the device is manufactured. For example, the sampling rate of digital sensor 2802 may be selected and / or updated, the cutoff frequency of low pass filter 28121618.294.1 1 175 and / or the cutoff frequency of low pass filter 2808 may be selected and / or updated, and / or the sampling rate of downsample element 2806 may be selected and / or updated. In addition, cardiac detection element 2810 may be updated. Each of these values and / or processes might be programmable in MCU 2804 enabling the maximum design flexibility compared to the devices described below with reference to FIGS. 16C-16D.

[0235] FIG. 16C is a diagram schematically representing an example device 2820 for detecting respiratory information. In some examples, device 2820 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2820 may include an analog sensor 2822 and a MCU or application specific integrated circuit (ASIC) 2824. MCU or ASIC 2824 may include a hardware (e.g., analog) low pass filter 2826, an analog to digital conversion element 2828, a low pass filter 2808, and a cardiac detection element 2810. In some examples, MCU or ASIC 2824 may be implemented by a component 1024a of FIG. 5A-5G. An output of analog sensor 2822 is communicatively coupled to an input of hardware low pass filter 2826 through an analog signal path 2823. An output of hardware low pass filter 2826 is communicatively coupled to an input of analog to digital conversion element 2828 through an analog signal path 2827. In some examples, hardware low pass filter 2826 may be excluded, such that an output of analog sensor 2822 is communicatively coupled to an input of analog to digital conversion element 2828. An output of analog to digital conversion element 2828 is communicatively coupled to an input of low pass filter 2808 through a communication path 2829. An output of low pass filter 2808 is communicatively coupled to an input of cardiac detection element 2810 through a communication path 2809.

[0236] In some examples, analog sensor 2822 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2822 may include a MEMS, an1618.294.1 1 176 optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2822 outputs the analog sensor signal on analog signal path 2823.

[0237] Hardware low pass filter 2826 receives the analog sensor signal on analog signal path 2823 and low pass filters the analog sensor signal. In some examples, the hardware (analog) low pass filter 2826 includes a cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. Hardware low pass filter 2826 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into the MCU or ASIC 2824. Hardware low pass filter 2826 outputs the low pass filtered analog sensor signal on analog signal path 2827.

[0238] Analog to digital conversion element 2828 receives the low pass filtered analog sensor signal (or analog sensor signal directly from analog sensor 2822 if hardware low pass filter 2826 is excluded) and converts the analog sensor signal to a digital sensor signal. In some examples, a sampling rate of the analog to digital conversion element 2828 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2828 outputs the digital sensor signal on communication path 2829.

[0239] Low pass filter 2808 receives the digital sensor signal on communication path 2829 and low pass filters the digital sensor signal. In some examples, low pass filter 2808 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2808 outputs the low pass filtered digital sensor signal on communication path 2809. In some examples, the low pass filtered digital sensor signal may correspond to signal 2702 of FIG. 16A. Cardiac detection element 2810 receives the low pass filtered digital sensor signal on communication path 2809 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal as previously described.1618.294.1 1 177

[0240] Compared to device 2800 of FIG. 16B, device 2820 of FIG. 16C provides a tradeoff between power consumption (which might be higher in device 2800) and programmability. Analog sensor 2822 may consume less power than digital sensor 2802, but the analog to digital conversion element 2828 is added in device 2820 to support the analog sensor.

[0241] FIG. 16D is a diagram schematically representing an example device 2840 for detecting cardiac information. In some examples, device 2840 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A- 1 B; 117 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a-1040b of FIGS. 4B- 4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2840 includes an analog sensor 2822, a hardware (e.g., analog) low pass or band pass filter 2846, an analog to digital conversion element 2848, and a cardiac detection element 2810. An output of analog sensor 2822 is communicatively coupled to an input of hardware low pass or band pass filter 2846 through an analog signal path 2823. An output of hardware low pass or band pass filter 2846 is communicatively coupled to an input of analog to digital conversion element 2848 through an analog signal path 2847. An output of analog to digital conversion element 2848 is communicatively coupled to an input of cardiac detection element 2810 through a communication path 2849.

[0242] In some examples, analog sensor 2822 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2822 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2822 outputs the analog sensor signal on analog signal path 2823.

[0243] Hardware low pass or band pass filter 2846 receives the analog sensor signal on analog signal path 2823 and low pass or band pass filters the analog sensor signal. In some examples, the hardware (analog) filter 2846 includes a low pass filter with a cutoff frequency within a range between about 80 hertz and1618.294.1 1 178 about 300 hertz, such as about 200 hertz. In some examples, the hardware (analog) filter 2846 includes a band pass filter with a lower cutoff frequency within a range between about 80 hertz and about 300 hertz, such as about 200 hertz, and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. The higher cutoff frequency may be between 2 to 10 times higher than the lower cutoff frequency. Hardware low pass or band pass filter 2846 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into a MCU or ASIC. Hardware low pass or band pass filter 2846 outputs the low pass or band pass filtered analog sensor signal on analog signal path 2847.

[0244] Analog to digital conversion element 2848 receives the low pass or band pass filtered analog sensor signal on analog signal path 2847 and converts the analog sensor signal to a digital sensor signal. In some examples, a sampling rate of the analog to digital conversion element 2848 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2848 outputs the digital sensor signal on communication path 2849. In some examples, the digital sensor signal may correspond to signal 2702 of FIG. 16A. Cardiac detection element 2810 receives the digital sensor signal on communication path 2849 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the digital sensor signal as previously described.

[0245] Device 2840 of FIG. 16D includes hardware filtering (e.g., analog filtering), which can be faster and more efficient (e.g., lower power consumption) than the digital filtering of device 2800 of FIG. 16B and device 2820 of FIG. 16C. In device 2840, however, programmability may be limited compared to the devices 2800 and 2820. If signal processing changes are desired in device 2840, hardware changes are needed, whereas in devices 2800 and 2820 signal processing changes may be implemented without impacting the hardware design.

[0246] FIG. 17A is a diagram schematically representing an example device 2900a for detecting respiratory information and cardiac information. In some examples, device 2900a may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS.1618.294.1 1 1793A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21A-21 B). Device 2900a may include a digital sensor 2902 and a microcontroller unit (MCU) 2904a. MCU 2904a may include a low pass filter 2918, a downsample element 2906, a low pass filter 2908, a cardiac detection element 2910, a high pass filter 2912, and a respiratory detection element 2914. In some examples, the low pass filter 2918 may be excluded. In some examples, MCU 2904a may be implemented by a component 1024a of FIG. 5A-5G.

[0247] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of MCU 2904a through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of low pass filter 2908 and an input of high pass filter 2912 through a communication path 2907. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 through a communication path 2909. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 through a communication path 2913.

[0248] In some examples, digital sensor 2902 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2902 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2902 may include a high sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2902 outputs the digital sensor signal on communication path 2903.1618.294.1 1 180

[0249] Low pass filter 2918 receives the digital sensor signal on communication path 2903 and low pass filters the digital sensor signal. Low pass filter 2918 may include an anti-aliasing low pass filter. Downsample element 2906 receives the low pass filtered digital sensor signal (or the digital sensor signal on communication path 2903 if low pass filter 2918 is excluded) and downsamples the digital sensor signal from the high sample rate to a low sample rate (LSR), such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The LSR is an integer divisor of the HSR. Downsample element 2906 outputs the downsampled digital sensor signal on communication path 2907.

[0250] Low pass filter 2908 receives the downsampled digital sensor signal on communication path 2907 and low pass filters the downsampled digital sensor signal. In some examples, low pass filter 2908 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2908 outputs the low pass filtered digital sensor signal on communication path 2909. In some examples, the low pass filtered digital sensor signal may correspond to signal 2902 of FIG. 16A.

[0251] Cardiac detection element 2910 receives the low pass filtered digital sensor signal on communication path 2909 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal. In some examples, cardiac detection element 2910 takes the absolute value or square of the low pass filtered digital sensor signal on communication path 2909 and determines a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. In some examples, the moving average of the signal may correspond to signal 2704 of FIG. 16A. With the moving average of the signal generated, cardiac detection1618.294.1 1 181 element 2910 may detect the peaks (e.g., 2706 of FIG. 16A) of the signal, which correspond to heart beats.

[0252] High pass filter 2912 receives the downsampled digital sensor signal on communication path 2907 and high pass filters the downsampled digital sensor signal. In some examples, high pass filter 2912 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2912 outputs the high pass filtered digital sensor signal on communication path 2913. In some examples, the high pass filtered digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0253] Respiratory detection element 2914 receives the high pass filtered digital sensor signal on communication path 2913 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the high pass filtered digital sensor signal. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the high pass filtered digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0254] By using the same sensor 2902 and downsample element 2906 for both cardiac detection and respiratory detection in device 2900a, power consumption may be reduced and reliability may be improved compared to using two separate sensors and / or two separate signal processing blocks for cardiac detection and respiratory detection (e.g., as shown in FIGS. 14A-14D for respiratory detection and in FIGS. 16B-16D for cardiac detection).

[0255] FIG. 17B is a diagram schematically representing an example device 2900b for detecting respiratory information and cardiac information. In some examples, device 2900b may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21A-21 B). Device 2900b may include a digital sensor 2902, a1618.294.1 1 182 microcontroller unit (MCU) 2904b, and an application specific integrated circuit (ASIC) 2916a. ASIC 2916a may include a low pass filter 2918 and a downsample element 2906. In some examples, the low pass filter 2918 may be excluded. MCU 2904b may include a low pass filter 2908, a cardiac detection element 2910, a high pass filter 2912, and a respiratory detection element 2914. In some examples, MCU 2904b and ASIC 2916a may each be implemented by a component 1024a of FIG. 5A-5G.

[0256] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of ASIC 2916a through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of low pass filter 2908 and an input of high pass filter 2912 of MCU 2904b through a communication path 2907. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 through a communication path 2909. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 through a communication path 2913.

[0257] The low pass filter 2918, digital sensor 2902, downsample element 2906, low pass filter 2908, cardiac detection element 2910, high pass filter 2912, and respiratory detection element 2914 operate similarly as described above with reference to FIG. 17A. By implementing the optional low pass filter 2918 and the downsample element 2906 in ASIC 2916a and the low pass filter 2908, cardiac detection element 2910, high pass filter 2912, and respiratory detection element 2914 in MCU 2904b, the device 2900b may be optimized for lower power consumption than device 2900a of FIG. 17A.

[0258] FIG. 17C is a diagram schematically representing an example device 2900c for detecting respiratory information and cardiac information. In some examples, device 2900c may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS.1618.294.1 1 1833A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2900c may include a digital sensor 2902, a microcontroller unit (MCU) 2904c, and an application specific integrated circuit (ASIC) 2916b. ASIC 2916b may include a low pass filter 2918, a downsample element 2906, a low pass filter 2908, and a high pass filter 2912. In some examples, the low pass filter 2918 may be excluded. MCU 2904c may include a cardiac detection element 2910 and a respiratory detection element 2914. In some examples, MCU 2904c and ASIC 2916b may each be implemented by a component 1024a of FIG. 5A-5G.

[0259] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of ASIC 2916b through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of low pass filter 2908 and an input of high pass filter 2912 of ASIC 2916b through a communication path 2907. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 of MCU 2904c through a communication path 2909. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 of MCU 2904c through a communication path 2913.

[0260] The digital sensor 2902, low pass filter 2918, downsample element 2906, low pass filter 2908, cardiac detection element 2910, high pass filter 2912, and respiratory detection element 2914 operate similarly as described above with reference to FIGS. 17A and 17B. By implementing the optional low pass filter 2918, downsample element 2906, low pass filter 2908, and high pass filter 2912 in ASIC 2916b, and cardiac detection element 2910 and respiratory detection element 2914 in MCU 2904c, the device 2900c may be optimized for lower power consumption than the device 2900a of FIG. 17A.1618.294.1 1 184

[0261] FIG. 17D is a diagram schematically representing an example device 2920 for detecting cardiac information and respiratory information. In some examples, device 2920 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2920 may include an analog sensor 2922 and a MCU or ASIC 2924a. MCU or ASIC 2924a may include a hardware (e.g., analog) low pass filter 2926, an analog to digital conversion element 2928, a low pass filter 2908, a cardiac detection element 2910, a high pass filter 2912, and a respiratory detection element 2914. In some examples, MCU or ASIC 2924a may be implemented by a component 1024a of FIG. 5A-5G.

[0262] An output of analog sensor 2922 is communicatively coupled to an input of hardware low pass filter 2926 through an analog signal path 2923. An output of hardware low pass filter 2926 is communicatively coupled to an input of analog to digital conversion element 2928 through an analog signal path 2927. In some examples, hardware low pass filter 2926 may be excluded, such that an output of analog sensor 2922 is communicatively coupled to an input of analog to digital conversion element 2928. An output of analog to digital conversion element 2928 is communicatively coupled to an input of low pass filter 2908 and an input of high pass filter 2912 through a communication path 2929. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 through a communication path 2909. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 through a communication path 2913.

[0263] In some examples, analog sensor 2922 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2922 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as1618.294.1 1 185 previously described. Analog sensor 2922 outputs the analog sensor signal on analog signal path 2923.

[0264] Hardware low pass filter 2926 receives the analog sensor signal on analog signal path 2923 and low pass filters the analog sensor signal. In some examples, the hardware (analog) low pass filter 2926 includes a cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. Hardware low pass filter 2926 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into the MCll or ASIC 2924a. Hardware low pass filter 2926 outputs the low pass filtered analog sensor signal on analog signal path 2927.

[0265] Analog to digital conversion element 2928 receives the low pass filtered analog sensor signal (or analog sensor signal directly from analog sensor 2922 if hardware low pass filter 2926 is excluded) and converts the analog sensor signal to a digital sensor signal. In some examples, a sampling rate of the analog to digital conversion element 2928 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2928 outputs the digital sensor signal on communication path 2929.

[0266] Low pass filter 2908 receives the digital sensor signal on communication path 2929 and low pass filters the digital sensor signal. In some examples, low pass filter 2908 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2908 outputs the low pass filtered digital sensor signal on communication path 2909. In some examples, the low pass filtered digital sensor signal may correspond to signal 2702 of FIG. 16A. Cardiac detection element 2910 receives the low pass filtered digital sensor signal on communication path 2909 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal as previously described.

[0267] High pass filter 2912 receives the digital sensor signal on communication path 2929 and high pass filters the digital sensor signal. In some examples, high1618.294.1 1 186 pass filter 2912 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2912 outputs the high pass filtered digital sensor signal on communication path 2913. In some examples, the high pass filtered digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0268] Respiratory detection element 2914 receives the high pass filtered digital sensor signal on communication path 2913 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the high pass filtered digital sensor signal. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the high pass filtered digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0269] By using the same sensor 2922, optional hardware low pass filter 2926, and analog to digital conversion element 2928 for both cardiac detection and respiratory detection in device 2920, power consumption may be reduced and reliability may be improved compared to using two separate sensors and / or two separate signal processing blocks for cardiac detection and respiratory detection (e.g., as shown in FIGS. 14A-14D for respiratory detection and in FIGS. 16B-16D for cardiac detection).

[0270] FIG. 17E is a diagram schematically representing an example device 2940 for detecting respiratory information and cardiac information. In some examples, device 2940 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2940 may include a digital sensor 2902 and a microcontroller unit (MCU) 2904d. MCU 2904d may include a low pass filter 2918, a downsample element 2906, a low pass filter 2908, a cardiac detection element 2910, a subtract element 2942, and a respiratory detection element 2914. In some examples, the low pass filter 2918 may be excluded. In some1618.294.1 1 187 examples, MCU 2904d may be implemented by a component 1024a of FIG. 5A- 5G.

[0271] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of MCU 2904d through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of low pass filter 2908 and a first input of subtract element 2942 through a sampled signal (S) communication path 2907. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 and to a second input of subtract element 2942 through a low pass filtered sampled signal (S_LPF) communication path 2909. An output of subtract element 2942 is communicatively coupled to an input of respiratory detection element 2914 through a sampled signal minus a low pass filtered sampled signal (S - S_LPF) communication path 2943.

[0272] In some examples, digital sensor 2902 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2902 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2902 may include a high sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2902 outputs the digital sensor signal on communication path 2903.

[0273] Low pass filter 2918 may include an anti-aliasing low pass filter as previously described with reference to FIG. 17A. Downsample element 2906 receives the low pass filtered digital sensor signal (or the digital sensor signal on communication path 2903 if low pass filter 2918 is excluded) and downsamples the digital sensor signal from the high sample rate to a low sample rate (LSR),1618.294.1 1 188 such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The LSR is an integer divisor of the HSR. Downsample element 2906 outputs the downsampled digital sensor signal S on communication path 2907.

[0274] Low pass filter 2908 receives the downsampled digital sensor signal S on communication path 2907 and low pass filters the downsampled digital sensor signal S. In some examples, low pass filter 2908 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2908 outputs the low pass filtered digital sensor signal S_LPF on communication path 2909. In some examples, the low pass filtered digital sensor signal S_LPF may correspond to signal 2702 of FIG. 16A.

[0275] Cardiac detection element 2910 receives the low pass filtered digital sensor signal S_LPF on communication path 2909 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal S_LPF. In some examples, cardiac detection element 2910 takes the absolute value or square of the low pass filtered digital sensor signal S_LPF on communication path 2909 and determines a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. In some examples, the moving average of the signal may correspond to signal 2704 of FIG. 16A. With the moving average of the signal generated, cardiac detection element 2910 may detect the peaks (e.g., 2706 of FIG. 16A) of the signal, which correspond to heart beats.

[0276] Subtract element 2942 receives the downsampled digital sensor signal S on communication path 2907 and the low pass filtered digital sensor signal S_LPF on communication path 2909 and subtracts the low pass filtered digital sensor signal S_LPF from the downsampled digital sensor signal S. By1618.294.1 1 189 subtracting the low pass filtered digital sensor signal S_LPF from the downsampled digital sensor signal S, the resulting signal S - S_LPF is effectively a high pass filtered signal. Subtract element 2942 outputs the signal S - S LPF on communication path 2943. In some examples, the signal S - S_LPF may correspond to signal 2354 of FIG. 13A.

[0277] Respiratory detection element 2914 receives the signal S - S_LPF on communication path 2943 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the signal S - S_LPF. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the signal S - S_LPF and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1.

[0278] FIG. 17F is a diagram schematically representing an example device 2950 for detecting cardiac information and respiratory information. In some examples, device 2950 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2950 may include an analog sensor 2922 and a MCll or ASIC 2924b. MCll or ASIC 2924b may include a hardware (e.g., analog) low pass filter 2926, an analog to digital conversion element 2928, a low pass filter 2908, a cardiac detection element 2910, a subtract element 2952, and a respiratory detection element 2914. In some examples, MCll or ASIC 2924b may be implemented by a component 1024a of FIG. 5A-5G.

[0279] An output of analog sensor 2922 is communicatively coupled to an input of hardware low pass filter 2926 through an analog signal path 2923. An output of hardware low pass filter 2926 is communicatively coupled to an input of analog to digital conversion element 2928 through an analog signal path 2927. In some examples, hardware low pass filter 2926 may be excluded, such that an output of analog sensor 2922 is communicatively coupled to an input of analog to digital conversion element 2928. An output of analog to digital conversion element 29281618.294.1 1 190 is communicatively coupled to an input of low pass filter 2908 and a first input of subtract element 2952 through a sampled signal (S) communication path 2929. An output of low pass filter 2908 is communicatively coupled to an input of cardiac detection element 2910 and a second input of subtract element 2952 through a low pass filtered sampled signal (S_LPF) communication path 2909. An output of subtract element 2952 is communicatively coupled to an input of respiratory detection element 2914 through a sampled signal minus a low pass filtered sampled signal (S - S_LPF) communication path 2953.

[0280] In some examples, analog sensor 2922 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2922 may include a MEMS, an optical mechanism, a piezoelectric crystal, or a piezoelectric film configured for sensing airflow of a patient as previously described. Analog sensor 2922 outputs the analog sensor signal on analog signal path 2923.

[0281] Hardware low pass filter 2926 receives the analog sensor signal on analog signal path 2923 and low pass filters the analog sensor signal. In some examples, the hardware (analog) low pass filter 2926 includes a cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. Hardware low pass filter 2926 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into the MCU or ASIC 2924b. Hardware low pass filter 2926 outputs the low pass filtered analog sensor signal on analog signal path 2927.

[0282] Analog to digital conversion element 2928 receives the low pass filtered analog sensor signal (or analog sensor signal directly from analog sensor 2922 if hardware low pass filter 2926 is excluded) and converts the analog sensor signal to a digital sensor signal S. In some examples, a sampling rate of the analog to digital conversion element 2928 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2928 outputs the digital sensor signal S on communication path 2929.1618.294.1 1 191

[0283] Low pass filter 2908 receives the digital sensor signal S on communication path 2929 and low pass filters the digital sensor signal. In some examples, low pass filter 2908 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). Low pass filter 2908 outputs the low pass filtered digital sensor signal S_LPF on communication path 2909. In some examples, the low pass filtered digital sensor signal S_LPF may correspond to signal 2702 of FIG. 16A. Cardiac detection element 2910 receives the low pass filtered digital sensor signal S_LPF on communication path 2909 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the low pass filtered digital sensor signal as previously described.

[0284] Subtract element 2952 receives the digital sensor signal S on communication path 2929 and the low pass filtered digital sensor signal S_LPF on communication path 2909 and subtracts the low pass filtered digital sensor signal S_LPF from the digital sensor signal S. By subtracting the low pass filtered digital sensor signal S_LPF from the digital sensor signal S, the resulting signal S - S_LPF is effectively a high pass filtered signal. Subtract element 2952 outputs the signal S - S_LPF on communication path 2953. In some examples, the signal S - S_LPF may correspond to signal 2354 of FIG. 13A.

[0285] Respiratory detection element 2914 receives the signal S - S_LPF on communication path 2953 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the signal S - S LPF. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the signal S - S_LPF and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1.

[0286] FIG. 17G is a diagram schematically representing an example device 2960 for detecting respiratory information and cardiac information. In some examples, device 2960 may be implemented within an implantable medical1618.294.1 1 192 device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2960 may include a digital sensor 2902 and a microcontroller unit (MCU) 2904e. MCU 2904e may include a low pass filter 2918, a downsample element 2906, a high pass filter 2912, a respiratory detection element 2914, a subtract element 2962, and a cardiac detection element 2910. In some examples, the low pass filter 2918 may be excluded. In some examples, MCU 2904e may be implemented by a component 1024a of FIG. 5A-5G.

[0287] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of MCU 2904e through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of high pass filter 2912 and a first input of subtract element 2962 through a sampled signal (S) communication path 2907. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 and to a second input of subtract element 2962 through a high pass filtered sampled signal (S_HPF) communication path 2913. An output of subtract element 2962 is communicatively coupled to an input of cardiac detection element 2910 through a sampled signal minus a high pass filtered sampled signal (S - S_HPF) communication path 2963.

[0288] In some examples, digital sensor 2902 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2902 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2902 may include a high1618.294.1 1 193 sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2902 outputs the digital sensor signal on communication path 2903.

[0289] Low pass filter 2918 receives the digital sensor signal on communication path 2903 and low pass filters the digital sensor signal. Low pass filter 2918 may include an anti-aliasing low pass filter. Downsample element 2906 receives the low pass filtered digital sensor signal (or the digital sensor signal on communication path 2903 if low pass filter 2918 is excluded) and downsamples the digital sensor signal from the high sample rate to a low sample rate (LSR), such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The LSR is an integer divisor of the HSR. Downsample element 2906 outputs the downsampled digital sensor signal S on communication path 2907.

[0290] High pass filter 2912 receives the downsampled digital sensor signal S on communication path 2907 and high pass filters the downsampled digital sensor signal. In some examples, high pass filter 2912 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2912 outputs the high pass filtered digital sensor signal S_HPF on communication path 2913. In some examples, the high pass filtered digital sensor signal S_HPF may correspond to signal 2354 of FIG. 13A.

[0291] Respiratory detection element 2914 receives the signal S_HPF on communication path 2913 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the signal S_HPF. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the signal S_HPF and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1.

[0292] Subtract element 2962 receives the downsampled digital sensor signal S on communication path 2907 and the high pass filtered digital sensor signal1618.294.1 1 194S_HPF on communication path 2913 and subtracts the high pass filtered digital sensor signal S_HPF from the downsampled digital sensor signal S. By subtracting the high pass filtered digital sensor signal S_HPF from the downsampled digital sensor signal S, the resulting signal S - S_HPF is effectively a low pass filtered signal. Subtract element 2962 outputs the signal S - S_HPF on communication path 2963. In some examples, the signal S - S_HPF may correspond to signal 2702 of FIG. 16A.

[0293] Cardiac detection element 2910 receives the signal S - S_HPF on communication path 2963 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the signal S - S_HPF. In some examples, cardiac detection element 2910 takes the absolute value or square of the signal S - S HPF on communication path 2963 and determines a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. In some examples, the moving average of the signal may correspond to signal 2704 of FIG. 16A. With the moving average of the signal generated, cardiac detection element 2910 may detect the peaks (e.g., 2706 of FIG. 16A) of the signal, which correspond to heart beats.

[0294] FIG. 17H is a diagram schematically representing an example device 2970 for detecting cardiac information and respiratory information. In some examples, device 2970 may be implemented within an implantable medical device (e.g., 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2970 may include an analog sensor 2922 and a MCll or ASIC 2924c. MCll or ASIC 2924c may include a hardware (e.g., analog) low pass filter 2926, an analog to digital conversion element 2928, a high pass filter 2912, a respiratory detection element 2914, a subtract element 2972, and a1618.294.1 1 195 cardiac detection element 2910. In some examples, MCll or ASIC 2924c may be implemented by a component 1024a of FIG. 5A-5G.

[0295] An output of analog sensor 2922 is communicatively coupled to an input of hardware low pass filter 2926 through an analog signal path 2923. An output of hardware low pass filter 2926 is communicatively coupled to an input of analog to digital conversion element 2928 through an analog signal path 2927. In some examples, hardware low pass filter 2926 may be excluded, such that an output of analog sensor 2922 is communicatively coupled to an input of analog to digital conversion element 2928. An output of analog to digital conversion element 2928 is communicatively coupled to an input of high pass filter 2912 and a first input of subtract element 2972 through a sampled signal (S) communication path 2929. An output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 and a second input of subtract element 2972 through a high pass filtered sampled signal (S_HPF) communication path 2913. An output of subtract element 2972 is communicatively coupled to an input of cardiac detection element 2910 through a sampled signal minus a high pass filtered sampled signal (S - S_HPF) communication path 2973.

[0296] In some examples, analog sensor 2922 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Analog sensor 2922 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. Analog sensor 2922 outputs the analog sensor signal on analog signal path 2923.

[0297] Hardware low pass filter 2926 receives the analog sensor signal on analog signal path 2923 and low pass filters the analog sensor signal. In some examples, the hardware (analog) low pass filter 2926 includes a cutoff frequency within a range between about 400 hertz and about 2000 hertz, such as about 800 hertz. Hardware low pass filter 2926 may be implemented using discrete components, a separate integrated circuit (IC), or integrated into the MCU or1618.294.1 1 196ASIC 2924c. Hardware low pass filter 2926 outputs the low pass filtered analog sensor signal on analog signal path 2927.

[0298] Analog to digital conversion element 2928 receives the low pass filtered analog sensor signal (or analog sensor signal directly from analog sensor 2922 if hardware low pass filter 2926 is excluded) and converts the analog sensor signal to a digital sensor signal S. In some examples, a sampling rate of the analog to digital conversion element 2928 is within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz (e.g., LSR). Analog to digital conversion element 2928 outputs the digital sensor signal S on communication path 2929.

[0299] High pass filter 2912 receives the digital sensor signal S on communication path 2929 and high pass filters the digital sensor signal. In some examples, high pass filter 2912 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2912 outputs the high pass filtered digital sensor signal S_HPF on communication path 2913. In some examples, the high pass filtered digital sensor signal S_HPF may correspond to signal 2354 of FIG. 13A.

[0300] Respiratory detection element 2914 receives the signal S_HPF on communication path 2913 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the signal S_HPF. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the signal S_HPF and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1.

[0301] Subtract element 2972 receives the digital sensor signal S on communication path 2929 and the high pass filtered digital sensor signal S_HPF on communication path 2913 and subtracts the high pass filtered digital sensor signal S_HPF from the digital sensor signal S. By subtracting the high pass filtered digital sensor signal S_HPF from the digital sensor signal S, the resulting1618.294.1 1 197 signal S - S_HPF is effectively a low pass filtered signal. Subtract element 2972 outputs the signal S - S_HPF on communication path 2973. In some examples, the signal S - S HPF may correspond to signal 2702 of FIG. 16A.

[0302] Cardiac detection element 2910 receives the signal S - S_HPF on communication path 2973 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the signal S - S_HPF. In some examples, cardiac detection element 2910 takes the absolute value or square of the signal S - S_HPF on communication path 2973 and determines a moving average of the signal. The averaging window may be selected based on the sampling rate. For example, if the sampling rate is low (e.g., less than about 2000 samples per second), the averaging window may be between about 2 and about 20 samples. If the sampling rate is high (e.g., 24, 48, or 96 kilosamples per seconds), the averaging window may be between about 20 samples and about 200 samples. In some examples, the moving average of the signal may correspond to signal 2704 of FIG. 16A. With the moving average of the signal generated, cardiac detection element 2910 may detect the peaks (e.g., 2706 of FIG. 16A) of the signal, which correspond to heart beats.

[0303] Referring back to FIGS. 17A-17H, digital sensors 2902 (FIGS. 17A-17C, 17E, and 17G) may have a higher sampling rate and consume more power than analog sensors 2922 (FIGS. 17D, 17F, and 17H). Thus, in some examples, the downsample element 2906 (FIGS. 17A-17C, 17E, and 17G) is used to discard extraneous data that is not needed to detect respiratory information and cardiac information. When using an analog sensor, the sampling frequency may be selected to be lower to provide power saving and to exclude a downsample element.

[0304] In some examples, the signal on communication path 2907 (FIGS. 17A- 17C, 17E, and 17G) may be considered to be equivalent to the signal on communication path 2929 (FIGS. 17D, 17F, and 17H). The signal on communication path 2907 is obtained after an optional low pass filter 2918 and downsample element 2906, while the signal on communication path 2929 is obtained after an optional hardware low pass filter 2926 and an analog to digital conversion element 2928. Accordingly, the signal on communication path 29071618.294.1 1 198 and the signal on communication path 2929 may represent the same signal, but obtained in different ways (i.e., digital sensor versus analog sensor).

[0305] Device 2940 of FIG. 17E is similar to device 2960 of FIG. 17G except that in device 2940 a low pass filter 2908 is implemented and in device 2960 a high pass filter 2912 is implemented. Devices 2900a, 2900b, 2900c, and 2920 of FIGS. 17A-17D implement two separate filters including a high pass filter 2912 for respiratory detection and a low pass filter 2908 for cardiac detection. The high pass filter 2912 and the low pass filter 2908 may have different cutoff frequencies.

[0306] Devices 2940, 2950, 2960, and 2970 of FIGS. 17E-17H implement a single filter, either a low pass filter 2908 (FIGS. 17E and 17F) or a high pass filter 2912 (FIGS. 17G and 17H), and a subtract element 2942 (FIG. 17E), 2952 (FIG. 17F), 2962 (FIG. 17G), or 2972 (FIG. 17H) to obtain the counterpart signal. In these examples, the low pass filter is designed to detect cardiac information and then the low pass filtered signal is subtracted from the original signal to detect respiratory information. Alternatively, the high pass filter is designed to detect respiratory information and then the high pass filtered signal is subtracted from the original signal to detect cardiac information. Using one filter and a subtract element may have less design flexibility than using two separate filters, since different cutoff frequencies cannot be used. However, using one filter and a subtract element may consume less power than two separate filters, since a subtraction operation is less computationally demanding than a filtering operation.

[0307] FIG. 171 is a diagram schematically representing an example device 2980 for detecting respiratory information and cardiac information. In some examples, device 2980 may be implemented within an implantable medical device (e.g. , 22A, 22B of FIGS. 1 A-1 B; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D; 3520 of FIGS. 20A-20B; or 3620 of FIGS. 21 A-21 B). Device 2980 may include a digital sensor 2902 and a microcontroller unit (MCU) 2904f. MCU 2904f may include a low pass filter 2918, a downsample element 2906, a saturation detection element 2984, a cardiac detection element 2910, a high pass filter 2912, and a respiratory detection element 2914. In some examples, the low pass filter 2918 may be excluded. In1618.294.1 1 199 some examples, MCU 2904f may be implemented by a component 1024a of FIG. 5A-5G.

[0308] An output of digital sensor 2902 is communicatively coupled to an input of low pass filter 2918 of MCU 2904f through a communication path 2903. An output of low pass filter 2918 is communicatively coupled to an input of downsample element 2906 through a communication path 2919. In some examples, low pass filter 2918 may be excluded and the output of digital sensor 2902 may be communicatively coupled to the input of downsample element 2906 through the communication path 2903. An output of downsample element 2906 is communicatively coupled to an input of high pass filter 2912 through a communication path 2907. A first output of high pass filter 2912 is communicatively coupled to an input of saturation detection element 2984 through a communication path 2982. An output of saturation detection element 2984 is communicatively coupled to an input of cardiac detection element 2910 through a communication path 2985. A second output of high pass filter 2912 is communicatively coupled to an input of respiratory detection element 2914 through a communication path 2913.

[0309] In some examples, digital sensor 2902 may be an acoustic sensor, such as acoustic sensing element 24 of FIGS. 1 A-1 B, sensing element 54 of FIG. 2A, sensing element 128 of FIG. 2B, acoustic sensor 520 of FIGS. 3A-3E, or acoustic sensor 1024b of FIGS. 5A-5G. Digital sensor 2902 may include a MEMS, an optical mechanism, a piezoelectric crystal, a piezoelectric film, or an acoustic signal sensing accelerometer configured for sensing airflow of a patient as previously described. In some examples, digital sensor 2902 may include a high sample rate (HSR) and corresponding data rate, such as about 24 kilohertz, about 48 kilohertz, or about 96 kilohertz. Digital sensor 2902 outputs the digital sensor signal on communication path 2903.

[0310] Low pass filter 2918 receives the digital sensor signal on communication path 2903 and low pass filters the digital sensor signal. Low pass filter 2918 may include an anti-aliasing low pass filter. Downsample element 2906 receives the low pass filtered digital sensor signal (or the digital sensor signal on communication path 2903 if low pass filter 2918 is excluded) and downsamples1618.294.1 1 1100 the digital sensor signal from the high sample rate to a low sample rate (LSR), such as a LSR within a range between about 600 hertz and about 4000 hertz, such as about 800 hertz. The LSR is an integer divisor of the HSR. Downsample element 2906 outputs the downsampled digital sensor signal on communication path 2907.

[0311] High pass filter 2912 receives the downsampled digital sensor signal on communication path 2907 and high pass filters the downsampled digital sensor signal. In some examples, high pass filter 2912 includes a cutoff frequency (fc) within a range between about 80 hertz and about 300 hertz, such as about 200 hertz. The LSR may be selected such that LSR is about 3 to about 10 times higher than fc, such as 4 (e.g., LSR = 800 hertz, fc = 200 hertz, ratio of 4). High pass filter 2912 outputs the high pass filtered digital sensor signal on communication path 2913. In some examples, the high pass filtered digital sensor signal may correspond to signal 2354 of FIG. 13A.

[0312] Respiratory detection element 2914 receives the high pass filtered digital sensor signal on communication path 2913 and determines respiratory information (e.g., inhalation, exhalation, expiratory pause) of the patient based on the high pass filtered digital sensor signal. In some examples, respiratory detection element 2914 generates an envelope (e.g., 2362 of FIG. 13B) or intensity signal (e.g., 1452 of FIG. 7) based on the high pass filtered digital sensor signal and determines respiratory information based on the envelope or intensity signal as previously described with reference to at least FIGS. 6-1 1 .

[0313] High pass filter 2912 also outputs saturation flags on communication path 2982 in response to the downsampled digital sensor signal on communication path 2907 saturating (clipping) high pass filter 2912. Saturation detection element 2984 detects the saturation flags from high pass filter 2912 and generates a signal corresponding to cardiac events based the saturation flags. Since the magnitude of cardiac components of the sensor signal may be orders of magnitude larger than respiration components of the sensor signal, amplifying the sensor signal and filtering the sensor signal for the frequency band of the cardiac components enables the saturation flags to provide peak detection, which indicate cardiac events. Saturation detection element 2984 may accept or reject1618.294.1 1 1101 the saturation flags as cardiac events based on the length of time of saturation and / or the time between saturation flags, since the normal beats per minute (BPM) of an individual falls between about 40 BPM and about 200 BPM so saturation flags outside of these boundaries means a cardiac event has not been detected. Cardiac detection element 2910 receives the detected cardiac events (detected peaks) on communication path 2910 and determines cardiac information (e.g., heart rate, heart rate variability) of the patient based on the detected cardiac events. In some examples, saturation detection element 2984 may be used in place of low pass filter 2908 of FIGS. 17B-17D in a similar manner as described with reference to FIG. 171. In some examples, the saturation detection element 2984 may detect saturation flags from another processing component to detect cardiac events other than high pass filter 2912, such as low pass filter 2918 or downsample element 2906 of FIGS. 17B, 17C, 17E, 17G, or 171 or low pass filter 2926 or analog-to-digital (A / D) conversion element 2928 of FIGS. 17D, 17F, or 17H.

[0314] FIG. 18A is a diagram 3000 schematically representing an example IMD 3020 within a patient. The IMD 3020 includes a first element 3022 and a second element 3024 implanted in a patient. In some examples, the IMD 3020 may include at least some of substantially the same features as, and / or an example implementation of at least some features of, the examples described in association with FIGS. 1 A-17I and 18B-36B. In some examples, the IMD 3020 may include an electrical stimulation element which may be implanted in a head 112 or neck region 114 of the patient. In some such examples, the first element 3022 may include a power and / or control element such as (but not limited to) an implantable pulse generator (IPG).

[0315] In some examples, the entire IMD 3020 is sized and / or shaped to be implanted within the neck region 1 14, and in some examples, the entire IMD 3020 may be sized and shaped for non-muscular, extra-vascular implantation. This arrangement stands in sharp contrast with at least some non-example IMDs which may be injectable via a hypodermic needle into muscular tissue and / or which may be sized and / or shaped for intravascular delivery. However, it will be understood that in some examples, IMD 3020 (and / or at least some other1618.294.1 1 1102 example IMDs described throughout various example of the present disclosure) may be sized and shaped for intravascular delivery and / or for delivery via an introducer or needle.

[0316] In some examples, as later shown in FIG. 19D, the first element 3022 may include the entire IMD 3020 such that IMD 3020 is lead-less. In some examples, such an arrangement may simplify and ease implantation of IMD 3020 as a percutaneously injectable element and / or percutaneously insertable element.

[0317] In some examples, as shown in FIG. 18A, the second element 3024 of IMD 3020 includes a lead body 3027, which may extend perpendicular to the longitudinal axis of a housing 3025 of the first element 3022, in some examples. In some instances, the entire second element 3024 may sometimes be referred to as a lead. Via this configuration, the lead body 3027 may be placed in the neck region 114 without making sharp turns (e.g., 90 degree turn) along a length of lead body 3027 relative to a longitudinal axis of a housing 3025 of the first element 3022. Accordingly, surgical implantation of the lead 3024 including lead body 3027 (and IMD 3020 generally) may be simplified and / or less stress may be applied to the lead body 3027. Moreover, this generally perpendicular configuration may enhance the ability to anchor the first and second elements 3022, 3024 relative to non-nerve tissues within the head-and-neck region (112, 114).

[0318] In some examples, such as shown in FIG. 18A, a conductive element 3030 (e.g., electrode portion) of the second element 3024 (e.g., lead) may be aligned and / or positioned for stimulation of a target tissue (e.g., infrahyoid-muscle (IHM)-innervating nerve and / or IHM). However, it will be understood that the first and second elements 3022, 3024 may be implanted in a wide variety of positions, orientations, etc. within the head-and-neck region (112, 1 14) to be placed in stimulating relation to a wide variety of nerves, nerve branches, muscles, and / or combinations thereof.

[0319] As one example implementation of the examples of at least FIGS. 1 A, 1 B, 2A, and 2B, the IMD 3020 of FIG. 18A may include a stimulation element (e.g., 56 in FIG. 2A; 1 17 of FIG. 2B) for delivering therapy and / or a sensing1618.294.1 1 1103 element (e.g., 24 of FIGS. 1A-1 B; 28 of FIG. 1 B, 54 of FIG. 2A; 128 of FIG. 2B) to sense information pertinent to the therapy. The IMD 3020 also may include a power element (e.g., rechargeable battery) and a communication element (e.g., coil). The first element 3022 may include an electrical connector to which an electrical connector 3026 at a proximal portion of the lead body 3027 is connected. The second element 3024 (e.g., lead) may include a wire (e.g., electrical conductor) extending through its length, and at least one electrode 3030 (e.g., stimulation electrode and / or sensing electrode) on a distal portion of the lead body 3027. In some examples, the second element 3024 may also include an antenna (e.g., coil antenna or RF antenna). The at least one electrode 3030 (e.g., cuff electrode, axial electrode, etc.) is electrically connected, via the at least one wire within lead body 3027 and the electrical connector 3026, to circuitry (e.g., stimulation circuitry, sensing circuitry), which resides within housing 3025 of first element 3022. In some examples, the second element 3024 may support independent addressability of a plurality of electrodes 3030 (e.g., 2, 3, 4, 5, 6, 7, 8, or more) that are electrically connected to circuitry of first element 3022 through a plurality of wires (e.g., extending through the lead body 3027) and the electrical connector 3026.

[0320] FIG. 18B is a diagram 3140 of a side sectional view (cross hatching omitted for illustrative clarity) of a head-and-neck region 3142 of a patient. In particular, an upper airway portion 3150 extends from the mouth 3144 to a neck portion 3155. In some examples, the upper airway portion 3150 may include a velum (soft palate) portion (or region) 3160, an oropharynx portion (or region) 3162, and an epiglottis-larynx portion (or region) 3164. The velum (soft palate) portion 3160 includes an area extending below sinus 3161 , and including the soft palate 3146 approximately to the point at which tip 3148 of the soft palate 3146 meets a portion of tongue 3147 at the back of the mouth 3144. The oropharynx portion 3162 extends approximately from the tip 3148 of the soft palate 3146 along the base 3152 of the tongue 3147 until reaching approximately the tip region of the epiglottis 3154. The epiglottis-larynx portion 3164 extends approximately from the tip of the epiglottis 3154 downwardly to a point above the esophagus 3157.1618.294.1 1 1104

[0321] In some examples, an IMD as described herein (e.g., 22A, 22B of FIGS. 1 A-1 B; 50 of FIG. 2A; 1 17 or 128 of FIG. 2B; 500a-500e of FIGS. 3A-3E; 1040a- 1040b of FIGS. 4B-4C; 1200a-1200g of FIGS. 5A-5G; 3020 of FIG. 18A; 3250, 3275, 3280, 3290 of FIGS. 19A-19D) may be implanted within the head-and-neck region 3142 of a patient. The IMD may include an acoustic sensor (e.g., 24 of FIGS. 1 A-1 B, 54 of FIG. 2A, 128 of FIG. 2B, 520 of FIGS. 3A-3E, 1024b of FIGS. 5A-5G, or 2002 of FIG. 10) to sense airway airflow within upper airway portion 3150. As further described below, the IMD including an acoustic sensor may be implanted at any suitable location near the upper airway portion 3150. In this way, acoustic energy may be transferred from the upper airway portion 3150 to the acoustic sensor through the tissue between the upper airway portion 3150 and the acoustic sensor.

[0322] In some examples, an IMD as described herein may be implanted relative to the mylohyoid muscle near the oropharynx portion 3162. For example, an IMD may be secured to the superficial side of the mylohyoid muscle using at least one suture. In some examples, an IMD as described herein may be implanted relative to the digastric muscle near the oropharynx portion 3162. For example, an IMD may be secured to the interior side of the anterior belly of the digastric muscle using at least one suture. In some examples, an IMD as described herein may be implanted relative to the hyoglossus muscle near the oropharynx portion 3162. For example, an IMD may be secured to the superficial side of the hyoglossus muscle using at least one suture.

[0323] In some examples, an IMD as described herein may be implanted between the mylohyoid muscle and the anterior belly of the digastric muscle or between the submandibular salivary gland and the hyoglossus muscle. In these examples, sutures may not be needed to secure the IMD relative to the mylohyoid, digastric, or hyoglossus muscles since the IMD may be passively fixated. In some examples, an IMD as described herein may be secured to the mandible using at least one bone screw. Each of the above described implant locations (e.g., mylohyoid muscle, digastric muscle, hyoglossus muscle, and mandible) are close to the hypoglossal nerve and / or may include anatomy that may be interacted with during surgical procedures when implanting an IMD within1618.294.1 1 1105 the head-and-neck region 3142 of a patient. In this way, the surgical procedures may be more efficient and lower the risk of trauma while implanting an IMD configured to sense airway airflow and / or apply stimulation to an upper airway patency-related tissue (e.g., hypoglossal nerve(s), genioglossus muscle, infrahyoid muscle (IHM)-innervating nerve(s), infrahyoid strap muscle(s) and / or other nerves, muscles).

[0324] In some examples, an IMD as described herein may be secured to the sternocleidomastoid muscle, sternothyroid muscle, or platysma muscle close to the internal superior laryngeal (iSL) nerves as a target location of electrical stimulation. In some examples, an IMD as described herein may be secured relative to the sternocleidomastoid muscle, sternothyroid muscle, omohyoid muscle, or platysma muscle close to at least some of the infrahyoid muscle (IHM)- innervating nerves (which may include the ansa cervicalis ansa cervicalis nerve loop and / or its branches) as a target location of electrical stimulation. In this way, an IMD may be secured close to the nerve target, which may reduce surgical complications and may reduce the length of a stimulation lead.

[0325] FIG. 18B further illustrates a relative location of the hyoid bone 3163 and thyroid cartilage 3165, as illustrated by dashed lines and with the arrows illustrating the direction of the movement of thyroid cartilage 3165, and optionally, the hyoid bone 3163, in response to electrical stimulation at a target location and applied to the IHM-innervating nerve and / or IHM, in accordance with some examples of the present disclosure.

[0326] The thyroid cartilage 3165 is connected to pharyngeal muscles connected to the pharyngeal walls (such as oropharynx walls) and pulling the thyroid cartilage 3165 down effectively causes the pharyngeal walls (e.g., oropharynx walls) to displace and / or redistribute tissue (e.g., at least adipose tissue) in at least the oropharynx portion 3162 to reduce extraluminal tissue pressure, which may increase and / or maintain patency of at least the oropharynx portion of the upper airway 3150. For example, the thyroid cartilage 3165 may be connected to the inferior pharyngeal constrictor muscle, the stylopharyngeus muscle, and the thyrohyoid muscle.1618.294.1 1 1106

[0327] As shown, the hyoid bone 3163 relates to the base 3152 of the tongue 3147 (e.g., genioglossus muscle). As noted above, and without being bound by theory, it is believed that pulling the hyoid bone 3163 inferiorly, as shown by the arrow, may pull on the middle pharyngeal constrictor muscle which effectively increases upper airway patency.

[0328] In some examples, moving the hyoid bone 3163 inferiorly may elongate (e.g., stretch, tug) at least one pharyngeal constrictor muscle, such as the middle constrictor muscle(s). For example, the middle pharyngeal constrictor muscle may attach to the hyoid bone 3163 and depression of the hyoid bone 3163 may cause the middle pharyngeal constrictor muscle to elongate (e.g., stretch) and increase airway patency in at least the oropharynx portion 3162. In some examples, elongating (e.g., stretching) the at least one pharyngeal constrictor muscle may stiffen the upper airway (e.g., increases pharyngeal muscle tone) and reduce collapsibility of the upper airway. In some examples, the hyoid bone 3163 may not move in a purely superior-inferior orientation. As such, as used herein, the hyoid bone 3163 being moved inferiorly may include moving generally inferiorly. For example, the patency of upper airway 3150 may increase wall stiffness (at least partially defined by pharyngeal muscles) become stiffened / stretched and / or to move in an orientation (e.g., superior-inferior, anterior-posterior, and / or medial-lateral), with such stiffening and / or movement acting to increase patency of the oropharynx portion.

[0329] In some examples, and as described above, applying stimulation to the IHM-innervating nerve and / or IHM at a target location may cause a physiological response due to activation (e.g., contraction) of at least one IHM (e.g., infrahyoid strap muscle). The physiological response may include at least one of the thyroid cartilage 3165 moving inferiorly and the hyoid bone 3163 moving inferiorly, and which causes a physiological effect for treating SDB that occurs remotely from the stimulation and / or remotely from the physiological response, e.g., movement of the thyroid cartilage 3165 and / or thyroid cartilage 3165 and hyoid bone 3163 as described above. In some examples, the physiological effect comprises opening at least the oropharynx portion and / or stiffening of a pharyngeal wall of the patient (which at least partially forms a lumen of the oropharynx portion),1618.294.1 1 1107 which occurs remotely from the physiological response to the stimulation of moving at least the thyroid cartilage inferiorly.

[0330] FIG. 19A illustrates an example device 3250 including a first element 3222 and second elements (e.g., leads) 3274A, 3274B. In some examples, device 3250 includes at least some of substantially the same features as device 3020 of FIG. 18A, except that device 3250 includes two second elements 3274A, 3274B (each including a lead body 3227A, 3227B, respectively) and associated respective conductive elements 3230A, 3230B) instead of just one second element 3024 (and lead body 3027) and one conductive element 3030. Each second element 3274A, 3274B may include a first portion (e.g., proximal portion) including an electrical connector 3226, at least one first wire, and at least one first conductive element 3230A, 3230B on an opposite second portion (e.g., first distal portion) of the second element 3274A, 3274B. As shown in FIG. 19A, in some examples the respective second elements 3274A, 3274B extend outward from opposite sides of the housing 3225 of first element 3222. The second elements 3274A, 3274B may include at least some of substantially the same features as the second element 3024 in FIG. 18A such as (but not limited to) at least one wire extending through a length of the lead body 3227A, 3227B and the at least one conductive element 3230A, 3230b, respectively.

[0331] In some examples, using at least two second elements 3274A, 3274B (and two corresponding at least one conductive elements 3230A, 3230B) extending from opposite sides of first element 3222 enables reaching target tissues in different directions (e.g., opposite) from housing 3225 of first element 3222.

[0332] For instance, in some examples, the device 3250 may be implanted in a lower portion of neck region 1 14, such as superior to the clavicle or the manubrium, at which one lead (e.g., 3274A) may extend to be in stimulating relation to an IHM and / or an IHM-innervating nerve distal to the ansa cervicalis nerve loop. The other lead (e.g., 3274B) may extend to be in operational relation to other target tissues related to treating sleep disordered breathing (SDB) including obstructive sleep apnea. However, in some examples, the other lead 3274B is positioned to be in sensing relation to other target tissues (e.g.,1618.294.1 1 1108 respiratory tissues) to facilitate stimulation therapy for treating sleep disordered breathing (e.g., obstructive sleep apnea). In some examples, the other target tissues may include a phrenic nerve and / or diaphragm muscle to sense respiration and / or other physiologic parameters suitable to facilitate stimulation therapy for treating sleep disordered breathing (e.g., obstructive sleep apnea).

[0333] In some examples, the sensed respiration may be used for providing closed loop stimulation in which a timing of the stimulation is based on the sensed respiratory information. However, in some examples, the stimulation may be an open loop stimulation (e.g., in which stimulation timing is not based on sensed respiration) and the sensed respiratory information may be used for other purposes to facilitate SDB treatment. Of course, the two lead arrangement is not limited to use with the above-mentioned stimulation and sensing targets.

[0334] While the leads 3274A, 3274B in FIG. 19A are illustrated as extending from opposite ends of the electrical connector 3226, in some examples, the leads may extend from the same sidewall of electrical connector 3226, from adjacent sidewalls (e.g., perpendicular sidewalls) of electrical connector 3226, and / or from the end wall of the electrical connector 3226. This principle similarly applies to the example of FIG. 19B described below.

[0335] In addition, while two leads are illustrated in FIG. 19A and FIG. 19B as extending from the first element 3222, in some examples, more than two leads 3274A, 3274B may extend from the first element 3222.

[0336] FIG. 19B illustrates an example device 3275 including at least some of substantially the same features as example device 3250 (FIG. 19A) except that the respective second elements 3274A, 3274B of device 3275 are connected to opposite ends 3223A, 3223B of the housing 3225 of first element 3222 via respective electrical connectors 3226A, 3226B which may provide greater flexibility in positioning the respective leads 3274A, 3274B relative to target tissues which are located in opposite directions from a location at which the housing 3225 of the first element 3222 may be anchored within the patient’s body.

[0337] FIG. 19C illustrates an example device 3280 including a first element 3222 and a second element 3284. In some examples, device 3280 includes at least some of substantially the same features as device 3020 of FIG. 18A (or1618.294.1 1 1109FIGS. 19A and 19B), except that instead of the lead body 3227 of second element 3284 (lead) extending perpendicular (e.g., 90°) to the longitudinal axis 3253 of the housing 3225 of the first element 3222, in device 3280 the lead body 3227 extends at an angle theta (8) relative to the longitudinal axis 3253. In some examples, the angle theta (0) may be within a range between 0° and 90° (e.g., 10°, 20°, 30°, 45°, 60°, 80°). While device 3280 illustrated in FIG. 19C includes one lead 3284, in some examples, device 3280 may include more than one lead (as in FIGS. 19A or 19B) and at least one of the multiple leads may be connected at a non-perpendicular angle relative to the long axis 3253 of the housing 3225 of the first element 3222. In some such examples, the angle theta (0) for each lead may be the same or different and each lead may extend from the same sidewall of electrical connector 3226, from adjacent sidewalls (e.g., perpendicular sidewalls) of electrical connector 3226, and / or from the end wall of the electrical connector 3226.

[0338] FIG. 19D is a diagram illustrating an example IMD 3290, which may include at least some of substantially the same features as the examples of FIGS. 18A and 19A-19C, except including a leadless implementation omitting a lead (e.g., second element 3024, 3274A, 3274B, 3284). As shown in FIG. 19D, the IMD 3290 includes a first element 3292 (like 3222 in FIGS. 19A-19C) and an array 3294 of electrodes 3295, which may be used for applying electrical stimulation to target tissue and / or for sensing physiologic phenomenon. It will be understood that the electrodes 3295 may be located on a single surface or multiple different surfaces (e.g., ends, sides, top, bottom, etc.) of a housing 3225 of the first element 3292, and that array 3294 may include a greater or fewer number of electrodes 3295. In some such examples, the first element 3292 also may include additional or other sensing elements (e.g., acoustic sensor) contained within a housing 3225 of the first element 3292.

[0339] It will be further understood that similar example arrangements of first and second leads, electrodes, housings, etc. may be implemented relative to target tissue (e.g., a single target tissue or multiple, different target tissues) solely for sensing, i.e., without applying stimulation.1618.294.1 1 11 10

[0340] It will be further understood that the present disclosure is not limited to the examples of FIGS. 18A and 19A-19D and that a wide variety of differently configured first elements (e.g., 3022, 3222, 3292) and second elements (e.g., 3024, 3227A, 3227B, 3284) may be employed to achieve stimulation and / or sensing of target tissue.

[0341] FIG. 20A is a diagram 3500 schematically representing an implantable medical device (IMD) 3520 implanted relative to a nerve 3512 (e.g., hypoglossal nerve(s), genioglossus muscle, infrahyoid muscle (IHM)-innervating nerve(s), infrahyoid strap muscle(s) and / or other nerves, muscles) of array 3510 of nerves within a subcutaneous, extravascular environment 3502, according to one example of the present disclosure. In some examples, the IMD 3520 comprises at least some of substantially the same features and attributes as, or an example implementation of, the devices described in association with FIGS. 1 A-19D. As shown in FIG. 20A, IMD 3520 includes a housing 3522 extending between opposite ends 3523A, 3523B with housing 3522 having a generally electrically non-conductive outer surface 3524. Housing 3522 may contain a sensing element (e.g., sensor 24 of FIGS. 1 A-1 B, 28 of FIG. 1 B, 54 of FIG. 2A, 520 of FIGS. 3A-3D, 1024b of FIGS. 5A-5G, 2002 of FIG. 10), stimulation elements (e.g., 56 of FIG. 2A, 117 of FIG. 2B), at least a portion of a control portion (e.g., 190 of FIG. 2C, 4500 of FIG. 28A), or other elements (e.g., 58 of FIG. 2A) and may sometimes be referred to as defining or including a body of the IMD 3520.

[0342] In some examples, an array 3530 of three ring electrodes 3532 are arranged in an axially spaced apart manner along a length of the housing 3522. In some examples, a fewer number or greater number of ring electrodes 3532 may be used. In some examples, the electrodes 3532 may be implemented as partial ring electrodes 3532, and thus not extend about a complete circumference of the housing 3522 provided that at least a portion of the respective partial ring electrodes 3522 can establish contact against the nerve 3512. In some examples, housing 3522 may be referred to as a housing comprising an integrated cuff electrode.1618.294.1 1 11 1 1

[0343] In some examples, the non-conductive area of the housing 3522 defines a first portion. Each respective electrode 3532 defines one of three second portions, each of which is surrounded by the non-conductive first portion.

[0344] As further shown in FIG. 20B, a pair of flanges 3540A, 3540B extend outwardly from the housing 3522 of IMD 3520 and are biased to encircle and releasably engage nerve 3512 to mechanically (and directly) secure the housing 3522 relative to, and in contact against, nerve 3512 such that electrodes 3532 become directly electrically coupled relative to the nerve 3512 (e.g., nerve bundle). In some instances, the flanges 3540A, 3540B may sometimes be referred to as wrapping around or about the nerve 3512. Accordingly, in at least some examples the flanges 3540A, 3540B may sometimes be referred to as an anchoring element(s). In some examples, each flange 3540A, 3540B may sometimes be referred to as an at least partially flexible element. In some examples, the flanges 3540A, 3540B are made from a polymer material, such as but not limited to a polyurethane material or one of several biocompatible materials, alone or in combination. In at least some examples, this arrangement enables application of stimulation therapy for treating sleep disordered breathing (SDB).

[0345] In some examples, the housing 3522 of IMD 3520 has a length at least two times a diameter or width of the housing 3522 with the elongate configuration enhancing stability of the housing 3522 when secured against nerve 3512. In some examples, the body 3522 has a volume less than about 3 cubic centimeters.

[0346] With general reference to at least FIGS. 20A-20B, in some examples, housing 3522 of IMD 3520 has opposite ends 3523A, 3523B formed of electrically non-conductive material, and therefore in some examples, no application of electrical stimulation occurs through the ends 3523A, 3523B of housing 3522.

[0347] With further general reference to at least FIGS. 20A-20B, in some examples, the housing 3522 of IMD 3520 has opposite ends 3523A, 3523B devoid of fixation elements. In other words, in such examples the housing 3522 lacks any mechanical element (such as but not limited to a screw) protruding axially from end 3523A, 3523B of housing 3522 or otherwise configured at end(s)1618.294.1 1 11 123523A, 3523B of housing 3522 for fixating solely the end of the housing 3522 relative to a bony structure.

[0348] With further general reference to FIGS. 20A-20B, in some examples, housing 3522 of IMD 3520 is not invasively fixed relative to the tissue (e.g., nerve 3512) to which electrical stimulation is to be applied and / or is located remotely from the muscle targeted for contraction. Accordingly, in some instances, the housing 3522 may sometimes be referred to as being non-nerve-invasively secured or being non-invasively secured relative to the nerve to be electrically stimulated.

[0349] In sharp contrast to at least some examples of the present disclosure, some commercially available implantable stimulators are attached to the to-be- stimulated tissue via a screw which is invasively implanted within the to-be- stimulated tissue, where a to-be-stimulated tissue is a muscle targeted for contraction. In such examples, the fixation mechanism penetrates the tissue to which electrical stimulation is applied.

[0350] In sharp contrast to some commercially available injectable stimulators, in some examples of the present disclosure, the housing 3522 of IMD 3520 (of at least some examples of the present disclosure) is secured relative to the tissue (e.g., nerve) to be stimulated, thereby minimizing and / or avoiding migration while enhancing patient comfort.

[0351] With further general reference to at least FIGS. 20A-20B, in some examples, a contact interface between housing 3522 of the IMD 3520 and nerve 3512 does not define a primary securing interface. Instead, the flanges 3540A, 3540B extending outward from the housing 3522 primarily define a securing interface relative to the nerve with the flanges 3540A, 3540B omitting any electrically conductive elements. In some examples, suture loops may be installed to further secure the flanges 3540A, 3540B in their closed position about the nerve 3512.

[0352] However, in some examples, a differently shaped housing may be substituted for the generally cylindrical shaped housing 3522. For instance, housing 3522 may be replaced with a housing having an external surface with an arcuate nerve-engaging portion. With such an arrangement, the arcuate shape1618.294.1 1 11 13 of the nerve-engaging portion enhances maintaining a stable, secure position of the housing relative to the nerve. In this way, the housing complements the action of flanges 3540A, 3540B in securing the IMD 3520 relative to nerve 3512.

[0353] FIG. 21 A is a diagram 3600 schematically representing an IMD 3620 implanted relative to a nerve 3512 (e.g., hypoglossal nerve(s), genioglossus muscle, infrahyoid muscle (IHM)-innervating nerve(s), infrahyoid strap muscle(s) and / or other nerves, muscles) of array 3510 of nerves within a subcutaneous, extravascular environment 3502, according to one example of the present disclosure. In some examples, the IMD 3620 comprises at least some of substantially the same features and attributes as the previously described devices in association with FIGS. 1 A-20B. As shown in FIG. 21 A, IMD 3620 includes a housing 3622 extending between opposite ends 3623A, 3623B with housing 3622 having a generally electrically non-conductive outer surface 3624. In some examples, no electrically conductive elements (such as ring electrodes 3532 in FIG. 20A) are present on external surface 3624. However, in some examples, the entire external surface 3624 or substantially the entire external surface 3624 can be electrically conductive and serve as a single electrode.

[0354] As shown in both FIGS. 21 A-21 B, flanges 3640A, 3640B extend outwardly from the housing 3622 of IMD 3620 and are biased to encircle and releasably engage nerve 3512 to mechanically (and directly) secure the housing 3622 relative to, and in contact against, nerve 3512, such that electrodes 3644 on the flange 3640B become directly electrically coupled relative to the nerve 3512. In one aspect, an array 3642 of electrodes 3644 are spaced apart from each other and arranged in a radial pattern along the length of the flanges 3640A, 3640B to at least partially surround or encircle the circumference of the nerve 3512 for applying a stimulation therapy to treat sleep disordered breathing (SDB). In some examples, housing 3622 may be referred to as a housing comprising an integrated cuff electrode.

[0355] It will be understood that housing 3622 contains stimulation circuitry (e.g., 56 of FIG. 2A, 117 of FIG. 2B) and that an electrical connection extends from the stimulation circuitry (within housing 3622) through the flange 3640B to the respective electrodes 3644 and / or through flange 3640A if some electrodes1618.294.1 1 11 143644 are located on flange 3640A. In some examples, each electrode 3644 may be independently controlled in applying a stimulation signal to the nerve 3512. As noted elsewhere, in at least some examples, the stimulator is encapsulated within non-conductive material within the housing 3622.

[0356] In one aspect, the housing 3622 of IMD 3620 is held directly against nerve 3512 (which is to be stimulated) even though no stimulation is applied via the external surface 3624 of the housing 3622 of the IMD 3620. As previously described in association with at least FIGS. 20A-20B, the housing 3622 may be substituted for a housing having a concave, arcuate cross-sectional shape which betters conforms or complements the arcuate outer surface of the nerve 3512.

[0357] With general reference to at least some examples of FIGS. 21 A-21 B, no electrically conductive element extends axially beyond either end 3623A, 3623B of the housing 3622. In one aspect, this arrangement gives the IMD 3620 (including electrodes 3644) a small footprint. In one aspect, this small footprint may enable a small electromagnetic footprint, at least in the sense that less heating may result from the relatively short lead loop length, thereby mitigating potential nerve damage that might otherwise occur in the presence of relatively larger lead loop lengths. In addition, this arrangement may minimize and / or avoid a patient-to-patient variability that commonly occurs in routing a lead in pectorally implanted therapy devices, in which such patient-to-patient variability may produce uncertainty in magnetic resonance (MR)-conditional testing. Accordingly, at least some examples of the present disclosure, such as at least the arrangement in FIGS. 21 A-21 B, may enable an IMD to receive an MR- conditional rating, in which the patient may be eligible for at least some types of MR scanning.

[0358] With general reference to at least some examples of FIGS. 20A-20B and 21 A-21 B, in some implementations, the flanges (3540A, 3540B in FIGS. 20A- 20B; 3640A, 3640B in FIGS. 21 A-21 B) do not extend a distance from the housing 3522, 3622 more than a length of housing 3522, 3622.

[0359] With general reference to at least some examples of FIGS. 21 A-21 B, in its closed position, the electrically conductive portions of the flange 3640B do not1618.294.1 1 11 15 extend a distance from the housing 3622 more than a diameter of nerve 3512 or more than a diameter of the housing 3622.

[0360] FIGS. 22A-27E disclose various examples of anchor structures, anchor portions, anchor elements, etc. which may be implemented on or with various examples of stimulation portions, stimulation elements, leads, lead segments, housings (e.g., housing of an implantable medical device, housing of an implantable pulse generator, etc.), etc. In some examples, the examples of FIGS. 22A-27E may comprise at least some of substantially the same features as, and / or an example implementation of, at least some of the features of the examples of FIGS. 1 A-21 B and 28A-36B.

[0361] FIG. 22A is a diagram schematically representing an example stimulation portion 3770. In some examples, the stimulation portion 3770 may comprise at least some of substantially the same features and attributes as, and / or an example implementation of, the example leads and / or stimulation electrode arrangements described in association with at least FIGS. 1A-21 B. In some examples, the stimulation portion 3770 may be used for both stimulation and sensing, while in some examples portion 3770 may comprise solely a sensing portion for sensing.

[0362] In some examples, the anchor structures, anchor portions, anchor elements, etc. described in association with at least FIGS. 22A-22C and 24A-24F may be implemented according to at least some of substantially the same features and attributes as anchor structures 3820, 3824, and 3828 later described in association with at least FIGS. 23A-23C and / or anchor structures 4100 and 4150 later described in association with FIGS. 25A-25B.

[0363] As shown in FIG. 22A, in some examples the stimulation portion 3770 comprises an anchor structure 3780 which extends along and around the entire or substantially the entire outer surface 3774 of the stimulation portion 3770 with at least some stimulation electrode arrangements 3776 interposed between segments of the anchor structure 3780 of anchor elements 3782. Each stimulation electrode arrangement 3776 may comprise one or more contact electrodes 3777 (i.e., stimulation electrodes). Among other aspects, the anchor structure 3780 stands in contrast to some leads which merely include a limited1618.294.1 1 11 16 number of discrete anchor elements. Instead, the anchor structure 3780 provides a continuous or substantially continuous coverage of anchor elements on outer surface 3774 of the stimulation portion 3770. In some examples, the substantially continuous coverage may comprise covering at least about 50 percent of the total surface area of the outer surface 3774 of the stimulation portion 3770. In some examples, the substantially continuous coverage may comprise at least about 60 percent, at least about 65 percent, at least about 70 percent, at least about 75 percent, at least about 80 percent, at least about 85 percent, or at least about 90 percent.

[0364] In some examples, the continuous or substantially continuous coverage of outer surface 3774 with anchor elements 3782 may sometimes be referred to as a region of indefinite number of anchor elements 3782. In some such examples, the continuous or substantially continuous coverage of outer surface 3774 with anchor elements 3782 may sometimes be referred to as an anchor blanket, anchor layer, or anchor sheet.

[0365] Among other aspects, the anchor structure 3780 of stimulation portion 3770 may facilitate robust fixation of the lead segments 3772A, 3772B, 3772C, 3772D, etc. and / or stimulation electrode arrangements 3776 relative to surrounding tissues. At the same time, the relatively low profile of the anchor structure 3780 permits at least lateral advancement and maneuvering of the lead segments and / or the stimulation electrode arrangements of stimulation portion 3770 into target implant positions (and orientations) as described in association with at least FIGS. 1 A-2B, 18A, and 19A-19C.

[0366] FIG. 22B is a diagram 3790 including a sectional view schematically representing one example implementation of the stimulation portion 3770 of FIG. 22A. As shown in FIG. 22B, the example stimulation portion 3770 may comprise an example implementation of, and / or at least some of substantially the same features of the leads, stimulation portions, etc. as previously described in association with at least FIGS. 1 A-21 B.

[0367] As shown in FIG. 22B, the example stimulation portion 3770 comprises an anchor structure 3780, which includes a plurality of anchor elements 3782 which are formed on, or defined as part of, the outer surface 3774 of an outer wall1618.294.1 1 11 173719 of one of the lead segments (e.g., 3772A, 3772B, etc.), which define at least part of the stimulation portion 3770 (FIG. 22A). In some examples, the anchor structure 3780 defines a general pattern covering the entire or substantially the entire outer surface 3774 of the lead segment(s) (e.g., 3772A, 3772B, etc.) of the stimulation portion 3770. In some such examples, the anchor structure 3780 may comprise (or sometimes be referred to as) as anchor layer or anchor sheet.

[0368] FIG. 22C is a diagram 3792 including a sectional view schematically representing one example implementation of the stimulation portion 3770 of FIG. 22A (and sectional view of FIG. 22B), while including a contact electrode 3794 in electrical connection with one of the electrical conductors 3717 extending within an interior 3779 of one of the lead segments (e.g., 3772A, 3772B, etc.) of stimulation portion 3770. As shown in FIG. 22C, in some examples, the anchor elements 3782 may at least partially surround the contact electrode 3794.

[0369] FIG. 23A is a diagram 3800 including a side view schematically representing an example stimulation portion 3810. In some examples, the example stimulation element 3810 comprises at least some of substantially the same features and attributes as various stimulation portions, stimulation elements, etc. described in association with the figures of the present disclosure, while also comprising an anchor structure 3820 instead of another anchoring arrangement such as tines in FIGS. 25C-25H or other types of anchor elements. In some examples, the stimulation portion 3810 may comprise a distal portion of a lead body which extends proximally from the proximal end 3818 of the stimulation portion 3810. It will be understood that anchor structure 3820 (e.g., plurality of anchor elements forming a matrix, pad, sheet, layer, etc.) may be substituted for the separate tines in the example arrangement of FIGS. 25C-25H and / or for other anchor structures in various examples throughout the present disclosure.

[0370] As shown in FIG. 23A, in some examples the anchor structure 3820 comprises a plurality of anchor elements 3824 which protrude from the sides 3811 of the body 3813 of the stimulation portion 3810. In some examples, the anchor elements 3824 may be grouped into different arrays 3822A, 3822B while in some1618.294.1 1 11 18 examples, the anchor structure 3820 may comprise a single cluster of anchor elements 3824.

[0371] It will be understood that in some examples, the elements 3824 may extend about an entire periphery (e.g., circumference of body 3813).

[0372] As shown in FIG. 23A, the anchor structure 3820 is positioned distal to the electrode array 3814, being between the electrode array 3814 and the distal end 3819 of the body 3813 of the stimulation portion 3810. In this configuration, the position of the anchor structure 3820 on just one end (e.g., the distal end) of the electrode array 3814 may prevent or minimize “lead elongation”, i.e., elongation of the lead body 3813 which may potentially be caused by muscle movement when anchor elements (e.g., tines) are present on opposite ends of the electrode array 3814.

[0373] In some examples, the elements 3824 may comprise a filament (e.g., fine thread) which is flexible and resilient, and biased to extend outward from the side 3811 of body 3813. The filament may be formed of a polymer material, such as but not limited to, nylon, propylene, silk, polyester, trimethylene carbonate, and the like. In some examples, such filaments may be resorbable or may be non- resorbable.

[0374] In some examples, each element 3824 may comprise a diameter (or greatest cross-sectional dimension) of about 0.05 to about 0.60 millimeters. In some examples, each element 3824 may comprise a length of about 0.2 to about 2 millimeters. In some examples, each element 3824 may comprise a length of about 0.5 percent to about 50 percent of a diameter of the lead body 3813 in the region of the electrode array 3814 and / or at distal end 3819. In some examples, at least some or all of the anchor elements 3824 may have generally the same shape, size, orientation, material, configuration, etc., such that the anchor elements 3824 may sometimes be referred to as being generally homogeneous anchor elements, i.e., being generally the same as each other.

[0375] However, in some examples, the anchor structure 3820 may be embodied as a matrix (e.g., grouped arrangement) of heterogeneous elements via filaments having pseudo-random sizes, shapes, orientations and / or positions exhibiting more variation than a plurality of identical or substantially similar1618.294.1 1 11 19 discrete elements (e.g., 3827 in FIG. 23B), which may be visually recognizable. Meanwhile, in some examples, all of the various features of the matrix of heterogeneous elements may not be readily visually recognizable. Among other features, this heterogeneous matrix may enable fixation in both (e.g., opposite) orientations (along the length of the stimulation element, stimulation lead, etc.) and ease deliverability of the lead and / or lead portions. At least some example implementations of anchor structures 4100 and 4150 comprising a matrix of heterogeneous elements are described later in association with at least FIGS. 25A-25B. In some examples, the heterogeneous elements may sometimes be referred to as heterogeneous fixation elements.

[0376] In some examples, the anchor structure 3820 may comprise a plurality of well-defined, discrete elements but with at least some of the discrete elements comprising a size, shape, orientation, and / or position different from a size, shape, orientation, and / or position of other respective discrete elements of the anchor structure 3820.

[0377] In some examples, the term matrix connotes a grouped arrangement of the fixation elements (e.g., anchor elements) in which the fixation elements are (structurally) independent from each other even though some of the fixation elements may at least partially contact each other in (at least) some instances. Stated differently, in some examples the grouped fixation elements do not interconnect with each other in a latticework or mesh format. In some examples, the fixation elements may be homogeneous relative to each other or in some examples, the fixation elements may be heterogeneous relative to each other. In some examples, the fixation elements may be oriented in near parallel planes, and in other examples, the fixation elements could be in orientations with intersecting planes. In some examples, the relative orientation of the fixation elements can be random.

[0378] In some examples, the anchor structure 3820 may enhance some example methods of implantation of a stimulation device at least because the respective elements 3824 exhibit a low profile relative to an outer diameter of the body 3813 of the stimulation portion 3810, such that the stimulation portion 3810 (FIG. 23A-23C) can be delivered via a hollow insertion needle without a sleeve or1618.294.1 1 1120 similar elements (to temporarily collapse the elements 3824 during insertion) while the elements 3824 are still large enough to robustly secure the stimulation portion 3810 once free from the insertion needle.

[0379] As further shown in the greatly enlarged side view of just one element 3824 in FIG. 23B, in some examples, at least some (or all) of the elements 3824 may comprise protrusions 3827 on their surfaces, which in some examples may comprise barbs, hooks, or other sharp tipped structures. In some examples, the protrusions 3827 may be present on just a portion of the element 3824, such as but not limited to a distal portion 3829 of the element 3824. However, in some examples, the protrusions 3827 may be present on the entire or substantially the entire surface of the element 3824. In yet other examples, groups of protrusions 3827 may be positioned in spaced apart clusters, which are spaced apart from each other along and around the surface of the element 3824.

[0380] It will be further understood that the protrusions 3827 are not strictly limited to structures having a sharp-tip or hook but may comprise structures comprising a rounded edge while including a sticky surface coating or formed as a non-sharp tipped member which can securely engage a surrounding non-nerve tissue in close proximity to a target stimulation site.

[0381] FIG. 23C is a diagram including a side view schematically representing an example protrusion 3828. In some examples, the protrusion 3828 may comprise at least some of substantially the same features and attributes as protrusion 3827 described in association with at least FIG. 23B and / or may comprise one example implementation of protrusion 3827. As shown in FIG. 23C, in some examples protrusion 3828 may comprise a main element 3823 for protruding outward (e.g., biased to extend outwardly at an angle) from an outer surface of a lead to function as part of an anchor structure, with protrusion 3828 including a first secondary element 3825A extending at an angle relative to the main element 3823. The combination of the first secondary element 3825A and the main element 3823 may sometimes be referred to as a barb at least to the extent that the respective main and secondary elements 3823, 3825A form a sharp point with the secondary element 3825A having an orientation which is at least partly opposite of the general orientation of the main element 3823. In some1618.294.1 1 1121 examples, the protrusion 3828 may further comprise additional secondary elements 3825B spaced apart from each other along a length of the main element 3823 and also extending outward at an angle relative to the main element 3823. In some examples, each secondary element 3825B also may comprise a barb, e.g., a further protrusion extending at an angle relative to the secondary element.

[0382] With regard to the example stimulation portion 3810 in FIGS. 23A-23C, it will be understood that in some examples the anchor structure 3820 may be located solely proximally of the electrode array 3814, such that no similar anchor structure 3820 is located distal to the electrode array 3814.

[0383] However, in some examples, a first anchor structure 3820 may be located distal to the electrode array 3814 as shown in FIG. 23A and a second anchor structure, similar to anchor structure 3820, may be located proximal to the electrode array 3814 so that at least some anchor structure or elements are present on opposite ends (e.g., sides when seen in the view of FIG. 23A) of the electrode array 3814. In some examples, elements 3824 of an anchor structure 3820 may be located between adjacent electrodes 3816 of the electrode array 3814.

[0384] Each of FIGS. 24A-24C is a diagram including a side view schematically representing an example stimulation portion (or portion of a stimulation lead body) including an anchor structure formed on, or defined at least partially by, an outer surface of the stimulation portion (or of the stimulation lead body). As previously noted elsewhere, in some examples the example stimulation portion may be used for stimulation and sensing while in some examples, the portion may be used solely for sensing. In some examples, each example anchor structure (391 1 in FIG. 24A; 3921 in FIG. 24B; 3942 in FIG. 24C) may comprise at least some of substantially the same features and attributes of an anchor structure (and its associated stimulation portion or portion of a stimulation lead body) of the examples described in association with at least FIGS. 22A-23C. It will be further understood that such example anchor structures also may be incorporated into other example devices of the present disclosure, such as on an outer surface of at least a portion of a stimulation lead body, stimulation portion, other type of anchor element, etc.1618.294.1 1 1122

[0385] As shown in the diagram 3900 of FIG. 24A, in some examples anchor structure 391 1 may comprise a plurality of rows 3912 of anchor elements 3914 formed on (or defined as at least part of) an outer surface 3974 of a stimulation portion (or portion of a lead body) with spacing 3918 (e.g., absence of anchor elements 3914) interposed between adjacent rows 3912 of the anchor structure 391 1 . In this arrangement, the rows 3912 are circumferentially spaced apart. In one aspect, each row 3912 is aligned with (e.g., generally parallel to) a longitudinal axis (represented by line A) of the stimulation portion 3971 (or lead body). In some such examples, the size (e.g., width W1 1 ) of spacing 3918 and size (e.g., width W12) of the rows 3912 may be selected to implement a desired percentage of coverage of the surface area on the outer surface 3974 of the stimulation portion 3771. However, even with the spacing 3918, in some examples the anchor structure 391 1 may sometimes be referred to as extending or covering the entire (or substantially the entire) length of the stimulation portion (or portion of lead body). It will be further understood that even with the inclusion of some minor interruptions (e.g., spaces) along a length of a row 3912 of the anchor structure 3911 , the row 3912 (and anchor structure) may still be considered to extend the entire length (or substantially the entire length) of the stimulation portion (or portion of stimulation lead body). For instance, one such non-limiting example of an interruption may comprise the presence of a stimulation electrode arrangement (e.g., array of stimulation electrodes) which is located along the length of the rows(s) 3912 of the anchor structure 3911 .

[0386] With regard to the examples of at least FIGS. 24A-24D, in some examples a plurality of anchor elements (which may be organized into strips, pads, etc. in some examples) provide substantially continuous coverage (e.g., occupy a surface area) on an outer surface of at least one of a lead body, stimulation portion, or a stimulation element. In some such examples, the substantially continuous coverage comprises at least about 25 percent coverage, at least about 30 percent coverage, at least about 35 percent coverage, at least about 40 percent coverage, at least about 45 percent coverage, at least about 50 percent coverage, at least about 60 percent coverage, at least about 65 percent coverage, at least about 70 percent coverage, at least about 75 percent coverage,1618.294.1 1 1123 at least about 80 percent coverage, at least about 85 percent coverage, at least about 90 percent coverage, or at least about 95 percent coverage of the outer surface of at least one of a lead body, a stimulation portion (including distal lead segments and / or a stimulation element), or a stimulation element. It will be further understood that these examples of substantially continuous coverage may be applied to examples of the present disclosure regarding a plurality of anchor elements other than FIGS. 24A-24D.

[0387] With regard to the example of at least FIG. 24A in which rows 3912 extend longitudinally along the length of a lead body, a stimulation portion, and / or a stimulation element, the rows 3912 are spaced apart from each other circumferentially, wherein spacing between adjacent rows 3912 comprises an arc length of about 5 to about 10 degrees, of about 10 to about 20 degrees, of about 20 to about 30 degrees, of about 30 to about 40 degrees, of about 40 to about 50 degrees, of about 50 to about 60 degrees, of about 60 to about 70 degrees, of about 70 to about 80 degrees, of about 80 to about 90 degrees, or of about 90 to about 120 degrees.

[0388] As shown in the diagram 3920 of FIG. 24B, example anchor structure 3921 may comprise at least some of substantially the same features and attributes of the anchor structure 3911 of FIG. 24A, except with the anchor elements 3914 arranged in a helical pattern of strips 3923A extending about the outer surface 3974 with spacing 3928 (e.g., absence of anchor elements 3914) interposed between adjacent strips 3923A. The dashed lines 3923B represent anchor strips on a backside of the stimulation portion not visible in the view of FIG. 24B, with strips 3923B being in general continuity with strips 3923A, in some examples. Among other aspects, the helically-patterned anchor structure 3921 may provide a desirable combination of sufficient anchorability in both the lateral and longitudinal orientations, while also permitting enough slidability in both the lateral and longitudinal orientations to facilitate implementing desired positioning of the stimulation elements of a stimulation portion at implant locations of target tissues. The helically-patterned anchor structure 3921 may sometimes be referred to as a spiral pattern. In some examples, spacing between adjacent turns about the outer surface 3974 may comprise at least some of substantially1618.294.1 1 1124 the same features regarding coverage and / or spacing as described in association with at least FIGS. 24A and 24C.

[0389] As shown in the diagram 3940 of FIG. 24C, example anchor structure3942 may comprise at least some of substantially the same features and attributes of the anchor structure 391 1 of FIG. 24A, except with the anchor elements 3914 on outer surface 3974 arranged in rows 3943 aligned perpendicular to the longitudinal axis (A) of the stimulation portion (or portion of lead body) with spacing 3948 (e.g., absence of anchor elements 3914) interposed between adjacent rows 3943 of anchor elements 3914. In some examples, the particular anchor structure may enhance longitudinal slidability while resisting lateral slidability, particularly after implantation.

[0390] In some such examples associated with FIG. 24C, the rows 3943 extend circumferentially with each row 3943 extending transverse to a longitudinal axis of a lead (and / or stimulation element), at least in the region in which the rows3943 are located, with the rows 3943 being spaced apart from each other longitudinally. In some such examples, the spacing (W14) between adjacent rows 3943 comprises at least one multiple, at least two multiples, or at least three multiples of a width (W13) of each row 3943.

[0391] FIG. 24D is a diagram 3950 including a sectional view schematically representing an example anchor structure 3952 for a stimulation electrode arrangement 3951 . As shown in FIG. 24D, example anchor structure 3952 may comprise at least some of substantially the same features and attributes of (and / or an example implementation of) the anchor structures as described in association with at least FIGS. 22A-24C and 25A-25B, with anchor structure 3952 deployed on an outer surface 3954 of a housing of the stimulation electrode arrangement 3951 having at least one contact electrode 3958. As shown in FIG. 24D, in some examples the anchor structure 3952 comprises a plurality of anchor elements 3964 (like anchor elements 3914) extending over the surface area of the entire (or substantially the entire) outer surface 3954 of the stimulation electrode arrangement 3951 , including lower and upper surfaces 3955A, 3955B, and side surfaces 3953A, 3953B, (and end surfaces not seen in the sectional view). As further shown in FIG. 24D, electrical conductors 3956 extend within1618.294.1 1 1125 and through the interior 3957 of the stimulation electrode arrangement 3951 with a respective one of the conductors 3956 being electrically connected (via link 3959) to the contact electrode 3958 on lower surface 3955A of the stimulation electrode arrangement 3951. Like the anchor structures present on lead segments (which extend between adjacent stimulation elements), the anchor structure 3952 on an outer surface 3954 of a stimulation element as in FIG. 24D may enhance securely fixing the stimulation electrode arrangement in a position of stimulating relation to target tissues in the manner of the examples of FIGS. 1A-21 B.

[0392] FIG. 24E is a side plan view of a portion of an example stimulation lead including a stimulation portion 4040 comprising an anchor structure 4044 including anchor portions (e.g., 4045A, 4045B). In some examples, the stimulation portion 4040 may comprise at least some of substantially the same features and attributes as the stimulation portions described in associated with FIGS. 22A-24D.

[0393] As shown in FIG. 24E, the stimulation portion 4040 may comprise an array 4042 of spaced apart stimulation elements 4032L, 4032M, 4032N, 40320 within lead segment 4030I extending between elements 4032L and 4032M, lead segment 4030J extending between elements 4032M and 4032N, lead segment 4030K extending between elements 4032N and 40320, and so on. Lead segments 4030H and 4030L are present on opposite ends of the array.

[0394] In some examples, each stimulation element 4032L, 4032M, etc., and each lead segment 4030I, 4030J, etc., may comprise a generally cylindrical shape. However, in some examples, each stimulation element 4032L, 4032M, etc. and / or each lead segment 4030I, 4030J, etc., may comprise a shape other than a generally cylindrical shape. Regardless of the particular shape, in some examples, the anchor elements 4047 may extend completely about a circumference of the respective lead segments (e.g., 4030I, 4030J), or in some examples, may extend partially about a circumference of the respective lead segments (e.g., 4030I, 4030J) per at least some of the example implementations described in association with at least FIGS. 22A-24D or described below in association with at least FIGS. 25A-27E.1618.294.1 1 1126

[0395] As shown in FIG. 24E, in some examples the stimulation portion 4040 may comprise a first amount of spacing S1 between adjacent pairs of stimulation elements (e.g., 4032M, 4032N) which may be uniform in some examples. However, in some examples, as later shown in FIG. 24F, non-uniform spacing (e.g., S2, S1 , and so on) may be implemented.

[0396] As further shown in FIG. 24E, each anchor portion 4045A, 4045B comprises an array of anchor elements 4047 arranged on a surface of a lead segment (e.g., 4030I, 4030K) in a pattern, density, thickness, and / or orientation, such that anchor elements 4047 work individually and / or collectively to securely engage surrounding tissue to thereby secure at least a portion of the stimulation portion 4040 relative to the surrounding tissue. In addition to their collective arrangement, each anchor element 4047 may include barbs, protrusions, shapes, sizes, and / or orientations which enhance secure engagement of the surrounding tissue. In some examples, the anchor portions of FIGS. 24E-24F, such as, but not limited to, the anchor elements 4047 may comprise at least some of substantially the same features and attributes as, and / or an example implementation of, the anchor elements 3824 and related components as described in association with at least FIGS. 23A-23C.

[0397] In some examples, at least because such anchor portions 4045A, 4045B (FIGS. 24E-24F) are situated immediately adjacent to at least some stimulation elements (e.g., 4032L and 4032M or 4032N and 40320, respectively), the anchor portions may secure the stimulation elements to be maintained in a stable position in stimulating relation to nearby target tissues (e.g., target nerve portions, target muscle portions, and / or target neuromuscular junctions).

[0398] In some examples, the anchor structure 4044 may comprise a spacing S7 between the respective anchor elements 4047 which is substantially less than a spacing S1 between adjacent stimulation elements (e.g., 4032M, 4032N, etc.) on the stimulation portion 4040. In some such examples, the “substantially less” spacing may be implemented via the anchor structure 4044 comprising a spacing S7 between the respective anchor elements 4047 which is at least one order of magnitude less than a spacing S1 between adjacent stimulation elements (e.g., 4032M, 4032N, etc.) on the stimulation portion 4040. In some examples, the1618.294.1 1 1127 substantially less spacing may be implemented via the spacing S7 being at least two orders of magnitude less than the spacing S1 between adjacent stimulation elements (e.g., 4032M, 4032N, etc.). In some examples, the substantially less spacing may be implemented via the spacing S7 being 50 percent less than the spacing S1 between adjacent stimulation elements (e.g., 4032M, 4032N, etc.).

[0399] In some examples, these same relationships (of the spacing S7 between anchor elements 4047 being substantially less than the spacing S1 ) also apply to larger spacing(s) between adjacent stimulation elements, such as when there is a greater spacing between adjacent stimulation elements, such as spacing S2 in FIG. 24F between adjacent stimulation elements 4032R and 4032S, which is greater than spacing S1 between adjacent stimulation elements 4032S and 4032T.

[0400] In some examples, such compressed spacing between adjacent anchor elements 4047 may be expressed as a density of the anchor elements 4047 in which the density may comprise a selected number of such anchor elements 4047 per area (e.g., square centimeters, square inches, and the like) of the stimulation portion 4040, such as on lead segments 4030I, 4030K, 4030Q, etc. In some such examples, the anchor elements 4047 ma...

Claims

1618.294.1 1 1207CLAIMSWhat is claimed is:1 . A medical device comprising: an acoustic sensor configured to sense upper airway airflow of a patient to generate a signal; a control portion configured to determine respiratory information based on the signal; and a stimulation element to apply electrical stimulation to an upper airway patency-related tissue of a patient based on the respiratory information.

2. The medical device of claim 1 , further comprising: a housing enclosing at least a portion of the control portion and at least a portion of the stimulation element; and a lead coupled to the housing, wherein the acoustic sensor is coupled to the lead and spaced apart from the housing.

3. The medical device of claim 1 , further comprising: a housing enclosing at least a portion of the control portion and at least a portion of the stimulation element, wherein the stimulation element comprises at least one electrode on the housing.1618.294.1 1 12084. The medical device of claim 1 , further comprising: a housing enclosing at least a portion of the control portion and at least a portion of the stimulation element, wherein the stimulation element comprises at least two electrodes on the housing.

5. The medical device of claim 1 , further comprising: a housing enclosing at least a portion of the control portion and at least a portion of the stimulation element, the housing comprising an integrated cuff electrode.

6. The medical device of claim 1 , wherein the upper airway airflow comprises airway turbulence.

7. The medical device of claim 1 , wherein the medical device is sized and / or shaped to be implanted in a neck region of the patient.

8. The medical device of claim 1 , wherein the acoustic sensor comprises an implantable acoustic senor configured to wirelessly transmit the signal to the control portion; wherein the control portion comprises an external control portion configured to wirelessly receive the signal from the acoustic sensor and to wirelessly control the stimulation element; and wherein the stimulation element comprises an implantable stimulation element.

9. The medical device of claim 8, wherein the control portion comprises a mobile device, a bedside appliance, a wireless power transmission device to power the acoustic sensor and the stimulation element, or a cloud based computing system.

10. An implantable medical device comprising:1618.294.1 1 1209 an acoustic sensor configured to sense airway airflow of a patient to generate a sensor signal; a control portion configured to: filter the sensor signal; generate an intensity signal from the filtered signal; compare the intensity signal to a threshold; at each crossing of the intensity signal from below to above the threshold, start an integral calculation of the intensity signal; at each crossing of the intensity signal from above to below the threshold, stop the integral calculation to provide a current integral value corresponding to a most recent respiratory phase; calculate a rolling average parameter of the integral values from a selected number of most recent integral calculations; determine the most recent respiratory phase is an expiratory phase in response to the current integral value being greater than the rolling average parameter; and determine the most recent respiratory phase is an inspiratory phase in response to the current integral value being less than the rolling average parameter; and a stimulation element to apply electrical stimulation to an upper airway patency-related tissue of a patient based on the inspiratory phase or expiratory phase.

11. The implantable medical device of claim 10, wherein the rolling average parameter comprises a rolling mean or a rolling median.

12. The implantable medical device of claim 10, wherein the acoustic sensor comprises a microphone.

13. The implantable medical device of claim 12, wherein the microphone comprises a microelectromechanical system (MEMS), an optical mechanism, a piezoelectric crystal, or a piezoelectric film.1618.294.1 1 121014. The implantable medical device of claim 10, wherein the acoustic sensor comprises an accelerometer.

15. The implantable medical device of claim 14, wherein the accelerometer is configured to be set to operate in a low frequency mode to sense frequencies less than 10 hertz or configured to be set to a high frequency mode to sense frequencies greater than 50 hertz.

16. The implantable medical device of claim 10, wherein the implantable medical device is sized and / or shaped to be implanted in a neck region of the patient.

17. The implantable medical device of claim 16, wherein the implantable medical device is sized and / or shaped to be implanted in a lower neck region.

18. The implantable medical device of claim 16, wherein the implantable medical device is sized and / or shaped to be implanted in a mid neck region.

19. The implantable medical device of claim 16, wherein the implantable medical device is sized and / or shaped to be implanted in an upper neck region.

20. The implantable medical device of claim 10, wherein the implantable medical device is sized and / or shaped to be implanted near a larynx of the patient.21 . The implantable medical device of claim 10, wherein the control portion is configured to filter the signal to reject at least one of ambient sound in the patient’s environment, snoring, speech, muscle motion, or cardiac-based motion.

22. The implantable medical device of claim 10, wherein the control portion is configured to band pass filter the sensor signal via an about 200 hertz high pass filter and an about 2000 hertz low pass filter.1618.294.1 1 121 123. The implantable medical device of claim 10, wherein the control portion is configured to filter the sensor signal via a hardware filter.

24. The implantable medical device of claim 10, wherein the control portion is configured to selectively switch the acoustic sensor on and off based on a duty cycle.

25. The implantable medical device of claim 10, wherein a sampling rate of the acoustic sensor is set at less than two times a bandwidth of the sensor signal.

26. The implantable medical device of claim 10, wherein in an open loop mode of operation, the control portion is configured to set the acoustic sensor to operate at a first sampling rate between predicted crossings of the intensity signal from below to above the threshold and from above to below the threshold, and set the acoustic sensor to operate at a second sampling rate greater than the first sampling rate during the predicted crossings of the intensity signal from below to above the threshold and from above to below the threshold.

27. The implantable medical device of claim 26, wherein the first sampling rate equals 0.

28. The implantable medical device of claim 10, wherein the control portion is configured to generate the intensity signal by taking an absolute value or square of the filtered signal and applying an about 0.5 to 5 hertz low pass filter to generate the intensity signal.

29. The implantable medical device of claim 10, wherein the control portion is configured to generate the intensity signal by taking an absolute value or square of the filtered signal and applying an about 0.5 to 5 hertz low pass filter and an about 1 second median filter to generate the intensity signal.1618.294.1 1 121230. The implantable medical device of claim 10, wherein the control portion is configured to set the threshold as a minimum of the intensity signal over a previous period within a range between about 10 seconds and about 50 seconds plus a selectable positive value.31 . The implantable medical device of claim 10, wherein the control portion is configured to set the threshold as a minimum of the intensity signal over a previous period within a range between about 4 integral calculations and about 20 integral calculations plus a selectable positive value.

32. The implantable medical device of claim 10, wherein the control portion is configured to, at each crossing of the intensity signal from above to below the threshold, determine whether the crossing corresponds to an end of an inspiratory phase or an end of an expiratory phase.

33. The implantable medical device of claim 10, wherein the control portion is configured to, at each crossing of the intensity signal from below to above the threshold, determine whether the crossing corresponds to a start of an inspiratory phase or a start of an expiratory phase.

34. The implantable medical device of claim 10, wherein the control portion is configured to determine a nominal intensity value as a rolling average parameter of the integral values from a most recent first selected number of integral calculations within a range between about 4 integral calculations and about 20 integral calculations.

35. The implantable medical device of claim 34, wherein the control portion is configured to determine a sleep disordered breathing event in response to a rolling average parameter of a most recent second selected number of prior integral calculations within a range between about 1 integral calculation and about 5 integral calculations being less than the nominal intensity value minus a selectable value.1618.294.1 1 121336. The implantable medical device of claim 35, wherein the control portion is configured to determine an apnea hypopnea index (AHI) based on a number of sleep disordered breathing events per hour.

37. The implantable medical device of claim 36, wherein the control portion is configured to transmit the AHI to an external device.

38. The implantable medical device of claim 36, wherein the control portion is configured to adjust the electrical stimulation applied by the stimulation element based on the AHI.

39. The implantable medical device of claim 10, wherein the control portion is configured to determine a nominal intensity value as a rolling average parameter of the integral values from a prior first selected number of integral calculations within a range between about 4 integral calculations and about 20 integral calculations while excluding a most recent third selected number of integral calculations within a range between about 1 integral calculation and about 5 integral calculations.

40. The implantable medical device of claim 39, wherein the control portion is configured to determine a sleep disordered breathing event in response to a rolling average parameter of a most recent second selected number of prior integral calculations within a range between about 1 integral calculation and about 5 integral calculations being less than the nominal intensity value minus a selectable value.41 . The implantable medical device of claim 40, wherein the control portion is configured to determine an apnea hypopnea index (AHI) based on a number of sleep disordered breathing events per hour.1618.294.1 1 121442. The implantable medical device of claim 41 , wherein the control portion is configured to transmit the AHI to an external device.

43. The implantable medical device of claim 41 , wherein the control portion is configured to adjust the electrical stimulation applied by the stimulation element based on the AHI.

44. An implantable medical device comprising: an acoustic sensor configured to sense airway airflow of a patient to generate a signal; a control portion configured to determine respiratory information based on the signal; and a housing enclosing the acoustic sensor and the control portion, wherein the acoustic sensor is adjacent an inner wall of the housing.

45. The implantable medical device of claim 44, wherein the housing encloses air between the inner wall and the acoustic sensor.

46. The implantable medical device of claim 44, further comprising: a potting material filling all space between the inner wall and the acoustic sensor.

47. The implantable medical device of claim 44, further comprising: a potting material filling a first portion of the space between the inner wall and the control portion; and air filling a second portion of the space between the inner wall and the acoustic sensor.

48. The implantable medical device of claim 44, wherein the housing comprises a potting material.1618.294.1 1 121549. The implantable medical device of claim 44, wherein the housing comprises a first potting material encapsulating the acoustic sensor and the control portion and a second potting material encapsulating the first potting material.

50. The implantable medical device of claim 44, wherein the housing comprises a first potting material encapsulating the acoustic sensor and a second potting material encapsulating the control portion.51 . The implantable medical device of claim 50, wherein the potting material comprises epoxy, polyurethane, liquid crystal polymer (LCP), or silicone.

52. The implantable medical device of claim 44, wherein the acoustic sensor directly contacts the inner wall.

53. The implantable medical device of claim 44, wherein the housing comprises titanium, stainless steel, MP35N, aluminum oxide (AI2O3), zirconium oxide (ZO2), liquid-crystal polymer (LCP), polyetheretherketone (PEEK), polyoxidemethylene (POM), polypropylene, polycarbonate, polysulfone (PSU), or epoxy.

54. The implantable medical device of claim 44, wherein the housing comprises a first portion and a second portion extending through the first portion and forming a flexible membrane, and wherein the acoustic sensor is adjacent to the flexible membrane.

55. The implantable medical device of claim 54, wherein the first portion comprises a first material and the second portion comprises a second material different from the first material.1618.294.1 1 121656. The implantable medical device of claim 55, wherein the first material comprises a metal and the second material comprises a polymeric material or a ceramic material.

57. The implantable medical device of claim 56, wherein the metal comprises titanium, stainless steel, or MP35N, and wherein the second material comprises silicone, polycarbonate (PC), polyetheretherketone (PEEK), polyoxidemethylene (POM), polypropylene, polysulfone (PSU), or epoxy.

58. The implantable medical device of claim 56, wherein the second portion comprises a thickness between an inner wall of the second portion and an outer wall of the second portion within a range between about 0.4 millimeters and about 0.6 millimeters.

59. The implantable medical device of claim 54, wherein the first portion comprises a first material and a first thickness between an inner wall of the first portion and an outer wall of the first portion, and the second portion comprises the first material and a second thickness between an inner wall of the second portion and an outer wall of the second portion less than the first thickness.

60. The implantable medical device of claim 59, wherein the second thickness is within a range between about 0.05 millimeters and about 0.2 millimeters.

61. The implantable medical device of claim 44, wherein the housing comprises a first portion and a second portion extending through the first portion, wherein the acoustic sensor is encapsulated by the second portion.

62. The implantable medical device of claim 61 , wherein the acoustic sensor is coated in parylene.1618.294.1 1 121763. The implantable medical device of claim 61 , wherein the first portion comprises a first material and the second portion comprises a second material different from the first material.

64. The implantable medical device of claim 63, wherein the first material comprises a metal and the second material comprises a potting material.

65. The implantable medical device of claim 64, wherein the potting material comprises epoxy, polyurethane, liquid crystal polymer (LCP), or silicone.

66. The implantable medical device of claim 44, wherein the housing comprises an enclosure and a header, and wherein the acoustic sensor is inside the header.

67. The implantable medical device of claim 44, wherein the housing comprises an enclosure and a portion comprising a potting material attached to the enclosure, and wherein the acoustic sensor is inside the portion.

68. The implantable medical device of claim 67, further comprising an antenna inside the portion.

69. The implantable medical device of claim 44, wherein the acoustic sensor comprises a digital acoustic sensor, and wherein the control portion comprises: a downsample element to receive a digital sensor signal from the digital acoustic sensor and downsample the digital sensor signal; a high pass filter to receive the downsampled digital sensor signal and high pass filter the downsampled digital sensor signal; and a respiratory detection element to receive the high pass filtered digital sensor signal and determine respiratory information based on the high pass filtered digital sensor signal.1618.294.1 1 121870. The implantable medical device of claim 69, wherein the downsample element comprises a sample rate within a range between about 600 hertz and about 4000 hertz, and wherein the high pass filter comprises a cutoff frequency within a range between about 80 hertz and about 300 hertz.71 . The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: a hardware low pass filter to receive an analog sensor signal from the analog acoustic sensor and low pass filter the analog sensor signal; an analog to digital conversion element to receive the low pass filtered analog sensor signal and convert the analog sensor signal to a digital sensor signal; a high pass filter to receive the digital sensor signal and high pass filter the digital sensor signal; and a respiratory detection element to receive the high pass filtered digital sensor signal and determine respiratory information based on the high pass filtered digital sensor signal.

72. The implantable medical device of claim 71 , wherein the hardware low pass filter comprises a cutoff frequency within a range between about 400 hertz and about 2000 hertz, wherein the analog to digital conversion element comprises a sampling rate with a range between about 600 hertz and about 4000 hertz, and wherein the high pass filter comprises a cutoff frequency within a range between about 80 hertz and about 300 hertz.

73. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises:1618.294.1 1 1219 a hardware band pass filter to receive an analog sensor signal from the analog acoustic sensor and band pass filter the analog sensor signal; an analog to digital conversion element to receive the band pass filtered analog sensor signal and convert the analog sensor signal to a digital sensor signal; and a respiratory detection element to receive the digital sensor signal and determine respiratory information based on the digital sensor signal.

74. The implantable medical device of claim 73, wherein the hardware band pass filter comprises a lower cutoff frequency within a range between about 80 hertz and about 300 hertz and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, and wherein the analog to digital conversion element comprises a sampling rate with a range between about 600 hertz and about 4000 hertz.

75. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: a hardware band pass filter to receive an analog sensor signal from the analog acoustic sensor and band pass filter the analog sensor signal; an envelope detector or rectifier or square law detector to receive the band pass filtered analog sensor signal and detect an envelope of the band pass filtered analog sensor signal; an analog to digital conversion element to receive the analog envelope signal and convert the analog envelope signal to a digital envelope signal; and a respiratory detection element to receive the digital envelope signal and determine respiratory information based on the digital envelope signal.

76. The implantable medical device of claim 75, wherein the hardware band pass filter comprises a lower cutoff frequency within a range between about 801618.294.1 1 1220 hertz and about 300 hertz and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, and wherein the analog to digital conversion element comprises a sampling rate with a range between about 10 hertz and about 200 hertz.

77. The implantable medical device of claim 44, wherein the acoustic sensor comprises a digital acoustic sensor, and wherein the control portion comprises: a downsample element to receive a digital sensor signal from the digital acoustic sensor and downsample the digital sensor signal; a low pass filter to receive the downsampled digital sensor signal and low pass filter the downsampled digital sensor signal; and a cardiac detection element to receive the low pass filtered digital sensor signal and determine cardiac information based on the low pass filtered digital sensor signal.

78. The implantable medical device of claim 77, wherein the downsample element comprises a sample rate within a range between about 600 hertz and about 4000 hertz, and wherein the low pass filter comprises a cutoff frequency within a range between about 80 hertz and about 300 hertz.

79. The implantable medical device of claim 77, wherein the control portion comprises an anti-aliasing low pass filter communicatively coupled between the digital acoustic sensor and the downsample element.

80. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: a hardware low pass filter to receive an analog sensor signal from the analog acoustic sensor and low pass filter the analog sensor signal;1618.294.1 1 1221 an analog to digital conversion element to receive the low pass filtered analog sensor signal and convert the analog sensor signal to a digital sensor signal; a low pass filter to receive the digital sensor signal and low pass filter the digital sensor signal; and a cardiac detection element to receive the low pass filtered digital sensor signal and determine cardiac information based on the low pass filtered digital sensor signal.81 . The implantable medical device of claim 80, wherein the hardware low pass filter comprises a cutoff frequency within a range between about 400 hertz and about 2000 hertz, wherein the analog to digital conversion element comprises a sampling rate with a range between about 600 hertz and about 4000 hertz, and wherein the low pass filter comprises a cutoff frequency within a range between about 80 hertz and about 300 hertz.

82. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: a hardware low pass or band pass filter to receive an analog sensor signal from the analog acoustic sensor and low pass or band pass filter the analog sensor signal; an analog to digital conversion element to receive the low pass or band pass filtered analog sensor signal and convert the analog sensor signal to a digital sensor signal; and a cardiac detection element to receive the digital sensor signal and determine cardiac information based on the digital sensor signal.

83. The implantable medical device of claim 82, wherein the hardware band pass filter comprises a lower cutoff frequency within a range between about 801618.294.1 1 1222 hertz and about 300 hertz and a higher cutoff frequency within a range between about 400 hertz and about 2000 hertz, and wherein the analog to digital conversion element comprises a sampling rate with a range between about 600 hertz and about 4000 hertz.

84. The implantable medical device of claim 44, wherein the acoustic sensor comprises a digital acoustic sensor, and wherein the control portion comprises: a downsample element to receive a digital sensor signal from the digital acoustic sensor and downsample the digital sensor signal; a low pass filter to receive the downsampled digital sensor signal and low pass filter the downsampled digital sensor signal; a cardiac detection element to receive the low pass filtered digital sensor signal and determine cardiac information based on the low pass filtered digital sensor signal; a high pass filter to receive the downsampled digital sensor signal and high pass filter the downsampled digital sensor signal; and a respiratory detection element to receive the high pass filtered digital sensor signal and determine respiratory information based on the high pass filtered digital sensor signal.

85. The implantable medical device of claim 84, wherein the control portion comprises an anti-aliasing low pass filter communicatively coupled between the digital acoustic sensor and the downsample element.

86. The implantable medical device of claim 84, wherein the control portion comprises a microcontroller unit comprising the downsample element, the low pass filter, the cardiac detection element, the high pass filter, and the respiratory detection element.

87. The implantable medical device of claim 84, wherein the control portion comprises:1618.294.1 1 1223 an application specific integrated circuit comprising the downsample element; and a microcontroller unit comprising the low pass filter, the cardiac detection element, the high pass filter, and the respiratory detection element.

88. The implantable medical device of claim 84, wherein the control portion comprises: an application specific integrated circuit comprising the downsample element, the low pass filter, and the high pass filter; and a microcontroller unit comprising the cardiac detection element and the respiratory detection element.

89. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: an analog to digital conversion element to receive the analog sensor signal and convert the analog sensor signal to a digital sensor signal; a low pass filter to receive the digital sensor signal and low pass filter the digital sensor signal; a cardiac detection element to receive the low pass filtered digital sensor signal and determine cardiac information based on the low pass filtered digital sensor signal; a high pass filter to receive the digital sensor signal and high pass filter the digital sensor signal; and a respiratory detection element to receive the high pass filtered digital sensor signal and determine respiratory information based on the high pass filtered digital sensor signal.

90. The implantable medical device of claim 44, wherein the acoustic sensor comprises a digital acoustic sensor, and wherein the control portion comprises:1618.294.1 1 1224 a downsample element to receive a digital sensor signal from the digital acoustic sensor and downsample the digital sensor signal; a low pass filter to receive the downsampled digital sensor signal (S) and low pass filter the downsampled digital sensor signal; a cardiac detection element to receive the low pass filtered digital sensor signal (S_LPF) and determine cardiac information based on the low pass filtered digital sensor signal; a subtract element to subtract the low pass filtered downsampled digital sensor signal (S_LPF) from the downsampled digital sensor signal (S); and a respiratory detection element to receive the signal S - S_LPF and determine respiratory information based on the signal S - S LPF.91 . The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: an analog to digital conversion element to receive the analog sensor signal and convert the analog sensor signal to a digital sensor signal (S); a low pass filter to receive the digital sensor signal and low pass filter the digital sensor signal; a cardiac detection element to receive the low pass filtered digital sensor signal (S_LPF) and determine cardiac information based on the low pass filtered digital sensor signal; a subtract element to subtract the low pass filtered digital sensor signal (S_LPF) from the digital sensor signal (S); and a respiratory detection element to receive the signal S - S_LPF and determine respiratory information based on the signal S - S_LPF.

92. The implantable medical device of claim 44, wherein the acoustic sensor comprises a digital acoustic sensor, and wherein the control portion comprises:1618.294.1 1 1225 a downsample element to receive a digital sensor signal from the digital acoustic sensor and downsample the digital sensor signal; a high pass filter to receive the downsampled digital sensor signal (S) and high pass filter the downsampled digital sensor signal; a respiratory detection element to receive the high pass filtered digital sensor signal (S_HPF) and determine respiratory information based on the high pass filtered digital sensor signal; a subtract element to subtract the high pass filtered downsampled digital sensor signal (S_HPF) from the downsampled digital sensor signal (S); and a cardiac detection element to receive the signal S - S_HPF and determine cardiac information based on the signal S - S HPF.

93. The implantable medical device of claim 44, wherein the acoustic sensor comprises an analog acoustic sensor, and wherein the control portion comprises: an analog to digital conversion element to receive the analog sensor signal and convert the analog sensor signal to a digital sensor signal (S); a high pass filter to receive the digital sensor signal and high pass filter the digital sensor signal; a respiratory detection element to receive the high pass filtered digital sensor signal (S_HPF) and determine respiratory information based on the high pass filtered digital sensor signal; a subtract element to subtract the high pass filtered digital sensor signal (S_HPF) from the digital sensor signal (S); and a cardiac detection element to receive the signal S - S_HPF and determine cardiac information based on the signal S - S_HPF.

94. A medical device comprising: an acoustic sensor configured to sense upper airway airflow of a patient to generate an acoustic sensor signal;1618.294.1 1 1226 an accelerometer sensor configured to sense respiratory movement of the neck tissue of a patient to generate an accelerometer sensor signal; a control portion configured to determine respiratory information based on the acoustic sensor signal and the accelerometer sensor signal; and a stimulation element to apply electrical stimulation to an upper airway patency-related tissue of a patient based on the respiratory information.

95. The implantable medical device of claim 94, wherein the control portion is configured to identify inspiratory phases and expiratory phases within the accelerometer sensor signal based on the acoustic sensor signal.

96. A method comprising: sensing, via an implantable acoustic sensor, airway airflow of a patient to generate a sensor signal; filtering the sensor signal; generating an intensity signal from the filtered signal; comparing the intensity signal to a threshold; at each crossing of the intensity signal from below to above the threshold, starting an integral calculation of the intensity signal; at each crossing of the intensity signal from above to below the threshold, stopping the integral calculation to provide a current integral value corresponding to a most recent respiratory phase; calculating a rolling average parameter of the integral values from a selected number of most recent integral calculations; determining the most recent respiratory phase is an expiratory phase in response to the current integral value being greater than the rolling average parameter; determining the most recent respiratory phase is an inspiratory phase in response to the current integral value being less than the rolling average parameter; and1618.294.1 1 1227 applying electrical stimulation, via an implantable stimulation element, to an upper airway patency-related tissue of a patient based on the inspiratory phase or expiratory phase.

97. A method comprising: receiving a sensor signal based on acoustically sensing an upper airway airflow parameter; determining an intensity parameter indicative of a respiratory phase based on the sensor signal; determining a baseline of the intensity parameters from a selected number of most recent intensity parameters; in response to the most recent intensity parameter being greater than the baseline, identifying the most recent intensity parameter as indicative of an expiratory phase; in response to the most recent intensity parameter being less than the baseline, identifying the most recent intensity parameter as indicative of an inspiratory phase; predicting a future respiratory phase based on the most recent intensity parameter; and stimulating a target tissue timed relative to the predicted future respiratory phase.

98. The method of claim 97, wherein the intensity parameter comprises at least one of area, peak, or shape.

99. A method comprising: receiving a sensor signal based on acoustically sensing an upper airway airflow parameter; determining an intensity parameter indicative of a respiratory phase based on the sensor signal; determining a first average parameter of the intensity parameters from a first selected number of most recent intensity parameters;1618.294.1 1 1228 determining a second average parameter of the intensity parameters from a second selected number of most recent intensity parameters where the second selected number is greater than the first selected number; and in response to the first average parameter being less than the second average parameter minus a selectable value, determining a sleep disordered breathing (SDB) event has occurred.

100. The method of claim 99, further comprising: adjusting parameters of electrical stimulation to target tissue in response to determining an SDB event has occurred.101 . The method of claim 99, further comprising: in response to the first average parameter being greater than the second average parameter minus a selectable value, determining a sleep disordered breathing (SDB) event has not occurred; and maintaining parameters of electrical stimulation to target tissue in response to determining an SDB event has not occurred.

102. The method of claim 99, further comprising: reporting the SDB event to an external device in response to determining an SDB event has occurred.

103. A method comprising: receiving a sensor signal based on sensing an upper airway airflow parameter; generating an intensity signal based on the sensor signal; determining an area of the intensity signal corresponding to a respiratory phase for each portion of the intensity signal that exceeds a selectable threshold; determining a baseline of the areas from a selected number of most recent determined areas;1618.294.1 1 1229 in response to the most recent determined area being greater than the baseline, identifying the most recent determined area as corresponding to an expiratory phase; in response to the most recent determined area being less than the baseline, identifying the most recent determined area as corresponding to an inspiratory phase; and applying electrical stimulation to target tissue timed relative to a predicted next respiratory phase.

104. A method comprising: receiving a sensor signal based on sensing an upper airway airflow parameter; generating an intensity signal based on the sensor signal; detecting peaks of the intensity signal above a threshold; and determining respiratory information based on the detected peaks.

105. The method of claim 104, further comprising: predicting a future inspiratory phase based on the detected peaks; and applying electrical stimulation to target tissue based on the predicted future inspiratory phase.

106. A method comprising: receiving a sensor signal based on sensing an upper airway airflow parameter; generating an intensity signal based on the sensor signal; determining midpoints of each region of the intensity signal above a threshold; and determining respiratory information based on the determined midpoints.

107. The method of claim 106, further comprising: predicting a future inspiratory phase based on the determined midpoint; and1618.294.1 1 1230 applying electrical stimulation to target tissue based on the predicted future inspiratory phase.

108. A method comprising: receiving an acoustic sensor signal indicative of upper airway airflow; filtering the acoustic sensor signal; and detecting heart beat sounds based on the filtered acoustic sensor signal.

109. The method of claim 108, wherein filtering the acoustic sensor signal comprises low pass filtering the acoustic sensor signal with a low pass frequency of about 200 hertz.

110. The method of claim 108, wherein filtering the acoustic sensor signal comprises band pass filtering the acoustic sensor signal with a low pass frequency of about 200 hertz and a high pass frequency of about 20 hertz.11 1 . The method of claim 108, further comprising: taking the absolute value or square of the filtered acoustic sensor signal and determining a moving average of the signal prior to detecting the heart beat sounds.

112. The method of claim 108, further comprising: determining heart rate and heart rate variabitliy based on the detected heart beat sounds.1 13. The method of claim 108, wherein detecting heart beat sounds comprises detecting heart beat sounds by detecting peaks in the filtered acoustic sensor signal.1 14. A method comprising: receiving an acoustic sensor signal indicative of upper airway airflow;1618.294.1 1 1231 detecting saturation flags of the acoustic sensor signal due to saturation of a processing component due to the acoustic sensor signal; and detecting heart beat sounds based on the detected saturation flags.

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