Sleep onset and wakefulness detection

Abstract

A device and / or method for detecting a second instance of sleep onset within a nighttime treatment period according to a second criterion different from a first criterion associated with a first instance of sleep onset during the nighttime treatment period.

Description

[Background technology]

[0001] A significant portion of the population suffers from various forms of sleep-related problems, some of which may include sleep-disordered breathing (SDB) and / or other conditions. In some patients, sleep-disordered breathing behaviors may not be treatable with external respiratory treatment devices and / or simple surgical intervention. [Brief explanation of the drawings]

[0002] [Figure 1A] FIG. 1A is a diagram schematically illustrating an example of a method for detecting sleep. [Figure 1B] FIG. 1B includes a front view of a schematic representation of a patient's body including an exemplary implantable component and an exemplary external element of an exemplary method and / or an exemplary device. [Figure 1C] FIG. 1C is a schematic diagram of the control section. [Figure 2] FIG. 2 is a diagram that schematically illustrates an exemplary timeline of sleep-wake related events according to an exemplary method of sleep-wake determination. [Figure 3A] FIG. 3A is a chart showing measured signal versus threshold for determining sleep-wake state. [Figure 3B] FIG. 3B is a chart showing measured signal versus threshold for determining sleep-wake state. [Figure 4A] FIG. 4A is a diagram that schematically illustrates an exemplary method for determining sleep-wake states. [Figure 4B] FIG. 4B is a diagram that schematically illustrates an exemplary method for determining sleep-wake states. [Figure 4C] FIG. 4C is a diagram that schematically illustrates an exemplary method for determining sleep-wake states. [Figure 4D] FIG. 4D is a diagram that schematically illustrates an exemplary method for determining sleep-wake states. [Figure 4E] FIG. 4E is a diagram that schematically illustrates an exemplary method for determining sleep-wake states. [Figure 5A] FIG. 5A is a diagram that schematically illustrates another exemplary method for determining sleep-wake states. [Figure 5B] FIG. 5B is a diagram that schematically illustrates another exemplary method for determining sleep-wake states. [Figure 6A] FIG. 6A is a diagram that schematically illustrates yet another exemplary method for determining a sleep-wake state. [Figure 6B] FIG. 6B is a diagram that schematically illustrates yet another exemplary method for determining a sleep-wake state. [Figure 6C] FIG. 6C is a diagram that schematically illustrates yet another exemplary method for determining a sleep-wake state. [Figure 6D] FIG. 6D is a diagram that schematically illustrates yet another exemplary method for determining sleep-wake states. [Figure 6E] FIG. 6E is a diagram that schematically illustrates yet another exemplary method for determining sleep-wake states. [Figure 6F] FIG. 6F is a diagram that schematically illustrates yet another exemplary method for determining a sleep-wake state. [Figure 7] FIG. 7 is a diagram that schematically illustrates yet another example method for determining a sleep-wake state. [Figure 8] FIG. 8 is a diagram that schematically illustrates an exemplary method for sensing physiological information through sensing movement. [Figure 9A] FIG. 9A is a diagram that schematically illustrates an exemplary method for determining sleep-wake states in comparison with posture information. [Figure 9B] FIG. 9B is a diagram that schematically illustrates an exemplary method for determining sleep-wake states in comparison with posture information. [Figure 9C] FIG. 9C is a diagram that schematically illustrates an exemplary method for determining sleep-wake states with respect to different exemplary sensed physiological parameters. [Figure 10] FIG. 10 is a flow diagram that schematically illustrates an exemplary method for detecting sleep and / or maintaining stimulation therapy. [Figure 11] FIG. 11 is a diagram that schematically illustrates an exemplary method for detecting sleep. [Figure 12] FIG. 12 is a diagrammatic representation of an exemplary method that includes distinguishing between body movements, postures, and the like. [Figure 13] FIG. 13 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing respiratory phase information. [Figure 14] FIG. 14 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing respiratory phase information. [Figure 15] FIG. 15 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing respiratory phase information. [Figure 16] FIG. 16 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing respiratory phase information. [Figure 17] FIG. 17 is a flow diagram that schematically illustrates an exemplary method for determining sleep-wake states with respect to variability in physiological signals / information. [Figure 18] FIG. 18 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing exemplary movement information. [Figure 19] FIG. 19 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing exemplary movement information. [Figure 20] FIG. 20 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states by comparing exemplary movement information. [Figure 21] FIG. 21 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states via identifying fluctuations in sensed physiological information relative to a threshold. [Figure 22] FIG. 22 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states via identifying fluctuations in sensed physiological information relative to a threshold. [Figure 23] FIG. 23 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states through tracking parameters related to time, activity, non-movement parameters, etc. [Figure 24] FIG. 24 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states through tracking parameters related to time, activity, non-movement parameters, etc. [Figure 25] FIG. 25 is a diagram that schematically illustrates an exemplary method for determining a sleep-wake state according to the probability of sleep and / or the probability of wakefulness. [Figure 26A] FIG. 26A is a diagram that schematically illustrates an exemplary method for determining a sleep-wake state with respect to taking an action based on the probability of sleep and / or the probability of wakefulness. [Figure 26B] FIG. 26B is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc., in the context of a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26C] FIG. 26C is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26D] FIG. 26D is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26E] FIG. 26E is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26F] FIG. 26F is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including starting or ending stimulation. [Figure 26G] FIG. 26G is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26H]FIG. 26H is a diagram that schematically illustrates examples of taking action relative to various exemplary boundaries related to time, temperature, sleep stage, etc. in relation to a method for determining a sleep-wake state, including initiating or terminating stimulation. [Figure 26I] FIG. 26I is a diagram that schematically illustrates an exemplary method for receiving input regarding at least a portion of the exemplary boundaries depicted in FIGS. 26B-26H. [Figure 27] FIG. 27 is a diagram that schematically illustrates an exemplary method for determining sleep-wake states, which includes splitting a sensed signal to allow evaluation of different sleep-wake decision parameters. [Figure 28] FIG. 28 is a diagram that schematically illustrates an exemplary method for determining a sleep-wake state according to the probability of sleep and / or the probability of wakefulness. [Figure 29A] FIG. 29A is a diagram schematically illustrating an exemplary method for determining a sleep-wake state according to arousal information and snoring information, respectively. [Figure 29B] FIG. 29B is a diagram schematically illustrating an exemplary method for determining sleep-wake states according to arousal information and snoring information, respectively. [Figure 30A] FIG. 30A is a block diagram that schematically illustrates an example sensing portion of an example device and / or is used as part of an example method for determining sleep-wake states. [Figure 30B] FIG. 30B is a block diagram that schematically represents an exemplary processing portion that may form part of and / or be in communication with an exemplary sensing portion. [Figure 31A] FIG. 31A includes a front view that schematically illustrates a patient's body and an exemplary implantable medical device for treating sleep-disordered breathing and / or determining sleep-wake states. [Figure 31AA] FIG. 31AA includes a front view that is a schematic representation of an exemplary implantable medical device with a sensor. [Figure 31B] FIG. 31B is a schematic representation of a different exemplary implementation of an exemplary implantable medical device as a microstimulator implanted in the head and neck region. [Figure 31C] FIG. 31C is a schematic representation of a different exemplary implementation of an exemplary implantable medical device as a microstimulator implanted in the head and neck region. [Figure 31D] FIG. 31D is a schematic representation of a different exemplary implementation of an exemplary implantable medical device as a microstimulator implanted in the head and neck region. [Figure 31E] FIG. 31E is a schematic representation of a different exemplary implementation of an exemplary implantable medical device as a microstimulator implanted in the head and neck region. [Figure 31F] FIG. 31F is a schematic representation of a different exemplary implementation of an exemplary implantable medical device as a microstimulator implanted in the head and neck region. [Figure 32] FIG. 32 is a block diagram that schematically represents an exemplary care engine. [Figure 33] FIG. 33 is a diagram schematically illustrating an exemplary breathing pattern. [Figure 34A] FIG. 34A is a block diagram that schematically illustrates an exemplary control portion. [Figure 34B] FIG. 34B is a block diagram that schematically illustrates an exemplary control portion. [Figure 35] FIG. 35 is a block diagram that schematically illustrates an exemplary user interface. [Figure 36] FIG. 36 is a block diagram that schematically depicts an exemplary communication arrangement between an implantable medical device and an external device. [Figure 37A] FIG. 37A is a diagram that schematically illustrates an exemplary user interface that includes exemplary therapeutic usage patterns, sleep-wake states, sleep quality portions, usage metrics, etc. that may be used in connection with an exemplary method and / or exemplary device for determining sleep-wake states. [Figure 37B] FIG. 37B is a diagram that schematically illustrates an exemplary method that includes taking action in relation to determining a sleep-wake state. [Figure 37C]FIG. 37C is a diagram that schematically illustrates an exemplary method that includes taking action in relation to determining a sleep-wake state. [Figure 38] FIG. 38 is a diagram that schematically illustrates an exemplary method for receiving input in connection with starting and / or stopping a therapeutic treatment. [Figure 39] FIG. 39 is a schematic representation of an exemplary method that includes tracking information regarding treatment usage, initiation, cessation, etc. [Figure 40] FIG. 40 is a diagram that schematically illustrates an exemplary method and / or exemplary device, including a medical device in association with resources for determining sleep-wake state and / or sleep delay occurrence information (e.g., variability, etc.), including training a data model. [Figure 41] FIG. 41 is a diagram that schematically illustrates an example method and / or an example device for training a data model related to determining sleep-wake states. [Figure 42] FIG. 42 is a diagram that schematically illustrates an example method and / or an example device for determining sleep-wake states according to a trained data model. [Figure 43] FIG. 43 is a schematic representation of an exemplary patient's movement signal over a 90 minute period. [Figure 44A] FIG. 44A is a chart that schematically illustrates an exemplary movement signal for detecting the onset of sleep in a patient. [Figure 44B] FIG. 44B is a chart that schematically illustrates an exemplary motion signal for detecting wake after sleep onset (WASO) in a patient. [Figure 45A] FIG. 45A is a diagram that schematically illustrates an exemplary method for determining when a patient has fallen asleep. [Figure 45B] FIG. 45B is a diagram that schematically illustrates an exemplary method for determining when a patient has fallen asleep. DETAILED DESCRIPTION OF THE INVENTION

[0003] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which are shown, by way of illustration, specific examples in which the present 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 the features of the various examples described herein may be combined with each other, either in part or in whole, unless specifically stated otherwise.

[0004] At least some examples of the present disclosure are directed to devices for the diagnosis, treatment, and / or other care of medical conditions. At least some examples may include implantable devices and / or methods involving the use of implantable devices. However, in some examples, the methods and / or devices may include at least some external components. In some examples, a therapeutic medical device may include a combination of implantable and external components.

[0005] At least a portion of the exemplary devices and / or exemplary methods may relate to detecting sleep onset and awakening, the results of which may be used in patient care, such as (but not limited to) diagnosing, assessing, monitoring, and / or treating a wide variety of patient conditions.

[0006] 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 in some instances include stimulation.

[0007] In some examples, providing patient care may include automatically determining a sleep-wake state, which may then include detecting sleep and / or detecting wakefulness. In some examples, detecting sleep includes detecting the occurrence of sleep. In some such examples, the sleep-wake determination may be used to initiate (and / or maintain) a treatment period, such as (but not limited to) a treatment in which neural stimulation therapy is used to treat sleep-disordered breathing. In some of these examples, the sleep-wake determination may include determining early sleep onset, wake after sleep onset (WASO), and / or sleep onset after WASO in order to automatically activate stimulation during sleep and automatically deactivate stimulation during wakefulness. In some examples, the same inputs, sleep-related signals, parameters, etc. may be used to detect each of early sleep onset, wake after sleep onset (WASO), and / or sleep onset after WASO. In some examples, different inputs, sleep-related signals, parameters, etc. may be used to detect each of early sleep onset, wake after sleep onset (WASO), and / or sleep onset after WASO. Additionally, thresholds, criteria, etc. associated with each input, sleep-related signal, parameter, etc. used to detect early sleep onset, wake after sleep onset (WASO), and / or sleep onset after WASO may vary.

[0008] Different patients have different sleep habits, including when they fall asleep and when they wake up during the night. This phenomenon exists regardless of whether the patient has a sleep-disordered breathing condition. Patients who fall asleep at the beginning of the night behave differently from patients who wake up during the night. This leads to differences in the signal, which leads to difficulties in setting a single threshold or criterion for detecting sleep-wake states throughout the night. Therefore, as disclosed herein, multiple (e.g., at least two) thresholds or criteria can be used to detect sleep-wake states throughout the night.

[0009] Patients may be anxious about activating stimulation before they fall asleep or waking up in the middle of the night to feel stimulation. Automatically activating / deactivating stimulation based on a patient's sleep-wake state may increase the use of the stimulation device because the patient does not need to manually activate the stimulation device, which may lead to better outcomes for the patient. Automatically activating / deactivating stimulation based on a patient's sleep-wake state may also increase the use of the stimulation device because the patient no longer worries about actually falling asleep within a predetermined window (e.g., 30 minutes) before stimulation will begin. Automatic activation / deactivation of stimulation may be performed by detecting when the patient is asleep and awake. The inputs, sleep-related signals, parameters, etc., and associated thresholds, criteria, etc., used to detect early sleep onset, wake-after-sleep-onset (WASO), and / or sleep onset after WASO may vary from patient to patient. In this manner, the inputs, sleep-related signals, parameters, etc., and associated thresholds, criteria, etc., used to detect a particular patient's sleep-wake state may be individualized for the patient.

[0010] At least some examples of determining sleep-wake states may also be related to cardiac care, drug delivery, and / or other forms of care, whether in the context of standing alone or sleep-disordered breathing (SDB) care.

[0011] These and additional examples are further described in connection with at least FIGS. 1A-42.

[0012] As schematically represented at 50 in FIG. 1A , in some examples, method 52 includes detecting a second instance of sleep onset within the nighttime treatment period according to a second criterion different from a first criterion associated with a first instance of sleep onset during the nighttime treatment period. In some examples, the first instance of sleep onset may be early sleep onset at the beginning of the nighttime treatment period, and the second instance of sleep onset may be sleep onset after WASO. The first criterion may include a first input, sleep-related signal, or parameter and / or a corresponding first threshold. The second criterion may include a second input, sleep-related signal, or parameter and / or a corresponding second threshold that is different from the first input, sleep-related signal, or parameter and / or the first threshold.

[0013] As described further below in connection with at least Figures 2-42, in some examples, determining the sleep-wake state may be associated with and / or form part of a method for determining early sleep onset based on a first sleep-wake determination parameter and a corresponding first threshold. In some examples, the exemplary method may further include determining wake after sleep onset (WASO) based on a second sleep-wake determination parameter and a corresponding second threshold. In some examples, the exemplary method may further include determining sleep onset after WASO based on a third sleep-wake determination parameter and a corresponding third threshold. The first, second, and third sleep-wake determination parameters for determining early sleep onset, wake after sleep onset (WASO), and sleep onset after WASO may be the same or different. The third threshold may be different from the first threshold and / or the second threshold.

[0014] FIG. 1B is a block diagram that schematically illustrates a patient's body 100, including exemplary target portions 110-134, in which at least some exemplary sensing and / or stimulation elements may be used to implement at least some examples of the present disclosure.

[0015] As shown in Figure 1B, the patient's body 100 includes a head and neck region 110, including a head 112 and a neck 114. The head 112 includes cranial tissue, nerves, etc., and an upper respiratory tract 116 (e.g., nerves, muscles, tissue, etc.). As further shown in Figure 1B, the patient's body 100 includes a torso 120, which includes various organs, muscles, nerves, and other tissues, such as, but not limited to, those in a chest region 122 (e.g., lungs 126, heart 127), an abdomen 124, and / or a pelvic region 129 (e.g., urinary tract / bladder, anus, genitals, etc.). As further shown in Figure 1B, the patient's body 100 includes limbs 130, such as arms 132 and legs 134.

[0016] It will be appreciated that the various sensing elements (and / or stimulation elements) described throughout various examples of the present disclosure may be positioned in various regions of the patient's body 100 to sense and / or otherwise diagnose, monitor, or treat various physiological conditions, such as, but not limited to, the examples described below in connection with Figures 2-42. In some such examples, the stimulation element 117 may be positioned in or near the upper airway 116 to treat sleep-disordered breathing (and / or near other nerves / muscles to treat other conditions), and / or the sensing element 128 may be positioned anywhere in the neck 114 and / or torso 120 (or other body region) to sense physiological information (e.g., SDB, etc.) for providing patient care, including, but not limited to, sleep onset detection and related parameters.

[0017] In some examples, at least a portion of the stimulation element 117 may include a portion of an implantable component / device, such as an implantable pulse generator (IPG), whether full-sized or sized as a microstimulator. The implantable component (e.g., IPG, etc.) may include stimulation / control circuitry, a power source (e.g., non-rechargeable, rechargeable), communication elements, and / or other components. In some examples, the stimulation element 117 may also include stimulation electrodes and / or stimulation leads connected to the implantable pulse generator.

[0018] Further details regarding the location, structure, operation, and / or use of sensing element 128, external element 150, and / or stimulation element 117 are described below in conjunction with at least Figures 1C-42, and particularly at least Figures 31A-31F.

[0019] In some examples, at least a portion of the stimulation element 117 may include a portion of an external component / device, such as, but not limited to, an external component including a pulse generator (e.g., stimulation / control circuitry), a power source (e.g., rechargeable, non-rechargeable), and / or other components. In some examples, a portion of the stimulation element 117 may be implantable and a portion of the stimulation element 117 may be external to the patient.

[0020] Thus, as further shown in FIG. 1B, various sensing elements 128 and / or stimulation elements 117 implanted in the patient's body may be in wireless communication (e.g., connection 137) with at least one external element 150.

[0021] As further shown in FIG. 1B , in some examples, external element 150 may be implemented via a wide variety of formats, such as at least one of formats 151, including, but not limited to, patient support 152 (e.g., bed, chair, sleep mat, etc.), wearable element 154 (e.g., finger, wrist, head, neck, shirt), non-contact element 156 (e.g., watch, camera, mobile device, etc.), and / or other element 158.

[0022] 1B , in some examples, external element 150 may include one or more different modalities 170, such as (but not limited to) sensing portion 171, stimulation portion 172, power portion 174, communication portion 176, and / or other portions 178. The different portions 171, 172, 174, 176, 178 may be combined in a single physical structure (e.g., package, arrangement, assembly), may be implemented in multiple different physical structures, and / or only some of the different portions 171, 172, 174, 176, 178 are combined together in a single physical structure.

[0023] Among other such details, in some examples, the external sensing portion 171 and / or the implantable sensing element 128 may include at least some of substantially the same features and attributes of the sensing portion 2000 and / or the care engine 2500, as further described below in Figures 30A and 32, respectively.

[0024] In some examples, the external stimulation portion 172 and / or the implantable stimulation element 117 may include at least some of the substantially same features and attributes of the stimulation arrangements as further described below in connection with at least Figures 31A-31F, 32, and / or other examples throughout this disclosure.

[0025] In some examples, the external power portion 174 and / or power components associated with the implantable stimulation element 117 may include at least some of substantially the same features and attributes of the stimulation arrangements as further described below in connection with at least Figures 31A-31F, 32 and / or other examples throughout this disclosure. In some such examples, each power portion, component, etc. may include rechargeable power elements (e.g., supplies, batteries, circuit elements) and / or non-rechargeable power elements (e.g., batteries). In some examples, the external power portion 174 may include a power source by which the power components of the implanted stimulation element 117 may be recharged.

[0026] In some examples, the wireless communication portion 176 (e.g., the connection / link at 137) may be implemented via various forms of wireless 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), and / or other short or long range wireless communication protocols suitable for use in communicating between implantable and external components in a medical device environment.

[0027] Examples are not limited to those represented by other portions 178, through which other aspects of providing medical care may be embodied in external element 150 and related to the various implantable and / or external components described above.

[0028] FIG. 1C schematically represents control portion 190, which may include at least some of the substantially same features and attributes of control portion 4000 in FIG. 34A and care engine 2500 in FIG. 32. In some examples, control portion 190 is part of a care engine (e.g., 2500) or the like. Among other aspects, example methods and / or example devices may be implemented via control portion 190. In some examples, control portion 190 may be used to implement at least some of the various example devices and / or example methods of the present disclosure described herein. In some examples, control portion 190 may form part of and / or be in communication with sensing element 128 and / or stimulation element 117 in FIG. 1B, external element 150, and / or other medical devices (or portions thereof), as further described below.

[0029] In various examples, control portion 190 is programmed to detect a second instance of sleep onset within the nighttime treatment period according to a second criterion that is different from the first criterion associated with the first instance of sleep onset during the nighttime treatment period, as represented by 50 in FIG. 1A. In various other examples, control portion 190 is programmed to detect early sleep onset, wake after sleep onset (WASO), and sleep onset after WASO using the same or different inputs, sleep-related signals, parameters, etc., and associated thresholds, as further described below with reference to the following figures.

[0030] 2 is a schematic diagram illustrating a timeline 210 of sleep-wake related events according to an exemplary method 200 of sleep-wake determination, such as may occur during sleep-disordered breathing (SDB) care (e.g., monitoring, diagnosis, treatment, etc.). In some examples, the exemplary SDB care may include at least some of the same features and attributes as the exemplary SDB care methods and / or devices (including sleep-wake detection) described in connection with FIGS. 1A-42. As shown in FIG. 2, the timeline 210 includes a series of wake and sleep periods, with a wake period 220 occurring immediately before a first sleep stage period 240 (e.g., stage 1). The wake period 220 in FIG. 2 may represent the end portion of a wake period extending from the end of the previous night's sleep, or may represent a separate wake period.

[0031] As further represented by indicator 235, the actual physiological transition occurs between the wake period 220 and the first sleep stage 240, and indicator 243 represents the detection of sleep (also referred to as early sleep onset) according to examples of the present disclosure. As shown in FIG. 2, the detection of sleep (243) may occur immediately after the physiological transition 235.

[0032] In some examples, detection of sleep 243 may trigger a delay period 245 before initiation of therapy (e.g., electrical stimulation). In some such examples, the duration of the delay generally corresponds to a sufficient amount of time for the patient to experience sufficient sound sleep so that the patient is not awakened by the onset of stimulation. Furthermore, in some examples, once stimulation begins, it may be implemented in a ramped fashion 246, with an initial lower stimulation intensity that gradually increases until a target stimulation intensity 247 is achieved to therapeutically provide electrical stimulation to upper airway patency-related tissue.

[0033] As previously described in this disclosure, at least some example implementations of method 200 in FIG. 2 may include identifying, maintaining, and / or optimizing a target stimulation intensity (e.g., a therapeutic level) without intentionally identifying a stimulation discomfort threshold at the time of implantation or at a later point in time after implantation.

[0034] As further shown in Figure 2, once the target stimulation intensity is achieved, it can be maintained throughout the treatment period.

[0035] In some examples, the target stimulation intensity may be automatically adjusted (e.g., auto-titrated) during the treatment period. In some such examples, the automatic adjustment of the target stimulation intensity may be implemented according to at least some of the substantially same features and / or attributes as described in connection with at least the auto-titration parameters 2920 in FIG. 32 .

[0036] 2, after a period of time (which may vary from night to night), the patient may experience an arousal phase 260 during the treatment period, which interrupts a sleep stage (e.g., second sleep stage (S2) 250 in this example). The exemplary method 200 detects an arousal 262 (also referred to as wake after sleep onset (WASO)), which may extend for a period of time (W1) before the patient returns to sleep, represented by, for example, sleep stages 270 and transitions 265 between the respective arousal periods 260 and sleep stages 270 (also referred to as sleep onset after WASO).

[0037] In some examples, method 200 may suspend stimulation entirely during wake periods 260, or alternatively, in some examples, method 200 may implement reduced therapy 264 during wake periods 260 in the expectation that the patient will return to sleep and full stimulation therapy will be resumed. In some such examples, the reduced therapy at 264 may include providing stimulation at a functional threshold (FT), which corresponds to the minimum amplitude at which the stimulation causes the tongue to protrude at least partially past the lower teeth and at which a therapeutic outcome (e.g., a reduction in apnea) can be achieved. However, in some such examples, the reduced therapy at 264 may include providing stimulation at a sensing threshold (ST), which includes a stimulation intensity below the stimulation intensity to reach the functional threshold (FT). The sensing threshold (ST) may correspond to the minimum amplitude at which the patient can sense the stimulation.

[0038] The therapy may be automatically resumed, as shown at 272 in Figure 2. It will be understood that, in at least some examples, the resumption of therapy 272 may include substantially the same features and attributes as the initiation of therapy, as previously described in connection with indicators 243, 246, 247 in Figure 2, including detection of sleep 243 to determine a sleep-wake state, in accordance with at least some examples of the present disclosure.

[0039] In some examples, in general terms, the onset of the first sleep stage 240 generally corresponds to the start of a treatment period during which the patient may be treated for sleep disordered breathing and / or a method (and / or device) may monitor for or diagnose sleep disordered breathing.

[0040] More specific, exemplary methods, devices, and / or arrangements for determining sleep-wake states (including determining initial sleep onset, wake after sleep onset, and sleep onset after WASO and related information) are described and illustrated in connection with at least FIGS. 3A-42.

[0041] 3A is a chart 282 illustrating thresholds for measured physiological signals / information for determining a sleep-wake state. As described further below, in some examples, the physiological signals may be obtained from accelerometer signals and / or other sensing modalities. However, in some examples, the accelerometer signals may be suitable for certain types of physiological signals / information that are particularly relevant to sleep-wake determination, such as (but not limited to) activity / movement, body position (e.g., posture), and the like. This type of information may be obtained via accelerometer signals in addition to accelerometer signals used to sense other physiological signals / information that are also suitable for sleep-wake determination, such as (but not limited to) at least respiratory and / or cardiac signals / information, among other purposes. Thus, in some examples, the measured physiological signals may include at least some physiological signals / information obtained from accelerometer signals and / or at least some physiological signals / information obtained from other sensing modalities, as described further below.

[0042] A measurement signal indicated at 284, which indicates the patient's sleep-wake state (e.g., 100 in FIG. 1B ), may be obtained (e.g., via sensing element 128 in FIG. 1B and / or external sensors 171, 150 in FIG. 1B ) throughout the night and / or sleep period (e.g., treatment period). Measurement signal 284 may be compared to a first threshold indicated at 286 to detect early sleep onset. With measurement signal 284 exceeding first threshold 286 at time T1, the patient is determined to be awake. With measurement signal 284 falling below first threshold 286 at time T2, the patient is determined to be asleep, indicating early sleep onset throughout the night and / or sleep period. In response to detecting early sleep onset, in some examples, stimulation may be activated as previously described with reference to FIG. 2 .

[0043] After initial sleep onset is detected at time T2, measurement signal 284 may be compared to a second threshold, as shown at 288, to detect wake after sleep onset (WASO). The second threshold 288 may be higher than the first threshold 286. With measurement signal 284 below the second threshold 288 between times T2 and T3, the patient is determined to be asleep even when measurement signal 284 rises above the first threshold 286, as shown just before time T3. If measurement signal 284 rises above the second threshold 288 at time T3 (e.g., due to a macro-arousal state, as described below), the patient is determined to be awake, indicating wakefulness after sleep onset throughout the night and / or sleep period. In response to detecting wakefulness after sleep onset, in some examples, stimulation may be paused as previously described with reference to FIG. 2.

[0044] It will be appreciated that once a patient awakens, they may remain awakened for a period of time (e.g., 1, 2, 5, 10, or 15 minutes) before attempting to return to sleep. For illustrative simplicity, the measurement signal 284 in FIG. 3A schematically represents awakening periods (above threshold 228) as a single or few data points, even though in reality awakening periods may last at least several minutes or longer in some instances.

[0045] After wake after sleep onset (WASO) is detected at time T3, measurement signal 284 may be compared to a third threshold indicated at 290 to detect sleep onset after WASO. In some examples, third threshold 290 may be lower than second threshold 288. In other examples, third threshold 290 may be equal to second threshold 288. Third threshold 290 may be higher than first threshold 286 because falling asleep in the middle of a night or sleep period after waking is typically easier / quicker than falling asleep initially at the beginning of a night or sleep period. With measurement signal 284 above third threshold 290 between times T3 and T4, the patient is determined to be awake. If measurement signal 284 falls below third threshold 290 at time T4, the patient is determined to be asleep, indicating sleep onset after WASO over the night and / or sleep period. In response to detecting sleep onset after WASO, in some instances, stimulation may be resumed as previously described with reference to FIG. 2.

[0046] As shown between times T5 and T6 and between times T7 and T8, measurement signal 284 rises above first threshold 286 (e.g., due to a micro-arousal state described below), but the patient is still determined to be asleep because measurement signal 284 does not rise above second threshold 288, indicating wakefulness after sleep onset. At the end of the night or sleep period, occasionally after time T8, measurement signal 284 may rise above second threshold 288, indicating the patient is wakeful. In response to detecting a wakefulness state at the end of the night or sleep period, in some examples, stimulation may be deactivated until the next night or sleep period.

[0047] As described further below, in some examples, first threshold 286, second threshold 288, and third threshold 290 may be initially selected based on a general population model, and in some examples, first threshold 286, second threshold 288, and third threshold 290 may then be adjusted based on patient feedback to individualize the thresholds for the patient.

[0048] While FIG. 3A illustrates a single measurement signal and thresholds for a single measurement signal for detecting early sleep onset, wake after sleep onset (WASO), and sleep onset after WASO, in other examples, multiple measurement signals and corresponding thresholds for each of multiple measurement signals may be used in combination to detect early sleep onset, wake after sleep onset, and sleep onset after WASO, as described further below. Also, while a higher measurement signal 284 in FIG. 3A indicates wakefulness and a lower measurement signal 284 in FIG. 3A indicates drowsiness, in other examples, a higher measurement signal may indicate drowsiness and a lower measurement signal may indicate wakefulness. In this case, the first, second, and third thresholds may be reversed. That is, the first threshold may be greater than the second and third thresholds, and the second threshold may be less than the third threshold.

[0049] 3B is a chart 292 showing time-dependent thresholds for determining sleep-wake states for measured physiological signals / information. In some examples, time-dependent thresholds may be used because the probability of falling asleep increases as a patient attempts to sleep for a period of time or increases if the patient has already slept for a given amount of time. The longer a patient attempts to fall asleep, the more likely the patient will eventually fall asleep. Furthermore, given that a patient has been asleep for a certain given amount of time, if a wakefulness occurs after sleep onset, it is then easier for the patient to fall back asleep. Thus, the time-dependent thresholds may take these probabilities into account.

[0050] A measurement signal indicated at 294 is indicative of the patient's sleep-wake state (e.g., 100 in FIG. 1B) and may be obtained throughout the night and / or sleep period (e.g., via sensing element 128 in FIG. 1 and / or external sensors 171, 150 in FIG. 1B). Measurement signal 294 may be compared to a time-dependent threshold, as indicated at 296, to detect initial sleep onset, wakefulness after sleep onset, and / or sleep onset after WASO. Time-dependent threshold 296 is initially at a minimum value at the beginning of the night and / or sleep period, increases from the minimum to a maximum value, and then decreases from the maximum value back to the minimum value by the end of the night and / or sleep period.

[0051] With measurement signal 294 exceeding time-dependent threshold 296 at time T1, the patient is determined to be awake. With measurement signal 294 falling below time-dependent threshold 296 at time T2, the patient is determined to be asleep, indicating initial sleep onset over the night or sleep period. With measurement signal 294 falling below time-dependent threshold 296 between times T2 and T3, the patient is determined to be asleep. If measurement signal 294 rises above time-dependent threshold 296 at time T3 (e.g., due to a macro-arousal state described below), the patient is determined to be awake, indicating wakefulness after sleep onset over the night or sleep period. With measurement signal 294 exceeding time-dependent threshold 296 between times T3 and T4, the patient is determined to be awake. As previously discussed in connection with FIG. 3A , it will be appreciated that once the patient is awake, the patient may remain awake for a period of time (e.g., 1, 2, 5, 10, or 15 minutes) before attempting to return to sleep. For illustrative simplicity, the measurement signal 294 in FIG. 3B schematically represents wake periods (above threshold 296) as single or few data points, although in reality wake periods may, in some instances, last at least several minutes or longer.

[0052] If measurement signal 294 falls below time-dependent threshold 296 at time T4, the patient is determined to be asleep, indicating sleep onset after WASO over the night or sleep period. With measurement signal 294 below time-dependent threshold 296 between times T4 and T7, the patient is determined to be asleep. Note that the patient would have been determined to be awake based on threshold 296 at time T1, at least between times T5 and T6. However, because threshold 296 is higher between times T5 and T6 than at time T1, the patient is determined to be asleep between times T5 and T6. Once measurement signal 294 rises above time-dependent threshold 296 at time T7, the patient is determined to be awake, which may be at or near the end of the night or sleep period.

[0053] In some examples, a time-dependent threshold 296 may be used for the first threshold 286, the second threshold 288, and / or the third threshold 290 in FIG. 3A . While FIG. 3B illustrates a single measurement signal and a time-dependent threshold for the single measurement signal to detect early sleep onset, wake after sleep onset, and sleep onset after WASO, in other examples, multiple measurement signals and corresponding time-dependent thresholds for each of the multiple measurement signals may be used in combination to detect early sleep onset, wake after sleep onset, and sleep onset after WASO, as described further below. Also, a higher measurement signal 294 in FIG. 3B indicates wakefulness, and a lower measurement signal 294 in FIG. 3B indicates drowsiness; in other examples, a higher measurement signal may indicate drowsiness, and a lower measurement signal may indicate wakefulness. In this case, the time-dependent threshold 296 may be inverted, such that the time-dependent threshold is initially at a maximum value at the beginning of the night or sleep period, decreases from the maximum value to a minimum value, and then increases from the minimum value back to the maximum value by the end of the night and / or sleep period.

[0054] 4A-4E are diagrams schematically illustrating an example method 300 for determining a sleep-wake state. Method 300 may be implemented by stimulation element 117, sensing element 128, external element 150, and / or control portion 190 of FIGS. 1B-1C. As illustrated in FIG. 4A at 302, method 300 may include receiving, via the control portion (e.g., 190), a plurality of inputs, each input corresponding to a different sleep-wake determination parameter. At 304, method 300 may include detecting, via the control portion, early sleep onset based on at least a first subset of the plurality of inputs and a first threshold value for the at least the first subset of the plurality of inputs. At 306, method 300 may include detecting wake after sleep onset (WASO) based, via the control portion, on at least a second subset of the plurality of inputs and a second threshold value for the at least the second subset of the plurality of inputs. At 308, method 300 may include detecting sleep onset after WASO based on at least a third subset of the plurality of inputs and a third threshold for the at least third subset of the plurality of inputs, the third threshold being different from the first threshold and the second threshold.

[0055] In some examples, the first threshold and / or the third threshold are time-dependent (e.g., as shown by 296 in FIG. 3B ). For example, during a sleep period, the third threshold may initially be at a minimum value at the beginning of the sleep period, increase from the minimum value to a maximum value, and then decrease from the maximum value back to the minimum value by the end of the sleep period. In some examples, the second subset of the plurality of inputs is the same as the first subset of the plurality of inputs. In other examples, the second subset of the plurality of inputs is different from the first subset of the plurality of inputs. For example, the first subset of the plurality of inputs may include at least one of heart rate variability or body temperature, and the second subset of the plurality of inputs may include at least one of activity or locomotor inactivity during sleep (LIDS).

[0056] In some examples, the first subset of the plurality of inputs is the same as the third subset of the plurality of inputs. In other examples, the first subset of the plurality of inputs may be different from the third subset of the plurality of inputs. In some examples, the third subset of the plurality of inputs may include at least one of activity, locomotor inactivity during sleep (LIDS), a light sensor signal, or time elapsed since initial sleep onset.

[0057] In one example, the plurality of inputs may include an accelerometer sensor signal, such as an accelerometer angle. In some examples, the plurality of inputs may include at least one of the following: physiological signals / information obtained from an accelerometer sensor signal or sensing modality, environmental signals, time, or patient information. In some examples, the plurality of inputs may include at least one of the following physiological signals / information: respiratory signals / information (e.g., respiratory rate, respiratory rate variability), electromyography (EMG), microneurography, cardiac signals / information (e.g., heart rate, heart rate variability), body temperature, posture, activity, or locomotor inactivity during sleep (LIDS). In still further examples, the plurality of inputs may include at least one of the following: geographic location, proximity to a sleep area (e.g., in a bedroom, in a bed, etc.), a light sensor signal, a noise sensor signal, a motion sensor signal (e.g., physiological signals / information), proximity and / or charge status of the patient's electronic device (e.g., proximity and / or charge may indicate nighttime or sleep periods), time elapsed since initial sleep onset (e.g., physiological signals / information, in some examples), or the patient's circadian rhythm (e.g., physiological signals / information). In still further examples, the plurality of inputs may include at least one of the following patient inputs: food intake, meal timing, indicated sleepiness, demographics, or co-morbidities. In still further examples, the plurality of inputs may include any suitable combination of the inputs described above.

[0058] In some such examples, at least a portion of the input (e.g., physiological signals, associated physiological information, etc.) may be obtained via accelerometer signals, such as, but not limited to, respiratory information (e.g., speed, speed variability), cardiac information (e.g., heart rate, heart rate variability), activity, exercise, body position (e.g., posture), LIDS, movement, and / or other physiological signals and associated physiological information. The accelerometer signals may be obtained from an implantable accelerometer (e.g., forming part of the stimulating element 117 or sensing element 128 of FIG. 1B) and / or from an external accelerometer (e.g., 171, 150 of FIG. 1B).

[0059] Inputs related to activity, movement, body position, and motion can relate to patterns in accelerometer signals over a period of time. LIDS is an example of this, where activity counts during a window of time are determined, the quantity 100 / (1 + activity count) is calculated, and then smoothed with an averaging filter. A high value of LIDS corresponds to a sustained low level of activity over a period of time, which indicates the onset of sleep. In another example, activity counts can be weighted by the magnitude of each movement. In this way, a few large movements provide as much indication of wakefulness as a larger number of small movements. In another example, activity counts can include small movements over a short time window and large movements over a long time window. In this way, the threshold can be exceeded only for a long time after large movements or for a short time after smaller movements. This can reflect how a patient's movements decrease in magnitude as they approach sleep onset.

[0060] Similarly, the input can relate to patterns of activity over a period of time that are associated with arousal. A single, large movement may correspond to either mild arousal or arousal, but repeated, large movements are more commonly associated with arousal. Thus, the input can include an activity count over a period of time. To identify sustained movement instead of bursts of movement, the input can include a count of consecutive periods in which at least one large movement occurs.

[0061] 4B at 310, method 300 may further include automatically initiating electrical stimulation via an electrode (e.g., implantable or external) to upper airway patency-associated nerves in response to detecting early sleep onset or sleep onset after WASO. At 312, method 300 may further include automatically pausing electrical stimulation via the electrode in response to detecting WASO.

[0062] As illustrated at 314 in FIG. 4C , method 300 may further include setting the first threshold, the second threshold, and the third threshold based on a general population model. As illustrated at 316 in FIG. 4D , method 300 may further include adjusting the first threshold, the second threshold, and the third threshold based on patient feedback. In some examples, the patient feedback may include at least one of a survey, manual control of electrical stimulation, polysomnography, a home sleep study, a wearable device, or a sleep mat. As illustrated at 318 in FIG. 4E , method 300 may further include applying a different weighting value to each of the multiple inputs. The different weighting values ​​may be applied to each of the multiple inputs such that each input is considered relatively more or relatively less in comparison to the other inputs when detecting early sleep onset, wake after sleep onset (WASO), and sleep onset after WASO. In some such examples, the relative weights applied to inputs may be based on confidence factors such as the accuracy, reliability, and / or other factors of a particular input generally and / or under particular circumstances.

[0063] In some examples, the available inputs may vary depending on whether the patient is sleeping at home or remotely (e.g., in a hotel), which may be sensed. In some such examples, an example method and / or device may first determine which inputs are available and then select and / or place more weight on the currently available input with the highest confidence factor.

[0064] In one specific example of method 300 described with reference to FIGS. 4A-4E, a patient can go to bed (or lie down), enabling early sleep onset detection. In some examples, early sleep onset detection can be enabled in response to detecting the patient's intent to sleep. The intent to sleep can be based on input from internal sensors (e.g., accelerometer, gyroscope, microphone), external sensors (e.g., accelerometer, light sensor, motion sensor, sleep mat, wearable device, air pressure sensor, low-power radar sensor, etc.), remote devices (e.g., 4030 in FIG. 34B or 4340 in FIG. 36), and / or a user interface (e.g., 4040 in FIG. 35). The patient reads in bed for 15 minutes, and movement around them (e.g., of a first subset of inputs) prevents sleep onset detection. The patient turns off the lights and goes to sleep. A decrease (e.g., below a corresponding first threshold) in their heart rate variability (HRV) (e.g., of a first subset of inputs) and body temperature (e.g., of a first subset of inputs) ultimately results in sleep onset detection, indicating that the patient is asleep. In response to detecting sleep onset, a stimulus may be activated, and wake after sleep onset (WASO) detection may be enabled. The patient awakens mid-night due to noise / external circumstances and attempts to go back to sleep. Their heart rate variability is still fairly low, and their body temperature is still low, so sleep can still be detected by initial sleep onset detection. However, the patient's locomotor inactivity during sleep (LIDS) standard deviation (e.g., of a second subset of inputs) increases during this period (e.g., above a corresponding second threshold), which results in WASO detection. In response to detecting a WASO, stimulation may be paused until the LIDS standard deviation (e.g., of a third subset of inputs) again decreases (e.g., falls below a corresponding third threshold) and sleep onset after the WASO is detected. In response to detecting sleep onset after the WASO, stimulation may be resumed.

[0065] In another specific example of method 300 described with reference to FIGS. 4A-4E, a patient may go to bed (or lie down) around 10:00 PM, enabling early sleep onset detection. The patient reads in bed for 15 minutes, and activity around them prevents sleep onset detection. The patient turns off the lights and goes to sleep. A decrease in their heart rate variability (HRV) and body temperature ultimately leads to sleep onset detection, indicating the patient is asleep. In response to detecting sleep onset, a stimulus may be activated, enabling time-dependent threshold-based wake-after-sleep-onset (WASO) detection. The patient awakens at 2:00 AM due to noise / external conditions and attempts to go back to sleep. Four hours have passed since going to bed, and ambient light is low because it is before sunrise. These factors contribute to a higher threshold leading to sleep onset detection after WASO, earlier than early sleep onset detection if the patient goes to bed at 10:00 PM.

[0066] In yet another specific example of method 300 described with reference to FIGS. 4A-4E, a patient can go to bed (or lie down) around 1:00 AM, enabling early sleep onset detection. The patient reads in bed for 15 minutes, and activity in their surroundings prevents sleep onset detection. The patient turns off the lights and goes to sleep. A decrease in their heart rate variability (HRV) and body temperature ultimately leads to sleep onset detection, indicating the patient is asleep. In response to detecting sleep onset, a stimulus can be activated, enabling wake-after-sleep-onset (WASO) detection based on a time-dependent threshold. The patient wakes at 5:00 AM due to noise / external conditions and attempts to go back to sleep. Four hours have passed since they went to bed, and ambient light is high because it is close to sunrise. These factors contribute to a lower threshold that leads to sleep onset detection after WASO, similar to early sleep onset detection if the patient went to bed at 1:00 AM. This is because daylight and the patient's circadian rhythm can make it more difficult to return to sleep.

[0067] The patient's circadian rhythm may include the patient's bedtime, sleep duration, and wake time. The patient's circadian rhythm may be used alone or in combination with other inputs to determine sleep-wake states (e.g., initial sleep onset, wake after sleep onset, and / or sleep onset after WASO), which may be used to activate and / or deactivate stimuli. In some examples, the patient's circadian rhythm may be determined based on demographics (e.g., age, gender), medical history (e.g., insomnia, depression, etc.), questionnaires (e.g., regarding current sleep habits, work schedule, etc.), temperature, geographic location (e.g., sunrise / sunset), external sensors (e.g., sleep / wake time patterns, lighting, mobile device use), physiological signals / information (e.g., heart rate variability), last night's sleep duration, work hours and activity level, food and beverage intake / timing, and / or the Unified Performance Model (UMP).

[0068] The sensor used to determine the patient's circadian rhythm can be part of a stimulation element (e.g., 117 in FIG. 1B), an internal sensor (e.g., 128 in FIG. 1B), or an external element (e.g., 171, 150 in FIG. 1B), such as a stimulation element remote, a mobile device, a wearable device (e.g., a smart watch), a device near the patient (e.g., a sleep mat), etc.

[0069] The patient's circadian rhythm can be used to initiate automatic sleep detection and / or automatically activate and / or deactivate stimulation. In some examples, a combination of manual and automatic control based on the patient's circadian rhythm can be used where 1) the patient manually activates stimulation, but the stimulation is automatically deactivated based on a predicted wake time, and / or 2) the device automatically activates stimulation based on a predicted sleep time, but the patient manually deactivates the stimulation. In some examples, the patient's circadian rhythm can also be used to provide relaxation and / or bedtime suggestions to the patient.

[0070] Stimulation may be automatically activated and deactivated based on predicted circadian rhythm cycles determined by a model (e.g., a data model, such as (but not limited to) a machine learning model), as described below with reference to FIGS. 41-42. The model may receive multiple inputs and predict sleep and wake times based on the multiple inputs. A generalized model may be developed first, which performs acceptably for a general population of patients with the same inputs / outputs as described above. The model may then be individualized (e.g., calibrated) based on individual patients, geography, daily activities, and the like. Patients may input their own information (e.g., demographics, sleep habits, socioeconomic, geographic) into the model via a user interface. External sensor data (e.g., accelerometer, mobile device, light sensor, GPS, air pressure sensor, low-power radar sensor, etc.) may also be used to provide input data to the model. Internal sensors (e.g., accelerometer, gyroscope, microphone, etc.) of the stimulation element (e.g., pulse generator) may also be used to provide input data to the model.

[0071] The model may generate predictions of ideal times for sleep and wakefulness. The model may be implemented externally in an implanted medical device, an external device near the patient, or a cloud-based application. In some examples, the model may be accessed via an application programming interface (API). The model predictions may be used to make recommendations to the patient, motivate the patient, educate the patient via a user interface about their ideal bedtime and wake-up time, provide diagnostic data for a physician via a user interface, and serve as input into a sleep detection algorithm to adjust (e.g., calibrate) detection thresholds and / or adjust (e.g., calibrate) stimulation activation and / or deactivation times.

[0072] 5A-5B are diagrams schematically illustrating another exemplary method 330 for determining a sleep-wake state. The method 330 may be implemented by a medical device (e.g., the implantable medical device 117 of FIG. 1B ) including at least one sensor for sensing physiological information (e.g., 128 of FIG. 1B and / or 171, 150 of FIG. 1B ) and a control portion (e.g., 190 of FIG. 1C ). In some examples, the at least one sensor for sensing physiological information may include at least one of an accelerometer sensor or a temperature sensor. In some examples, the at least one sensor for sensing physiological information is implantable. In some examples, the at least one sensor for sensing physiological information is external to the patient's body. In some examples, the medical device further includes at least one sensor external to the patient's body for sensing environmental information. The at least one sensor for sensing environmental information may include at least one of a light sensor, a noise sensor, or a motion sensor.

[0073] As illustrated in FIG. 5A at 332, the control portion may receive a plurality of inputs, each corresponding to a different sleep-wake determination parameter, where the plurality of inputs includes sensed physiological information. At 334, the control portion may detect early sleep onset based on at least a first subset of the plurality of inputs and a first threshold for the at least the first subset of the plurality of inputs. At 336, the control portion may detect wake after sleep onset (WASO) based on at least a second subset of the plurality of inputs and a second threshold for the at least the second subset of the plurality of inputs. In some examples, the second subset of the plurality of inputs may be different from the first subset of the plurality of inputs. At 338, the control portion may detect sleep onset after WASO based on at least a third subset of the plurality of inputs and a third threshold for the at least the third subset of the plurality of inputs, where the third threshold is different from the first and second thresholds.

[0074] In some examples, the medical device may further include a pulse generator (e.g., 117 in FIG. 1B) for applying electrical stimulation. In this case, as illustrated in FIG. 5B at 330, the control portion may further automatically initiate a first function of the pulse generator in response to detecting early sleep onset or sleep onset after WASO. At 342, the control portion may further automatically pause the first function of the pulse generator in response to detecting WASO. In some examples, the pulse generator includes an implantable pulse generator. In some examples, the first function of the pulse generator is to apply electrical stimulation to upper airway patency-related nerves.

[0075] In some examples of method 330, the first threshold and / or the third threshold are time-dependent. For example, during a sleep period, the third threshold may initially be at a minimum value at the beginning of the sleep period, increase from the minimum value to a maximum value, and then decrease from the maximum value back to the minimum value by the end of the sleep period.

[0076] 6A-6F are schematic diagrams illustrating yet another exemplary method for determining sleep-wake states (e.g., early sleep onset, wake after sleep onset, and / or sleep onset after WASO). FIGS. 6A-6F may include at least some of the substantially same features and attributes as previously described with reference to FIGS. 2-5B. As illustrated in FIG. 6A at 400, the method may include detecting a second instance of sleep onset within the nighttime treatment period according to at least one second sleep-related signal different from the at least one first sleep-related signal. For example, the first sleep-related signal may include heart rate variability, and the second sleep-related signal may include locomotor inactivity during sleep (LIDS).

[0077] As illustrated in FIG. 6B at 402, the method may include detecting a second instance of sleep onset within the nighttime treatment period according to at least one second sleep-related signal that is different from and in addition to the at least one first sleep-related signal.

[0078] As illustrated in FIG. 6C at 404, the method may include detecting a second instance of sleep onset within the nighttime treatment period by varying the threshold based on the time elapsed since initial sleep onset.

[0079] As illustrated in FIG. 6D at 406, the method may include detecting wake after sleep onset (WASO) within the nighttime treatment period according to a second criterion different from a first criterion associated with a first instance of sleep onset during the nighttime treatment period.

[0080] As illustrated in FIG. 6E at 408, the method may include detecting WASO within the nighttime treatment period according to at least one second sleep-related signal that is different from the at least one first sleep-related signal.

[0081] As illustrated in FIG. 6F at 410, the method may include detecting WASO within the nighttime treatment period according to at least one second sleep-related signal that is different from and in addition to the at least one first sleep-related signal.

[0082] 7 is a diagram that schematically illustrates yet another exemplary method 420 for determining a sleep-wake state. At 422, the method 420 includes adjusting a sleep threshold associated with a first sleep-related signal once early sleep onset is detected. At 424, the method 420 includes comparing the first sleep-related signal to the adjusted threshold. At 426, the method 420 includes detecting sleep onset after wake after sleep onset (WASO) based on the comparison.

[0083] Various additional aspects of determining a sleep-wake state (e.g., early sleep onset, wake after sleep onset, and / or sleep onset after WASO) based on sleep-wake determination parameters and corresponding sleep-wake thresholds are further described below in connection with at least Figures 8-42 of the present disclosure.

[0084] As schematically represented at 520 in FIG. 8 , in some examples, sensing physiological information may include sensing at or movement of the chest, neck, and / or head, which may then be used to determine a sleep-wake state. At least some aspects of such determination are further described in connection with FIGS. 30A-32 . For example, sensing portion 2000 in FIG. 30A and / or care engine 2500 in FIG. 32 (including, but not limited to, sensing portion 2510) may include multiple sensor types, modalities, etc., at least some of which may be used to sense at or movement of the chest, neck, and / or head and to determine a sleep-wake state (e.g., detect sleep) using such sensed movement. One such exemplary modality may include using an accelerometer to sense movement at the chest, neck, and / or head, as further described below. In some examples, the accelerometer may be implanted in the chest, neck, and / or head, as described at least in connection with FIG. 1B, while in some examples the accelerometer may be fixed externally on the patient's body in such locations or may be present as part of a patient support, sleep mat, wearable, etc.

[0085] Sensed motion at the chest, neck, and / or head may include motion of the chest, neck, and / or head, or motion phenomena at their respective locations, without necessarily including gross motion of the chest, neck, and / or head, as further described below in connection with at least Figures 31A-31F. In one non-limiting example, sensing motion phenomena at the neck or other locations may include sensing blood circulation within the blood vessel / vascular system (e.g., arterial motion within a blood vessel). In some such examples, the sensing element (e.g., accelerometer, impedance, etc.) may be at least partially incorporated into a microstimulator (or other implantable pulse generator) sized and shaped for implantation within a blood vessel. An exemplary method may include sensing ballistic motion of a blood vessel caused by the patient's heartbeat. In some cases, the blood vessels may include the external jugular vein, and therefore, the motion sensing does not necessarily involve neck movement (e.g., bending, tilting, twisting, etc.), but may in some cases occur in the neck.

[0086] As represented schematically at 528 in FIG. 9A , in some examples, a method for determining a sleep-wake state may be implemented using sensed posture information and / or body position information. Sensed posture information may include static posture or may include changes in posture, which may be considered forms of gross body movement referred to above. As noted elsewhere, sensed posture may be used to help determine whether a patient is likely asleep (e.g., lying down) or awake (e.g., standing), which may be in combination with other sensed information (e.g., heart rate, respiratory rate, etc.).

[0087] As represented diagrammatically at 530 in FIG. 9B, in some examples, the method for determining a sleep-wake state may be performed without utilizing posture and / or body position information.

[0088] For example, a patient may occasionally fall asleep intentionally (or unintentionally) while sitting in a chair or airplane seat and would benefit from SDB care (e.g., neurostimulation therapy). In such instances, determining the sleep-wake state without using posture information may enhance faster or more accurate detection of sleep for patients sleeping in a sitting position because the exemplary method may avoid false negative indications (due to posture-based determinations) that the patient is awake.

[0089] Conversely, patients may occasionally intentionally wake up when lying horizontally and therefore do not wish to receive SDB care. In such instances, determining the sleep-wake state without using posture information may enhance faster or more accurate detection of sleep for patients who are awake in a recumbent position because the exemplary method avoids false positive indications (due to posture-based determinations) that the patient is asleep because they are lying in a horizontal position typically associated with sleep.

[0090] As schematically represented at 535 in FIG. 9C , in some examples, a method for determining a sleep-wake state includes sensing at least one of a first type of physiological signal / information (e.g., a respiratory signal, from which a respiratory rate and / or other information may be derived, and / or a cardiac signal, from which a heart rate and / or other information may be derived) and a second type of physiological signal / information (e.g., body movement), and performing a sleep-wake state determination at least via the respective first type of sensed physiological signal / information and / or the second type of sensed physiological signal / information. In some examples, the sensed body movement may correspond to the sensed movement in FIG. 8 . Various aspects of such sleep-wake state determination based on sensed physiological information are further described at least in connection with FIGS. 30A-32 and elsewhere throughout various examples of the present disclosure.

[0091] In some examples, detecting sleep (and / or wakefulness) in conjunction with administering a stimulation therapy may include the method shown at 540 in FIG. 10 . As shown at 542 in FIG. 10 , method 540 may include detecting sleep upon detection of (1) time of day; and (2) a lack of physical movement indicative of sleep for a selectable, predetermined period of time. The time of day may be selectable and / or based on patient data. Once at least these two criteria are met, then, as shown at 544 in FIG. 10 , the method includes increasing the intensity of the stimulation therapy from a lower initial intensity level to a target intensity level, such as in a ramp manner. Stimulation at the target intensity level then continues as long as the sensed physiological information indicates continued sleep. However, upon detection of physical movement by the patient (which indicates wakefulness) or detection of the patient mechanically indicating wakefulness (e.g., physical tapping on the proximal chest with the IPG), then the method may terminate any stimulation therapy and remain in a no-stimulation mode for a selectable, predetermined period of time (e.g., 15 minutes). In other words, after an interruption, this method can delay the start of therapy for a period of time (e.g., 15 minutes), the length of the delay period being programmable.

[0092] 11 , in some examples, method 540 may further include detecting sleep onset via additional physiological signals / information, such as posture, respiratory signals / information (e.g., stability based on respiratory period, depth, etc.), cardiac signals / information (e.g., stability based on per RR interval, HR, etc.), and / or other information. For example, in one non-limiting example, portion 542 of method 540 includes detecting posture (550) and may include detecting sleep for some specific postures (but not others) and / or for some specific changes in posture (but not others). In some examples, the specific postures and / or specific changes in posture may be selectable by the patient and / or clinician. For example, identified postures in which sleep is detectable may include lying postures (e.g., prone, supine, left lateral position, right lateral position), however, this method does not allow for automatic detection of sleep when the patient is standing.

[0093] In some instances, the exemplary method may detect (e.g., recognize) REM sleep, thereby avoiding false-positive detection of wakefulness. In particular, while breathing during REM sleep does not exhibit the same stability as during non-REM sleep, such detected less stable breathing may be confirmed as occurring during REM sleep (and not wakefulness) based on the patient sleeping for an extended period of time (e.g., passing through multiple sleep stages, S1-S4) and exhibiting a lack of body movement (e.g., the type of body movement one would observe in wakefulness).

[0094] Implementation of method 540 may also include enhancing sensitivity to and / or specificity with respect to the physiological phenomenon being sensed.

[0095] In some examples, detecting sleep in method 540 in FIG. 10 (e.g., at 542) may also include distinguishing between degrees and / or types of body movement, posture, and the like, as shown at 552 in FIG. 12. This distinction may be performed in conjunction with ramp-up stimuli (e.g., at 544), ramp-down stimuli, termination stimuli (e.g., 546), etc. For example, via aspect 552 of method 540, the method may distinguish voluntary body movement as opposed to a patient being woken by a vehicle movement (e.g., airplane, car, etc.) or by a bed partner. In some such examples, upon detecting such a push, method 540 may include temporarily reducing stimulation therapy or pausing therapy, and then resuming the method at 544 to effect a rapid return to the target (e.g., therapeutic) intensity stimulation level. In contrast, via aspect 552, method 540 may identify a physical tap on the chest (near the IPG) as a voluntary physical movement / cause, or a significant change to posture (e.g., from lying down to standing) as being voluntary (e.g., not accidental), and then terminate therapy (or cause a longer pause), as at 546 in FIG. 10, because such detected behavior, whether momentary or of longer duration, indicates arousal.

[0096] At least Figures 42-45 provide at least some example ways in which sleep-wake state determinations may be made according to respiratory morphology features. Furthermore, at least some aspects of such sensing and related determinations (of sleep-wake state) relating to respiratory morphology features are further described in connection with at least Figures 58 and 61A.

[0097] In one aspect, various features of respiratory morphology (e.g., inspiration onset, inspiration end, amplitude, etc.) discussed below in FIGS. 13-16 may enhance determining sleep-wake states (e.g., at least sleep detection). In one aspect, these features of respiratory morphology are readily identifiable and therefore beneficial for use in tracking respiratory rate, which may indicate sleep (vs. wakefulness) according to the value of respiratory rate, trend, and / or variability in respiratory rate. In some examples, at least some of these features of respiratory morphology may indicate stability, which may be characteristic of sleep (vs. wakefulness). Some examples of such stability, which may be used to detect sleep / wake transitions, may include a stable respiratory rate, stability in the amplitude of the respiratory signal, stability in the percentage of the respiratory period corresponding to inspiration, and / or stability in the percentage of the respiratory period corresponding to expiration.

[0098] As schematically represented at 555 in FIG. 13 , in some example methods, determining the sleep-wake state, such as through tracking at least a portion of the respiration rate information identified above, may include sensing at least one of inspiration start, expiration end, and expiration pause end, and performing sleep-wake state determination at least via the sensed inspiration start, sensed expiration start, and sensed expiration pause end.

[0099] As represented schematically at 560 in FIG. 14 , in some example methods, determining the sleep-wake state, such as via tracking at least a portion of the respiration rate information identified above, may include sensing at least one of an end of exhalation and an end of an exhalation pause, and performing the sleep-wake state determination via at least one of the sensed end of exhalation and the sensed end of an exhalation pause.

[0100] It will be appreciated that other combinations may be used, such as combining different combinations of criteria from Figures 13-16 (e.g., inhalation start, exhalation pause end, etc.), or using only one of these criteria from Figures 13-16 when determining the sleep-wake state.

[0101] 15 , in some example methods, determining the sleep-wake state (e.g., via tracking at least a portion of the respiration rate information identified above) may include sensing an inhalation-to-exhalation transition, and performing the sleep-wake state determination at least via the sensed inhalation-to-exhalation transition. Conversely, in some examples, sensing the physiological information includes sensing an exhalation-to-inhalation transition, and the sleep-wake state determination is performed via the sensed exhalation-to-inhalation transition.

[0102] As schematically represented at 580 in FIG. 17 , in some example methods, determining the sleep-wake state (e.g., via tracking at least a portion of the respiration rate information identified above) may include sensing at least one of an inspiratory peak and an expiratory peak, and performing the determination of the sleep-wake state via the sensed inspiratory peak and at least one of the sensed expiratory peak.

[0103] In some examples, at least some of the sensing of respiratory features, morphology, etc. may be detected via sensing bioimpedance, as further described below in connection with at least impedance parameters 2536 in FIG. 32. Of course, as noted elsewhere, sensing of such respiratory features, etc. may be performed via sensing modalities other than or in addition to sensing bioimpedance. For example, in some examples, at least some of the sensing of respiratory features, morphology, etc. may be detected via sensing electrocardiogram (ECG) information, as further described below in connection with at least ECG parameters 2520 in FIG. 32 and / or 2020 in FIG. 30A.

[0104] In some such examples associated with at least Figures 13-16 and / or at least Figures 27-30A, methods and / or devices for determining sleep-wake states via sensing variability in respiratory behavior, cardiac behavior, and / or other physiological information may include identifying certain features of such variability that are indicative of sleep-disordered breathing (SDB) and distinguishing the identified features indicative of SDB from other features of respiratory behavior, cardiac behavior, and / or other physiological information, such as those indicative of sleep or wakefulness.

[0105] 17 , in some examples, a method 580 (or device therefor) for determining a sleep-wake state may include sensing physiological signals / information (e.g., respiratory features and / or cardiac features), as shown at 582. At 583, the method 580 includes applying filtering and processing (F / P) to the sensed physiological signals / information to produce (1) filtered / processed physiological signal information including variability in physiological signals / information (e.g., respiratory features and / or cardiac features) that are characteristic of sleep-disordered breathing (SDB) at 584; and (2) filtered / processed signal information including variability in physiological signals / information (e.g., respiratory features and / or cardiac features) other than those that are characteristic of sleep-disordered breathing (SDB) at 585. Via the signal information at 585, the method may include determining a sleep-wake state at 590. In some such examples, the sleep-wake state determination may include at least some of the substantially same features as described in connection with at least Figures 13-16 or other examples described throughout this disclosure.

[0106] With further reference to FIG. 17 , in some examples, output 586 of information 584 may be used in monitoring, diagnosing, treating, etc., sleep-disordered breathing (SDB). However, in some examples, via path 592, this sensed physiological signal / information (e.g., respiratory and / or cardiac information) characteristic of sleep-disordered breathing (SDB) may be used to confirm the sleep-wake state determination, such as by confirming that the patient is asleep by confirming the occurrence of sleep-disordered breathing. In making this confirmation, the method may identify characteristics of sleep-disordered breathing, including at least in part (but not limited to) the periodic nature of SDB, such as recurrent sequences of flow limitation, apnea (or hypopnea), and recovery. This identification may also include identifying similar periodic changes in heart rate that occur without detecting any visible changes in posture.

[0107] Alternatively, the method may include confirming that the patient is in an awake state (which is primarily determined by other information) at least in part through confirming the absence of sleep disordered breathing, such as due to the periodic nature of changes to breathing pattern and heart rate without gross postural changes.

[0108] In some examples, the determination of the sleep-wake state may be performed via sensed cardiac morphological features. At least some aspects of such sensing and associated determination (of sleep-wake state) relating to cardiac morphological features are further described in connection with at least FIGS. 30A and 32.

[0109] Various features of cardiac morphology may enhance determining sleep-wake states (e.g., at least sleep detection), at least because these features of cardiac morphology are easily identifiable and therefore beneficial for use in tracking heart rate, which may indicate sleep (vs. wakefulness) according to the value, trend, and / or variability of heart rate (HRV). In some examples, at least some of these features of cardiac morphology may exhibit increasing stability, which may be characteristic of sleep (vs. wakefulness). In some examples, at least some sleep stages may exhibit more or less variability in heart rate variability (HRV) and / or more or less variability in respiratory features, as described above. For example, more variability in cardiac features (e.g., heart rate, etc.) and respiratory features (e.g., respiratory rate, etc.) may be expected in REM sleep. At least some examples of determining sleep-wake states may identify such variability in cardiac and respiratory signals characteristic of REM sleep stages in a manner that can be distinguished from variability in cardiac and respiratory signals (or in some instances, the lack thereof) characteristic of wakefulness. For example, if a detection of a moderate increase in variability in respiratory and / or cardiac features follows other sleep stages (e.g., S3, S4) coupled with a detection of a lack of body movement, then an exemplary method may identify that the patient is in REM sleep.

[0110] In some examples, at least a portion of the sensing of cardiac features, morphology, etc. may be detected via sensing bioimpedance, as described further below in connection with at least impedance parameters 2036 in FIG. 30A and 2536 in FIG. 32. In some examples, at least a portion of the sensing of cardiac features, morphology, etc. may be detected via sensing electrocardiogram (ECG) information, as described further below in connection with at least ECG parameters 2020, 2520 in FIGs. 30A and 32, respectively. The bioimpedance and / or ECG used to sense cardiac features, morphology, etc. may also be used to sense respiratory features, morphology, etc. (as previously described), or may be used to sense both cardiac and respiratory features, morphology, etc. It will be further appreciated that FIGS. 30A and 32 provide additional example sensing types, modalities, etc., whereby cardiac information (including, but not limited to, heart rate and / or heart rate variability) may be sensed, which may then be used in determining a sleep-wake state.

[0111] At least some of these relationships in cardiac morphology are further described in relation to the cardiac portion 2600 of the care engine 2500 in FIG.

[0112] As schematically represented at 700 in FIG. 18 , in some example methods, determining the sleep-wake state may include sensing a plurality of physiological signals / information (e.g., at least respiratory information, cardiac information (e.g., cardiac motion)) and performing the determination of the sleep-wake state via the plurality of physiological signals / information (e.g., the sensed respiratory information and / or the sensed cardiac information).

[0113] As schematically represented at 705 in Figure 19, in some example methods, determining the sleep-wake state (e.g., falling asleep, etc.) may include comparing the subsequent second motion information with the first motion information. As further shown at 710 in Figure 20, in some examples, method 705 may include determining the sleep-wake state (e.g., falling asleep, etc.) upon determining from the comparison that the second value of the subsequent second motion information and the first value of the first motion information are less than a predetermined difference. The predetermined difference value may be selectable.

[0114] In some examples of methods 705, 710, each of the respective first and second motion information includes at least one of sensed respiratory information, sensed cardiac information, and sensed body motion.

[0115] In some examples of methods 705, 710, the subsequent second information includes information obtained in the most recent sensed respiratory cycle and the first information includes information obtained in a previous respiratory cycle. In some examples, the subsequent second information includes information about respiratory activity in at least the last 30 seconds. In some examples, this information may relate to respiratory activity in at least the last 60 seconds. In some examples, this information may relate to respiratory activity in at least the last 7 breaths.

[0116] In some examples, the previous respiratory cycle includes the respiratory cycle immediately before the most recently sensed respiratory cycle. In some examples, the previous respiratory cycle includes respiratory activity in the 30 seconds (or 60 seconds, or 7 breaths) prior to the most recently sensed respiratory cycle. In some examples, the first information includes respiratory information over at least one respiratory cycle or at least 30 seconds or at least 60 seconds.

[0117] In some examples of methods 705, 710, recent movement information is compared to objective values ​​indicative of sleep. In some examples, lower and / or more stable respiratory and heart rates are more likely to be associated with sleep. As previously discussed in connection with at least FIG. 17 , determining the sleep-wake state may include isolating (e.g., filtering, rejecting) respiratory features characteristic of sleep-disordered breathing (SDB) and / or respiratory features characteristic of specific sleep stages that do not necessarily contribute to general sleep detection (e.g., detecting sleep onset).

[0118] However, as previously described with respect to at least aspects 584, 592 of the method of FIG. 17, detection of sleep disordered breathing (SDB) may also be used to detect or confirm the presence of sleep, or in some instances, to detect or confirm the onset of sleep.

[0119] In some examples, the method (at 705, 710 in FIGS. 19-20 ) may include determining subsequent second motion information from second average values ​​of the motion information over respiratory cycles of the sensed second respiratory period, and determining first motion information from first average values ​​of the motion information over respiratory cycles of the first respiratory period. In some such examples, the second average values ​​of the motion information correspond to an average of a parameter, such as, but not limited to, an average amplitude for the sensed second respiratory period; an average respiratory rate for the sensed second respiratory period; and / or an average ratio of inhalation periods to exhalation periods over the sensed second respiratory period.

[0120] It will be further understood that in some examples, the exemplary implementations associated with Figures 19-20 may be used for any physiological (biological) signal of interest that may contribute to determining sleep-wake states throughout various examples of the present disclosure.

[0121] In some examples, at least some of the aspects described above with respect to Figures 19-20 may be implemented via history parameters 2542 and / or comparison parameters in the sensing portion 2510 of the care engine 2500, as described below in connection with at least Figure 32.

[0122] As schematically represented at 740 in FIG. 21 , in some exemplary methods, determining the sleep-wake state may include identifying a wakefulness state (or lack thereof) via determining variability in sensed physiological information, including variability in at least one of: a respiratory signal and / or information derived therefrom (e.g., respiratory rate); a cardiac signal and / or information derived therefrom (e.g., heart rate); other physiological signals (e.g., EEG, ECG, EMG, EOG, etc.); the inspiratory and / or expiratory portions of the respiratory cycle; the duration of the inspiratory portion; the amplitude of the peak of the inspiratory portion; the duration of the expiratory portion; posture; physical activity / movement; and the amplitude of the peak of the expiratory portion. In some examples, for at least some parameters, variability may be evaluated relative to a threshold, which may be fixed in some examples. For example, determining the sleep-wake state may include determining a wakefulness state (or lack thereof) via determining variability in the sensed physiological information that exceeds a selectable threshold.

[0123] As schematically represented by 745 in FIG. 22 , in some exemplary methods, determining the sleep-wake state may include identifying a wake state (or lack thereof) via determining variability in sensed physiological information, including variability in at least one of: a respiratory signal and / or information derived therefrom (e.g., respiratory rate); a cardiac signal and / or information derived therefrom (e.g., heart rate); other physiological signals (e.g., EEG, ECG, EMG, EOG, etc.); the inspiratory and / or expiratory portions of the respiratory cycle; the duration of the inspiratory portion; the amplitude of the peak of the inspiratory portion; the duration of the expiratory portion; posture; physical activity / movement; and the amplitude of the peak of the expiratory portion. In some examples, for at least some parameters, variability may be evaluated relative to a threshold, which may be fixed in some examples. For example, determining the sleep-wake state may include identifying a sleep state (or lack thereof) via determining variability in the sensed physiological information that remains below a selectable threshold.

[0124] As schematically represented at 750 in FIG. 23, in some examples, performing the sleep-wake state determination includes tracking at least one second parameter other than (or movement of) the chest, neck, and / or head, the second parameter including at least one of: time of day; daily activity pattern; and (typical) breathing pattern.

[0125] As represented schematically at 760 in Figure 24, in some examples, performing the sleep-wake state determination includes tracking at least one second parameter other than movement at or of the chest, neck, and / or head, where the second parameter includes a physiological parameter. For example, one such physiological parameter may include temperature (e.g., 2038 in Figure 30A, 2538 in Figure 32).

[0126] As represented diagrammatically at 770 in FIG. 25, in some examples, determining the sleep-wake state includes assessing at least one of a probability of sleep and a probability of wakefulness based on sensing physiological information.

[0127] As represented schematically at 780 in Figure 26A, some example methods (and / or devices) include taking action if the probability of sleep or the probability of wakefulness exceeds a threshold. In some such examples, some example methods (and / or devices) include taking action if the probability of sleep or the probability of wakefulness exceeds a threshold by a selectable predetermined percentage for a selectable predetermined duration.

[0128] In some examples of method 780 (FIG. 26A), taking an action may include at least one of initiating a stimulation treatment period and terminating a stimulation treatment period, as shown at 781 in FIG. 26A. In some such examples, taking an action (if the probability of sleep exceeds a threshold, as at 780 in FIG. 26A) may include initiating a therapeutic treatment period (e.g., applying stimulation), resuming stimulation within a treatment period after a pause or interruption of stimulation, and / or other actions. In some examples, taking an action (if the probability of wakefulness exceeds a threshold) may include terminating a therapeutic treatment period, interrupting stimulation within a treatment period, and / or other actions.

[0129] In some such examples, initiating and / or resuming stimulation therapy may involve using a stimulation ramp that has a lower initial stimulation intensity and then increases to a target intensity level. In some examples, terminating therapy may involve using a stimulation ramp that gradually decreases the stimulation intensity from a target therapy intensity level until stimulation is no longer applied (i.e., the stimulation intensity equals zero).

[0130] With further reference to at least FIG. 26A, in some examples, taking action in method 780 may include using an observer for an additional period to ensure the patient is asleep, and / or using a start timer to begin counting a selectable predetermined period (e.g., delay) until stimulation begins as part of a treatment period.

[0131] In some such examples, such as method 780 (FIG. 26A), the method further includes applying boundaries to the respective beginnings and ends, as shown at 782 in FIG. 26C. At least some aspects of such boundaries are further described in connection with boundary parameters 3016 of activation portion 3000 in FIG. 32.

[0132] In some example methods associated with method 780, applying a boundary includes setting a start boundary before which starting is not implemented and / or setting a stop boundary by which ending is implemented, as shown at 783 in FIG. 26D.

[0133] In some examples, the method of determining sleep-wake states according to boundaries (e.g., 782, 783) may include implementing the respective start and stop boundaries based on time of day, as shown at 784 in Figure 26E. In some examples, the method may include implementing the time of day based on at least one of time zone; ambient light via external sensing; daylight saving time; geographic latitude; and seasonal calendar, as shown at 785 in Figure 26F.

[0134] In some examples, as shown at 786 in FIG. 26G, a method (eg, 783) may include implementing stopping boundaries based on at least one of the number, type, and duration of sleep stages.

[0135] In some examples, as shown at 787 in FIG. 26H , a method (e.g., 783) may include implementing at least one of a start boundary parameter and a stop boundary parameter based on a sensed temperature via a sensor. In some examples, a method may include implementing at least one of a start of a stimulation treatment period and an end of a stimulation treatment period based on sensing a body temperature via a sensor. In some examples, a method may include disposing a sensor within an implantable pulse generator, where the sensor includes a temperature sensor. In some such examples, method 787 may be implemented via at least temperature sensor 2038 in FIG. 30A , temperature parameter 2538 in FIG. 32 , and / or at least boundary parameter 3016 in FIG. 32 , and / or is further described below in connection therewith.

[0136] In some examples, as shown at 788 in FIG. 26I, the method (including determining the sleep-wake state, e.g., at 770 in FIG. 25 and / or 780 in FIG. 26A) may include receiving input from at least one of an app on the remote control and the mobile consumer device regarding at least one of the degree of ambient light; the degree or type of movement of the remote control or mobile consumer device; and the frequency, type, or degree of use of the remote control or mobile consumer device.

[0137] As schematically represented at 800 in FIG. 27 , some examples of determining a sleep-wake state may include dividing a signal associated with sensing physiological information into multiple different signals, each respective signal representing a different sleep-wake decision parameter; and determining a probability of a sleep-wake state based on evaluating each different signal associated with each different sleep-wake decision parameter. In some examples, this exemplary method may include voting, whereby each signal provides input to the overall probability of sleep. In some such examples, various separate signals may be weighted differently to apply each respective sleep-wake decision parameter relatively more or relatively less in comparison to other respective sleep-wake decision parameters.

[0138] In some examples, at least some aspects of method 800 (FIG. 27) may be implemented via at least some of the features and attributes of the arrangements described in connection with at least FIGS. 30A-32.

[0139] As schematically represented at 810 in FIG. 28 , in some examples, determining the sleep-wake state includes at least one of assessing at least one of a probability of sleep and a probability of wakefulness based on sensing physiological information via sensing movement at (or of) the chest, neck, and / or head.

[0140] As schematically represented at 820 in FIG. 29A , in some examples, sensing the physiological information includes obtaining and identifying arousal information (e.g., during a normal wakefulness period) and performing a sleep-wake state determination at least in part via the arousal information. In some such examples, the arousal information is used to better characterize sleep and thus more easily determine the sleep-wake state (e.g., to detect sleep or lack thereof). However, in this context, the identified arousal information is not used to adjust therapy (e.g., stimulation parameters, etc.) and / or to characterize breathing disorders. In some examples, identifying arousal may be performed via sensing at least one of gross body movement and motion. In some examples, sensing the physiological information includes obtaining sleep information and performing a sleep-wake state determination via the sleep information.

[0141] As schematically represented at 830 in FIG. 29B , in some examples, the method includes detecting snoring and using the snoring information as part of determining the sleep-wake state. In some such examples, the method may include quantifying the detected snoring and reporting the snoring information to at least one of the patient, a physician, or a caregiver. In some examples, snoring may be distinguished from normal speech. In some examples, snoring may be detected, tracked, etc. in conjunction with acoustic sensors 2039 ( FIG. 30A ) and / or acoustic parameters 2539 ( FIG. 32 ).

[0142] In some examples, various features and attributes of the exemplary methods (and / or care devices) described in connection with at least Figures 1A-29B for determining sleep-wake states may be combined and implemented in a complementary or additive manner.

[0143] These, as well as additional features and attributes associated with Figures 1A-29B, are further described in connection with at least Figures 30A-32. Additionally, at least some of the examples described in connection with Figures 30A-42 may include example implementations of the examples described in connection with Figures 1A-29B.

[0144] 30A is a block diagram that schematically illustrates an exemplary sensing portion. In some examples, the exemplary method uses, and / or the exemplary SDB care device includes, a sensing portion 2000 to sense physiological information and / or other information, such sensed information being related to sleep-wake detection, among other uses. The sensed information can be used to implement at least a portion of the exemplary method and / or exemplary device described in connection with at least FIGS. 1A-29B and / or 30B-42.

[0145] It will be understood that sensing portion 2000 may be implemented as a single sensor or multiple sensors, and may include a single type of sensor or multiple types of sensing, and it will be further understood that the various types of sensing schematically represented in FIG. 30A may correspond to sensors and / or sensing modalities.

[0146] In some examples, sensed information may refer to physiological signals (e.g., biosignals) and / or metrics that may be derived from such physiological signals. For example, one example of physiological information may include respiration (2005), which is obtained from a respiratory signal and from which various metrics may be derived, such as, but not limited to, respiratory rate, respiratory rate variability, respiratory phase, number x volume, waveform morphology, and many more, among other sensed physiological information. Respiration information and / or signals may be sensed via one or more sensing modalities described below (and / or other sensing modalities), such as, but not limited to, accelerometer 2026, ECG 2020, EMG 2022, ballistocardiogram 2023A, vibratory cardiogram 2023B, acceleration electrocardiogram 2023C, impedance 2036, pressure 2037, temperature 2038, acoustics 2039, and / or other sensing modalities, at least some of which are described further below. In some examples, the sensed physiological information may include cardiac information (2006) obtained from a cardiac signal, from which various metrics may be derived, such as, but not limited to, heart rate (HR), heart rate variability (HRV), PR interval, waveform morphology, and more. An example of a cardiac signal may include an ECG signal, as represented at 2020 in FIG. 30A . Accordingly, cardiac information and / or signals may be sensed via one or more sensing modalities (and / or other sensing modalities) described further below, such as, but not limited to, accelerometer 2026, ECG 2020, EMG 2022, impedance 2036, pressure 2037, temperature 2038, and / or acoustic 2039. In some examples, the sensed physiological information (e.g., via sensing portion 2000) may include a wide range of physiological information other than respiration and / or cardiac information, with at least some examples further described below in connection with FIGS. 30A , 32, and other examples throughout this disclosure.

[0147] The sensed physiological signals and / or information (e.g., respiratory 2005, cardiac 2006, and / or other information 2007) may be used for a wide variety of purposes, such as, but not limited to, determining sleep-wake states (e.g., various sleep onset determinations), timing stimulation relative to respiration, determining disease burden, determining wakefulness, etc. In some such examples, determining disease burden may include detection of sleep disordered breathing events, which may be used in determining, assessing, etc. treatment outcomes, such as, but not limited to, AHI, etc., as well as titrating stimulation parameters, adjusting sensitivity to sensing physiological information, etc.

[0148] For example, in one non-limiting example, the electrocardiogram (ECG) sensor 2020 in FIG. 30A may include a sensing element (e.g., an electrode) or multiple sensing elements positioned relative to a patient's body (e.g., implanted in the transthoracic region) to obtain ECG information. In some examples, the ECG information may include one example implementation for obtaining cardiac information, including, but not limited to, heart rate and / or heart rate variability (HRV), which may be used (with or without other information) in determining sleep-wake states as described throughout the examples of this disclosure.

[0149] However, in some instances, the ECG sensor 2020 may represent an ECG sensing element in general terms, regardless of the particular manner in which sensing ECG information may be implemented.

[0150] In some examples where multiple electrodes are used to obtain ECG signals, the ECG electrodes may be attached to or form at least a portion of the case (e.g., outer housing) of an implantable pulse generator (IPG), such as those further described below in connection with at least FIG. 31A. In such examples, other ECG electrodes are spaced apart from the ECG electrodes associated with the IPG. In some examples, such as those further described below in connection with FIG. 31A, at least some ECG sensing electrodes may also be used to deliver stimulation to nerves or muscles, such as, but not limited to, upper airway patency-related nerves (e.g., hypoglossal nerves) or other nerves or muscles.

[0151] In some examples, multiple ECG sensing electrodes may be attached to or form different portions of the case of the IPG, such as those described below in connection with at least Figures 31B, 31C, and 31D. In such examples, each ECG electrode is positioned on the case of the IPG so that they are electrically independent of each other so that an appropriate ECG signal can be obtained.

[0152] In some examples, ECG sensing electrodes may be used for sensing only (e.g., single-purpose), but are positioned along the lead body of the stimulation lead, as described further below in connection with FIG. 31A. It will be understood that such dedicated ECG sensing electrodes are positioned along the stimulation lead in a manner that avoids contact with the case of the IPG, particularly in examples where exposed conductive portions of the case of the IPG may act as electrodes and the sensing vector may be obtained via a combination of sensor electrodes along the lead and conductive portions of the IPG. Similarly, the same / similar electrode arrangements may be used to sense bioimpedance, as described more fully below in connection with FIGS. 31A-31F, 32.

[0153] In some examples, other types of sensing may be used to obtain cardiac information (including, but not limited to, heart rate and / or heart rate variability), such as via a ballistocardiogram sensor 2023A, a vibratory cardiogram sensor 2023B, and / or an accelerometer electrocardiogram sensor 2023C, as shown in FIG. 30A. In some examples, such sensing is based on and / or implemented via accelerometer-based sensing, such as that described further below in connection with accelerometer 2026.

[0154] In one aspect, in some examples, the ballistocardiogram sensor 2023A senses cardiac information caused by cardiac output, such as the forced ejection of blood from the heart into the aorta, which occurs with each heartbeat. The sensed ballistocardiogram information may include heart rate (HR), heart rate variability (HRV), and / or additional cardiac morphology. In some examples, such ballistocardiogram-type information may be sensed from within a blood vessel, where a sensor (e.g., an accelerometer) senses the movement of the blood vessel wall caused by the pulsation of blood moving through the vessel with each heartbeat. This phenomenon may sometimes be referred to as arterial motion.

[0155] In one aspect, the vibratory cardiogram sensor 2023B may provide cardiac information similar to that described for the ballistocardiogram sensor 2023A, except that it is obtained via sensing vibrations in or along the chest wall caused by cardiac output, according to an accelerometer (e.g., single-axis or multi-axis). In particular, the vibratory cardiogram measures compression waves generated by the heart (e.g., according to cardiac wall motion and / or blood flow) during its movement and transmitted to the chest wall. Thus, the sensor 2023B may be placed in the chest wall.

[0156] In some such examples of sensing according to sensors 2023A, 2023B, such methods and / or devices may also include sensing respiration rate and / or other respiratory information.

[0157] 30A , in some examples, sensing portion 2000 may include an electroencephalography (EEG) sensor 2012 to acquire and track EEG information. In some examples, EEG sensor 2012 may also sense and / or track central nervous system (CNS) information in addition to sensing EEG information. In some examples, EEG sensor 2012 may be implanted subcutaneously under the scalp or in another suitable head and neck region for sensing EEG information. Thus, EEG sensor 2012 may be positioned near the brain and detect frequencies associated with electrical brain activity.

[0158] In some examples, the sensing element used to sense EEG information may be chronically implanted, such as in a subcutaneous location (e.g., a subcutaneous location outside the skull) rather than an intracranial location (e.g., inside the skull). In some examples, the EEG sensing element is positioned and / or designed to sense EEG information without stimulating the vagus nerve, at least because stimulating the vagus nerve can exacerbate sleep apnea, particularly with respect to obstructive sleep apnea. Similarly, the EEG sensing element may be used in a device in which a stimulation element delivers stimulation to the hypoglossal nerve or other upper airway patent nerve without stimulating the vagus nerve to avoid exacerbating obstructive sleep apnea.

[0159] In some examples, sensing portion 2000 may include an electromyography (EMG) sensor 2022 to acquire and track EMG information. In some such examples, the EMG sensor may include electrodes positioned near the tongue to detect signals indicative of voluntary control of the tongue, which may in turn indicate arousal. In some examples, the sensed EMG signals may be used to identify sleep and / or obstruction events. At least some additional aspects related to EMG sensing are described at least in connection with FIG. 31A .

[0160] In some examples, as shown in FIG. 30A , the sensing portion 2000 may include an EOG sensor 2024 for obtaining and tracking EOG information, which may be used to determine sleep-wake states and / or different sleep stages. In some instances, such sensed EOG information may be used to distinguish REM sleep from non-REM sleep or from wakefulness. In some examples, the sensing element for obtaining the EOG information may be implanted in the head and neck region, such as adjacent to the eyes, eye muscles, and / or ophthalmic nerves. In some examples, the sensing element may communicate the EOG information wirelessly or via implanted leads to a control element (e.g., a monitor, a pulse generator, and the like) implanted in the head and neck region. In some such examples, the sensing element may include electrodes implanted near one or both of the patient's eyes.

[0161] However, in some examples, EOG information may be obtained via external sensing elements worn on the head or that may observe eye movement, position, etc., such as via a cell phone, a monitoring station in the patient's vicinity, and the like. Such externally obtained EOG information may be wirelessly communicated to implanted monitors, pulse generators, and the like that control sensing and / or stimulation elements implanted within the patient's body. Some aspects of sensing via EOG sensors are further described below in connection with at least FIG. 32.

[0162] In some examples, any one or combination of the various sensing modalities (e.g., EEG, EMG, etc.) described in connection with FIG. 30A may be implemented via a single sensing element 2014.

[0163] In some examples, sensing portion 2000 may include an accelerometer 2026. In some examples, accelerometer 2026 and associated sensing (e.g., movement of (or of) chest, neck, and / or head, respiration, heart, posture, etc.) may be implemented according to at least some of the substantially same features and attributes as described in ACCELEROMETER-BASED SENSING FOR SLEEP DISORDERED BREATHING (SDB) CARE by Dieken et al., published as US 2019-0160282 on May 30, 2019, which is incorporated by reference herein in its entirety. In some examples, the accelerometer may include a single-axis accelerometer, while in some examples, the accelerometer may include a multi-axis (e.g., three-axis) accelerometer. In some examples, a three-axis accelerometer (e.g., x-axis, y-axis, and z-axis) may provide three sensor signals indicative of patient movement and the angle of the sensor relative to gravity.

[0164] Among other types and / or methods of sensing information, the accelerometer sensor 2026 may be used to sense or obtain a ballistocardiogram (2023A), a oscilloscope (2023B), and / or an accelerated electrocardiogram (2023C), which may be used to sense (at least) heart rate and / or heart rate variability (and in some instances, among other information, such as respiration rate), which may then be used as part of determining sleep-wake state, as described throughout the examples of this disclosure.

[0165] In some examples, the accelerometer 2026 may be used to sense activity, posture, and / or body position as part of determining the sleep-wake state, and the sensed activity, posture, and / or body position may sometimes at least partially indicate the sleep-wake state.

[0166] In some examples, sensing portion 2000 may include an impedance sensor 2036, which may sense a patient's transthoracic impedance or other bioimpedance. In some examples, impedance sensor 2036 may include multiple sensing elements (e.g., electrodes) spaced apart across a portion of the patient's body, such as electrodes 2120, 2135, 2130 in FIG. 31A and / or the illustrative electrodes (e.g., 2310, 2402, 2404) in FIGS. 31B-31F. In some such examples, one of the sensing elements (e.g., electrode 2135 in FIG. 31A) may be attached to or form part of the exterior (e.g., case) of an implantable pulse generator (IPG) or other implantable sensing monitor, while the other sensing elements (e.g., electrodes 2120, 2130 in FIG. 31A) may be positioned a distance away from the sensing elements of the IPG or sensing monitor. In at least some such examples, the impedance sensing arrangement integrates all body motion / changes (e.g., respiratory effort, cardiac motion, etc.) between the sensing electrodes (including in the case of an IPG, if present). Some example implementations of impedance measurement circuitry will include separate drive and measurement electrodes to control tissue access impedance from the electrodes at the drive node.

[0167] In some examples, sensing portion 2000 may include a pressure sensor 2037 that senses respiratory information, such as, but not limited to, cardiorespiratory information. In some such examples, the respiratory pressure sensor may include at least some of the substantially same features and attributes as those described in U.S. Patent Publication No. 2011 / 0152706 to Ni et al., "METHOD AND APPARATUS FOR SENSING RESPIRATORY PRESSURE IN AN IMPLANTABLE STIMULATION SYSTEM," published June 23, 2011, which is incorporated herein by reference in its entirety. In some examples, pressure sensor 2037 may be positioned in direct or indirect continuity with the respiratory organs or airways, or tissue supporting the respiratory organs or airways, to sense respiratory information.

[0168] In some examples, one sensing modality within sensing portion 2000 may be implemented at least in part via another sensing modality within sensing portion 2000.

[0169] In some examples, sensing portion 2000 may include an acoustic sensor 2039 for sensing acoustic information, such as, but not limited to, cardiac information (including heart sounds), respiratory information, snoring, and the like.

[0170] In some examples, the sensing portion 2000 may include body movement parameters 2035, in which the patient's body movement (e.g., activity, spontaneous motor inactivity during sleep) may be detected, tracked, etc. Body movement may be detected, tracked, etc. via a single type of sensor or via multiple types of sensing. For example, in some examples, body movement may be sensed via an accelerometer 2026, in some examples, body movement may be sensed via EMG 2022 and / or other sensing modalities, as described throughout various examples of this disclosure.

[0171] In some examples, the sensing portion 2000 in FIG. 30A may include posture parameters 2040 for sensing and / or tracking sensed information related to posture, which may also include sensing of the patient's body position, activity, etc. This sensed information may, in some examples, indicate the patient's wakefulness or sleep state. In some such examples, such information may be sensed via the accelerometer 2026 and / or other sensing modalities, as mentioned above. In some examples, such posture information (and / or body position, activity) may be used alone and / or in combination with other sensed information to determine the sleep-wake state. As described elsewhere herein, in some examples, posture may be considered as one of several parameters when determining the probability of sleep (or wakefulness).

[0172] For example, sensing an upright posture is typically associated with an awake state, such as standing or walking. However, as noted elsewhere, a person may be in an upright sitting position and still be asleep (e.g., asleep in a chair). Thus, posture may be just one parameter used in determining the sleep-wake state, along with at least some of the other parameters described in connection with the sensing portion 2000 of FIG. 30A and / or the care engine 2500 in FIG. 32. Conversely, sensing a supine or lateral (i.e., lying on one's side) posture is typically associated with a sleep state. However, a patient may be in such positions without being asleep, and other parameters (e.g., FIGS. 30A, 32) in addition to or instead of posture may significantly enhance the determination of the sleep-wake state.

[0173] Furthermore, posture sensing is not limited to sensing static posture, but can be extended to sensing simple changes in posture (or body position), which may be indicative of a sleep-wake state, at least because certain changes in posture (e.g., from supine to upright) are likely to be indicative of a wakefulness state in most cases. Similarly, maintaining a single stable posture for an extended period of time may be indicative of a sleep state, whereas more complex or frequent changes in posture and / or body position may further indicate a wakefulness state.

[0174] In some examples, sensing portion 2000 includes other sensors or parameters 2041 to direct the sensing of and / or receive, track, evaluate, etc., sensory information (e.g., time, geolocation, proximity to sleep area, light, noise, motion, etc.) other than the information previously described sensed via sensing portion 2000. In some examples, such other sensed information may be tracked, evaluated, etc. in conjunction with other parameters 2541 in sensing portion 2510 of care engine 2500 in FIG.

[0175] In some exemplary methods and / or devices, via the sensing portion 2000, the sleep-wake state can be determined without using posture information or body position information. In some such examples, determining the sleep-wake state without relation to posture information (or body position) can enable the device to provide effective sleep disordered breathing (SDB) care even when the patient may be sleeping in a vertical position, such as in a zero-gravity environment, for example, while sitting in a chair, as opposed to the traditional assumption of sleep occurring in a horizontal body position. Such implementation can enable SDB care when the patient is sleeping while moving, for example, sitting in an airplane seat, a car seat, a train seat, etc. In some such examples, the SDB care method and / or SDB care device can sometimes be referred to as being posture insensitive.

[0176] 30A, in some examples, the sensing portion 2000 may include a temperature sensor 2038. In some examples, such sensed temperature may be tracked, evaluated, etc. in relation to a temperature parameter 2538 in the sensing portion 2510 of the care engine 2500 in FIG.

[0177] In some examples, sensed temperature may be used as a factor in making a sleep-wake state determination according to examples of the present disclosure. In one aspect, the temperature sensor 2038 may sense and track a patient's normal fluctuations in body temperature (e.g., temperature profile) within a 24-hour diurnal period, which may indicate a change on the order of 2 degrees Fahrenheit. For most patients, their body temperature may reach and remain at the high end of its range (e.g., 99.5 degrees Fahrenheit) during the day and evening (e.g., 7:00 PM) before dropping to the low end of its range (e.g., 97.5 degrees Fahrenheit) by early morning (e.g., 5 or 6:00 AM) from midnight through the night. In some examples, the temperature sensor 2038 may sense a change in sensed temperature that occurs within a selectable time window within the 24-hour diurnal period and exceeds a selectable threshold. In some examples, the selectable time window may include one, two, or other time periods. In some such examples, one method involves selecting that a change of a predetermined degree within a selectable time window corresponds to either a transition from a wakefulness state to a sleep state or a transition from a sleepful state to a wakefulness state.

[0178] In some example methods, sensing a change in temperature during a treatment period (e.g., via sensor 2038) may be used to identify sleep disordered breathing behavior. In some such examples, additional sensed information (as described in examples of the present disclosure) may be used to identify sleep disordered breathing (SDB) behavior in addition to sensed temperature.

[0179] In some examples, this temperature fluctuation information sensed via temperature sensor 2038 may be used in conjunction with boundary parameters 3016 (FIG. 32) to automatically implement boundaries or limits at the beginning and end of a treatment period, such that the lowest sensed body temperature may be used to at least partially mark the boundary at the end of a treatment period for a typical patient sleeping at night. Similarly, the highest sensed body temperature (e.g., held for an extended period) may be used to at least partially implement the boundary at the beginning of a treatment period. In some such examples, these features may be used to implement method 787 in FIG. 26H.

[0180] In some examples, these same temperature-based boundaries may be used as one factor (among other factors) to determine the sleep-wake state. At least some other factors may be used along with this sensed temperature variation information to determine the sleep-wake state, which may include time of day parameters, accelerometer information, cardiac information, respiratory information, etc.

[0181] In some cases, smaller, yet detectable, temperature changes within the treatment period may be used to at least partially determine the sleep-wake state. For example, a detectable temperature change may be sensed as a result of the patient's effort to breathe in response to an apnea event, given the greater muscular effort involved in attempting to breathe.

[0182] Furthermore, in some instances, such sensed temperature fluctuation information may provide a more specific or characteristic indication of a sleep or wake period when compared to heart rate or body position, which may exhibit more variation, some of which, at least in some instances, are not necessarily indicative of a sleep or wake period.

[0183] In some examples, at least some of the sensors and / or sensor modalities described in connection with FIG. 30A (and / or FIG. 32) may be incorporated within or on a pulse generator (IPG2133 in FIG. 31A) or within or on a microstimulator (e.g., FIGS. 31A-31F).

[0184] FIG. 30B is a block diagram that schematically represents an exemplary processing portion 2200, which may form a part of and / or be in communication with at least the sensing portion 2000 (FIG. 30A). In general terms, the processing portion processes signals and / or information obtained by a single sensor, a single sensor type, or multiple types of sensors, at least as described in connection with FIG. 30A. As shown in FIG. 30B, the processing portion 2200 may include a filtering function 2210 for filtering the sensed signals to remove noise, irrelevant information, etc. In some examples, the processing portion 2200 may include an interpretation function 2212, by which information sensed via the sensing portion 2000 may be interpreted in light of sensed physiological information present in typical sleep patterns. In some such examples, the interpretation may be performed at least in part with respect to information associated with reference parameters 2220. In some such examples, information available via reference parameters 2220 (for interpreting the sensed information) may include respiration rate and / or respiration signal morphology, and / or may include heart rate and heart signal morphology. Normalization may or may not be utilized.

[0185] In some examples, the sensing portion 2000 ( FIG. 30A ) and / or the processing portion 2200 ( FIG. 30B ) may be used in a method for extracting significant features from a sensor signal. Such feature extraction may include bandpass filtering, frequency analysis, power spectrum analysis, signal amplitude analysis, derivative signal analysis, the use of thresholds, and differential signal analysis. Furthermore, in some examples, such feature extraction may also include amplification and gain control, physiological rate-based outlier rejection methods, and / or wavelet analysis, as well as combinations of the preceding parameters. In some examples, feature extraction may be related to and / or performed to enable analysis of the duration of periodic behavior. For example, feature extraction may be performed on the sensed signal, but the extracted features may be analyzed as moving averages or as distributions in discrete time chunks to determine whether a particular extracted feature (e.g., heart rate, heart rate variability, respiration rate, etc.) has reached a threshold of stability or indicates a change from previous behavior.

[0186] In some examples, at least a portion of the processing portion 2200 may include and / or be implemented by at least some of the features and attributes described in connection with FIGS. 40-42.

[0187] In some examples, all or a portion of the processing portion 2200 may be incorporated within the sensing portion 2510 in FIG. 32 or other portions of the care engine 2500, and / or may be incorporated within the control portion 4000 (FIG. 34A).

[0188] 30A-32 include diagrams that schematically represent several example implementations of sensing elements, stimulation devices, related components, care engines, etc. for treating a patient, which may be used in, include, and / or include example implementations of, at least some of the substantially same features and attributes of, and / or at least some of the example methods and / or example devices described throughout this disclosure. In particular, in some examples, at least some aspects of the examples associated with FIGS. 30A-32 may include example implementations of additional / other elements from, include, and / or include (or may replace) at least some of the substantially same features and attributes of, various components, functions, and / or relationships of, at least some of the examples previously described in connection with at least FIGS. 1A-1C.

[0189] 31A is a schematic diagram illustrating several example implementations 2100 of a sensing element and a neurostimulation device 2113 implanted within a patient. As shown in FIG. 31A , the neurostimulation device 2113 may include an implantable pulse generator (IPG) 2133 and a stimulation lead 2117, which includes a lead body 2118 and a stimulation electrode 2112. The stimulation electrode 2112 is implanted subcutaneously and engaged to an upper airway patency-associated nerve 2105, such as the hypoglossal nerve. In some examples, the IPG 2133 is implanted in the thoracic region 2101, with the stimulation lead 2117 extending upward into the head and neck region 2103. In some examples, the stimulation electrode 2112 is chronically implantable and may include a cylindrical arrangement wrapped at least partially around a nerve, may include a paddle-style electrode, a non-cuff configuration, or other configuration in which an electrode may be chronically implanted in a neurostimulating relationship to a nerve.

[0190] In some examples, the stimulation electrode 2112 may include at least some of the substantially same features and attributes as those described in U.S. Patent No. 8,340,785 to Bonde et al., entitled "SELF EXPANDING ELECTRODE CUFF," issued December 25, 2012, and U.S. Patent No. 9,227,053 to Bonde et al., entitled "SELF EXPANDING ELECTRODE CUFF," issued January 5, 2016, both of which are hereby incorporated by reference in their entireties. In some examples, the stimulation electrode 2112 may include at least some of the features and attributes substantially the same as those described in U.S. Patent No. 8,934,992 to Johnson, "NERVE CUFF," issued January 13, 2015, and / or "CUFF ELECTRODE" to Rondoni, published February 14, 2019 as WO2019 / 032890 (and filed August 14, 2019 as U.S. Patent Application No. 16 / 485,954), both of which are hereby incorporated by reference in their entireties. Additionally, in some examples, the stimulation lead 2117 may include at least some of the features and attributes substantially the same as the stimulation lead described in U.S. Patent No. 6,572,543 to Christopherson et al., which is hereby incorporated by reference.

[0191] It will be appreciated, however, that in some examples, the IPG 2133 may also take the form of a microstimulator that is sized and positioned in the head and neck region 2103 in proximity to the stimulated upper airway patency-related nerves 2105. In some such examples, the microstimulator 2133 may also incorporate and / or include stimulation electrodes 2112 and / or sensing electrodes. In some example implementations in which the IPG 2133 may include a microstimulator, then the placement of the microstimulator in the head and neck region 2103, such as in proximity to the upper airway patency-related nerves, also positions any exposed electrodes (e.g., 2135) on the microstimulator in proximity to the nerves 2105, as well as in closer proximity to the head portion 2106, from which EEG information (including sleep information) may be determined via such electrodes 2135. In some examples, such an exemplary microstimulator may include at least some of the substantially same features and attributes as described in connection with at least one "MICROSTIMULATION SLEEP DISORDERED BREATHING (SDB) THERAPY DEVICE," published on May 26, 2017 as PCT Publication WO2017 / 087681 from PCT / US2016 / 062546 filed on November 17, 2016, and filed on May 8, 2018 as U.S. Patent Application No. 15 / 774,471, both of which are incorporated herein by reference. In such an exemplary arrangement including a microstimulator as the IPG2133, the stimulation leads 2117 may be omitted (while still retaining the stimulation electrodes 2112) or the stimulation leads 2117 may be significantly shortened.

[0192] Delivery of stimulation signals to upper airway patency-associated nerves 2105 via such neurostimulation devices (2133, 2112) can cause contraction of at least some upper airway patency muscles (e.g., genioglossus) to maintain or restore upper airway patency at least to the tongue protrusion, thereby providing therapeutic treatment for obstructive sleep apnea. At least some further exemplary implementations of such stimulation are described at least in connection with FIGS. 32-42.

[0193] In some examples, an exemplary microstimulator may be implanted in the patient's head and neck region (e.g., 2103) to sense at least a portion of desired sleep-wake related information that may be used to perform a sleep-wake determination. In some examples, the sleep-wake determination may be used to implement, control, adjust, etc., treatment for sleep-disordered breathing through neural stimulation of upper airway patency-related nerves, muscles, tissues, etc. Exemplary implementations of at least some of the head and neck implanted microstimulators may take the forms described below in connection with at least FIGS. 31B-31F.

[0194] In some examples, a device implanted in the head and neck region, such as those described below in Figures 31B-31F, may include a sensing element forming part of and / or associated with a microstimulator. In some such examples, the sensing element may be used to determine sleep-wake status through detection of cardiac signals, such as heart rate, based on an ECG or arterial motion. In some examples, the sensing element may be used to determine sleep-wake status through detection of respiratory signals, such as respiratory motion, or a subset of such motion that may be considered sounds, including, but not limited to, snoring. In some such examples, the sensing element may detect both cardiac and respiratory signals.

[0195] In some examples, whether involving a microstimulator or other implantable pulse generator (IPG2133 in FIG. 31A), changes to the sensed signal after and / or during stimulation can be used to quantify therapeutic effectiveness and / or implement automatic titration of stimulation, as further described below in connection with at least FIG. 32.

[0196] In some examples, the stimulating electrode 2112 may also serve as a sensing element for sensing physiological information. In some such examples, the electrode 2112 may act as the sole sensing element for sensing physiological information via the sensing portion 2000 ( FIG. 30A ) or the sensing portion 2510 ( FIG. 32 ), such as a single channel EEG electrode or a single channel ECG electrode, or other sensing modality. As described below, in some examples, the stimulating electrode 2112 may be used for sensing in combination with other sensing elements and / or sensing modalities.

[0197] In some examples, the stimulation lead body 2118 may include a sensing element (e.g., electrode) 2120, which may act as the sole sensing element for sensing physiological information, such as cardiac information, EEG information, EMG information, movement information, etc., in accordance with sensing portion 2000 ( FIG. 30A ), 2510 ( FIG. 32 ); thus, in some examples, the sensing element 2120 may include an accelerometer. However, in some examples, the sensing element (e.g., electrode) 2120 may be considered the sole sensing element when used in connection with a conductive outer portion (e.g., at least a portion of a case / housing) of an implantable stimulation device (e.g., an IPG or microstimulator).

[0198] In some examples, the single / single sensor may include a pressure sensor (e.g., 2037 in FIG. 30A), but in some examples, the pressure sensed via sensor 2037 may be tracked, evaluated, etc. via pressure parameter 2537 in the sensing portion 2510 of care engine 2500 in FIG. 32.

[0199] In some examples, EMG information sensed via one of the electrodes (e.g., 2120, 2112, etc.) may include detecting upper airway patency and detecting (and / or assessing) inhalation / exhalation during breathing to assess obstruction (e.g., degree, location, etc.) and / or assess stimulation effectiveness. In some examples, the sensed EMG information may include sensing intercostal muscle activity to identify respiratory cycle information (e.g., inhalation, exhalation, expiratory pauses) and / or to identify or distinguish between central and obstructive sleep apnea.

[0200] However, in some examples, one or both of electrodes 2112 and 2120 may be used in conjunction with other sensing elements (eg, electrodes) to sense physiological information.

[0201] In some examples, the IPG 2133 includes a sensing element 2135. In some such examples, the sensing element 2135 is positioned on (or forms) a surface of the case of the IPG 2133, and one of both the electrodes 2112 and 2120 can be used in conjunction with the electrodes 2135 to measure bioimpedance (2036 in FIG. 30A ; 2536 in FIG. 32 ), to obtain ECG signals, EMG signals, etc., and / or to sense cardiac information (including cardiac morphology), respiratory information (including respiratory morphology), and / or movement / motion of the chest, neck, and / or head, etc.

[0202] In some such examples, the sensing element 2135 of the IPG 2133 may include an accelerometer, which may include a single-axis or multi-axis (e.g., three-axis) accelerometer. The accelerometer may be positioned internally within the IPG 2133, externally on the IPG 2133, or may extend a short distance from the IPG 2133 via a small lead body.

[0203] As discussed in connection with at least parameters 2026, 2526 in FIGS. 30A and 32, respectively, the accelerometer may be used to sense movement at or of the chest, neck, and / or head, cardiac information, respiratory information, etc. In some examples, the accelerometer may be used to sense physical activity / movement / movement, such as gross physical movement (e.g., walking, talking), which may indicate activity associated with wakefulness. Alternatively, sensing a lack of activity via the accelerometer may, in some examples, indicate a sleep state. In some such examples, the accelerometer may be used to sense physiological information for use in at least some of the example methods for determining a sleep-wake state, as previously described herein, without being used to sense posture or body position. However, in some examples, the accelerometer may be used to sense such posture or body position.

[0204] With further reference to FIG. 31A , in some examples of determining sleep-wake states, an electrode 2110 may be implanted subcutaneously in the head portion 2106 (e.g., above the shoulder) of the head and neck region 2103 to sense electrical brain activity and obtain EEG information and / or other central nervous system (CNS) information, with such sensed information being used to determine the sleep-wake state. Among other aspects, sleep onset, sleep termination, and / or various sleep stages may be determined via the sensed EEG information. In some examples, multiple electrodes 2110 may be placed subcutaneously around the head portion 2106 to sense such EEG information. In some examples, a single electrode 2110 may be used in combination with another electrode, such as a stimulating electrode 2112, to sense such EEG information. In some examples, the electrode 2110 may include a sole sensing element used to determine the sleep-wake state.

[0205] In some exemplary methods and / or devices for determining sleep-wake states, electrode 2114 can be implanted in or near the tongue 2115 to sense electromyogram (EMG) information. This sensed EMG information can include signals indicating voluntary control of the tongue (e.g., conversation, eating, etc.), which can then indicate wakefulness. Also, this sensed EMG information can include signals indicating sleep and / or sleep breathing disorders (e.g., obstructive events), for example, when the tongue can relax during a position where it obstructs the upper airway.

[0206] It will be understood that only some of the various electrodes shown in FIG. 31A are implanted or can exist in a particular exemplary implementation. Further, some of the electrodes (if present) can be used in combination with each other, while some of the electrodes can be used to implement a particular sensing modality periodically or selectively, not all of the time. For example, there can be a period during which some electrodes are used to sense one modality (e.g., cardiac information, such as ECG or others), while some electrodes are used to sense another modality (e.g., impedance) during some periods, and such periods can overlap, coincide by chance, or be independent of each other.

[0207] With this in mind, in some examples, one or more sensing modalities for determining the wake-sleep state can be implemented in some instances, while another, different sensing modality (or different combination of sensing modalities) can be implemented in other instances. For example, a particular sensing modality can be used alone or not very significantly during a portion of a day (e.g., normal waking period, such as 6 am to 10 pm, etc.), and then not used at all or not very significantly during another portion of the day (e.g., normal sleep period), or vice versa.

[0208] In some example methods and / or devices, a normal awake period may be identified via at least one of clinician input, patient input, a data model (e.g., machine learning, etc.), and other observational criteria. In an example of clinician input or patient input, a user may directly identify the start and / or end time of a normal awake period (and conversely, a normal sleep period). In some examples, a normal awake period (or conversely, a normal sleep period) may be determined at least in part via historical data for a particular patient and / or historical data for multiple patients or a general population. In some such examples, a data model (e.g., data model parameters 3230 in FIG. 32) may be built, trained, etc., and applied to the historical data to make the determination. In some such examples, the data model may include data obtained, used, etc., continuously, e.g., daily, using at least the most recent historical data (e.g., the past 30 days).

[0209] In some examples, as described below, the probability of sleep (or sleep-wake state) may be determined from among multiple sleep-wake state parameters, where different sleep-wake state parameters may be weighted differently. Such different weighting for a given sleep-wake state parameter may depend on the time of day, clinician / patient input, etc.

[0210] As represented schematically in Figure 31AA, in some examples, the IPG 2133 of Figure 31A may include multiple sensing elements (e.g., electrodes 2145, 2147) mounted on or formed as part of an outer surface (e.g., case) of the IPG 2133. As previously described elsewhere, this arrangement may be used to sense cardiac information (e.g., ECG, etc.), impedance, etc.

[0211] In some examples, the electrodes shown in FIGS. 31A , 31AA , whether mounted on a single housing (e.g., IPG 2133 in FIG. 31AA ) or located in multiple different locations (or on different components), can be used to sense cardiac information (including cardiac morphology), respiratory information (including respiratory morphology), chest and / or neck motion / movement, etc., as described in connection with sensing portion 2000 ( FIG. 30A ) and / or sensing portion 2510 ( FIG. 32 ). In some examples, this sensed information can include respiratory rate and / or heart rate. In some such examples, the sensed respiratory and / or cardiac information can include at least some of the substantially same features and attributes as described in connection with respiratory portion 2580 and / or cardiac portion 2600 in FIG. 32 .

[0212] In some instances, respiratory information is obtained through measuring transthoracic impedance via electrodes on the IPG alone or in addition to electrodes present on the surface of the IPG, but in some instances, respiratory information may be derived from ECG information.

[0213] With this in mind, in some examples described elsewhere in this disclosure, respiratory and / or cardiac information may be obtained via an accelerometer (2026 in FIG. 30A ), which may be located in the IPG 2133. As noted above, there may be times when an accelerometer is used to sense respiratory, cardiac, and / or other information to determine a sleep-wake state, while at other times sensing modalities other than an accelerometer (e.g., ECG electrodes, EMG electrodes, etc.) may be used to sense respiratory, cardiac, and / or other information to determine a sleep-wake state.

[0214] Unless specifically stated otherwise, it will be understood that the electrodes depicted in FIG. 31A include exposed conductive portions for contact with body tissue or the like within a patient.

[0215] In some examples, a single SDB care device includes a single housing. In some examples, a single device includes a built-in power source. In some examples, a single device includes multiple sensing elements (e.g., electrodes). In some examples, at least one sensing element (e.g., electrode) is located on two separate portions of the device. For example, one electrode may be located on the IPG 2133 while one electrode may be located on the stimulation lead body 2118.

[0216] As previously described in connection with at least Figures 31A-31AA, in some examples, an implantable pulse generator (IPG) may take the form of a microstimulator, which, as previously described, may be used to implement various sensing modalities. Exemplary implementations of at least some of such microstimulators are shown in at least Figures 31B and 31E.

[0217] As shown in the schematic representation 2300 in FIG. 31B , an exemplary device 2359 including an exemplary microstimulator 2355 may be implanted in a patient's head and neck region 2302, and specifically, in this example, in the neck region 2303. The microstimulator 2355 is implanted subcutaneously through an access incision 2311 in region 2354. In the particular illustrated example, a stimulation electrode 2310 is electrically connected to and extends from the microstimulator 2355, and the stimulation electrode 2310 is coupled to the nerve 2305 to stimulate the nerve, thereby causing contraction of musculature (e.g., the tongue) that maintains or restores upper airway patency to treat sleep-disordered breathing. In some examples, the stimulation electrode 2310 may include at least some of the substantially same features and attributes as the stimulation electrode 2112 in FIG. 31A , but in some examples, including acting as a sensing electrode.

[0218] As further shown in the schematic representation of exemplary device 2400 in FIG. 31C , in some examples, microstimulator 2355 may include at least one electrode (e.g., 2402 and / or 2404) as previously described, regarding which sensing vectors V1, V2, and / or V3 between electrodes 2310, 2402, 2404 may be established to sense a physiological phenomenon (e.g., ECG, bioimpedance, movement at or of neck 2303, etc.). This sensed physiological information may be used, among other things, to determine a sleep-wake state, such as to implement a stimulation therapy. It will be further understood that in some examples, additional sensing modalities (e.g., EMG) described in connection with FIGS. 30A , 31A, and 32 may be implemented via at least a portion of the microstimulator device of FIGS. 31B-31F. While not fully shown in FIG. 31B, FIG. 31C illustrates that the electrodes 2310 may be placed on leads 2313 extending from the microstimulator 2355.

[0219] As further shown in the schematic representation of the example device 2420 in FIG. 31D , in some examples, the microstimulator 2355 may include an accelerometer 2422, whereby sensing physiological information (e.g., via sensing at or movement of the neck, etc.) may be implemented as previously described throughout this disclosure. The microstimulator 2355 in FIG. 31D may also include electrodes 2402 (as in FIG. 31C ), whereby at least some of the sensing (e.g., cardiac, ECG, bioimpedance, movement, etc.) previously described may be implemented via the sensing vector V2. This sensed physiological information may be used, among other things, to determine a sleep-wake state, such as to implement stimulation therapy, etc.

[0220] 31E provides a schematic representation 2450 of an exemplary device 2459 including at least some of the substantially same features as the device of FIGS. 31B-31D, except further including a dedicated sensing lead 2433 extending into tissue (subcutaneously) from the microstimulator 2355 to support at least one electrode (e.g., 2431, 2432) spaced apart from the microstimulator 2355 and / or other electrodes (e.g., 2431, 2432, or 2404 in FIG. 31F). This arrangement can be used to sense physiological information (e.g., ECG, bioimpedance, movement at or of the neck 2303) via vectors V1, V2, V5, V6, and / or V7, as illustrated in schematic representation 2460 of FIG. 31F, in a manner similar to that described for at least the exemplary arrangements of FIGS. 31B-31D.

[0221] 31B-31F facilitate SDB care, including sleep-wake determination, in a compact arrangement where sensing, stimulation, implant access, etc. may be implemented in a single body region (e.g., neck) instead of being distributed among several body regions (e.g., neck and torso), thereby simplifying implantation and SDB care. For example, a microstimulator device positioned in the neck may sense physiological phenomena (e.g., respiration, heart, etc.) that may sometimes be primarily associated with a different region of the body (e.g., chest), while simultaneously conveniently positioning stimulation elements in the neck region where the microstimulator is positioned.

[0222] FIG. 32 is a block diagram that schematically represents an example care engine 2500. In some examples, the care engine 2500 may form part of the control portion 4000, at least as described below in connection with FIG. 34A, including, for example, without limitation, at least a portion of the instructions 4011 and / or information 4012. In some examples, the care engine 2500 may be used to implement at least a portion of various example devices and / or example methods of the present disclosure, as previously described in connection with FIGs. 1-31F and / or as described below in connection with FIGs. 33-42. In some examples, the care engine 2500 (FIG. 32) and / or the control portion 4000 (FIG. 34A) may form part of and / or be in communication with a pulse generator (e.g., 2133 in FIGS. 31A-31AA), whether such element includes a microstimulator or other arrangement.

[0223] In one aspect, at least the sensing portion 2510 of the care engine 2500 in FIG. 32 directs the sensing of information and / or receives, tracks, and / or evaluates sensed information obtained via one or more of the sensing modalities, sensing elements, etc. of the sensing portion 2000 (FIG. 30A), and the care engine 2500 uses such information to determine a sleep-wake state, among other functions, functions, etc., as further described below.

[0224] 32 , in some examples, the care engine 2500 includes a sensing portion 2510, a sleep state portion 2650, a sleep-disordered breathing (SDB) parameters portion 2800, and / or a stimulation portion 2900. In some examples, the sensing portion 2510 may include EEG parameters 2512 for sensing EEG information, such as a single channel (2514) or multiple channels of an EEG signal. Such sensed EEG information may be obtained via an EEG sensor 2012 ( FIG. 30A ) or may be derived from information sensed via another sensing modality. In some examples, the EEG information sensed by the parameters 2512 includes sleep state information. In some such examples, the sleep state information may include parameters provided in the sleep state portion 2650 of the care engine 2500, which will be described later.

[0225] In some examples, the sensing portion 2510 may include electro-oculogram (EOG) parameters 2524, which relate to receiving, tracking, evaluating, and / or indicating sensing of eye movement, eye position, etc., such as via an EOG sensor (e.g., 2024 in FIG. 30A ). In some such examples, the sensing element may include an optical sensor.

[0226] In some examples, this EOG information can be used as part of determining and / or confirming sleep state information, among other CNS information that can be used to detect, diagnose, and / or treat sleep-disordered breathing (SDB) behavior. For example, in some such examples, this EOG information can include detection and / or tracking of rapid eye movement (REM) during sleep via parameter 2668 (FIG. 32), which can then be used in distinguishing between wakefulness, REM, and / or other sleep states, including various sleep stages.

[0227] 32 , the care engine 2500 may, in some examples, include a sleep state portion 2650 for sensing and / or tracking sleep state information, which may be obtained via EEG information parameters 2512. In some examples, the sleep state portion 2650 may identify and / or track sleep onset (2660) (e.g., initial sleep onset or sleep onset after WASO) and / or wakefulness (2662) (e.g., WASO), as well as identify and / or track sleep stages once the patient falls asleep. Accordingly, in some examples, the sleep state portion 2650 includes sleep stage parameters 2666 for identifying and / or tracking the patient's various sleep stages (e.g., REM and N1, N2, N3 or S1, S2, S3, S4) during the treatment portion or over a longer period of time. In some instances, various stages other than REM sleep (e.g., N1-N3 or S1-S4) may sometimes be referred to as non-REM sleep. Sleep state portion 2650 may also, in some examples, include separate rapid eye movement (REM) parameters 2668 for sensing and / or tracking REM information in connection with various aspects of sleep-disordered breathing (SDB) care, as described further below and throughout various examples of this disclosure. In some examples, REM parameters 2668 may form part of or be used with sleep stage parameters 2666.

[0228] In some examples, sleep state portion 2650 may include arousal parameters 2664 to indicate sensing and / or receiving, tracking, evaluating, etc., the patient's arousal state (e.g., WASO). The patient's arousal state may indicate general non-sleep periods (e.g., daytime) and / or interrupted sleep events, such as macro-arousals (per parameters 2672 of arousal state parameters 2670) associated with the patient waking to use the toilet (e.g., urinate), turning over in bed, waking in the morning to turn off their alarm, etc.

[0229] Conversely, in some examples, sleep state portion 2650 may include microarousals parameters 2674 to detect and / or track neurological arousals associated with sleep disordered breathing (SDB) events in which a patient experiences brief neurological arousals due to sleep apnea, such as, but not limited to, obstructive sleep apnea, central sleep apnea, and / or hypopnea. Such SDB-related microarousals typically do not result in the patient waking up in the traditional sense familiar to the general public. In at least some examples, stimulation intensity within a treatment period does not vary in response to such SDB-related microarousals, because one goal of treatment is to prevent or substantially reduce sleep disordered breathing through electrical stimulation, which in turn may reduce the frequency and amount of such SDB-related microarousals.

[0230] In some examples, a sleep detection method / device, at least via the sleep state portion 2650 of the care engine 2500, can distinguish between wakefulness and sleep-disordered breathing (SDB), which occurs during sleep. Among other situations, this distinction can enable effective neural stimulation therapy, such as when a patient is in a sleep position (e.g., lying horizontally or in an inclined position) and the sleep detection configuration detects a change in sensed data that could potentially be interpreted as a rollover (e.g., from supine to their side (e.g., lateral position), or vice versa) or as consistent with SDB behavior. In the event of a genuine rollover by the patient, such as when getting out of bed, the system will pause neural stimulation therapy. However, if the detected change can be confirmed as a legitimate SDB behavior, then the system / method, at least in some examples, will not pause neural stimulation therapy.

[0231] With this in mind, in some examples, the device / method may distinguish between REM sleep (even in the absence of sleep-disordered breathing (SDB)) and wakefulness, at least because the system avoids pausing neural stimulation therapy for sleep-disordered breathing if the patient is in REM sleep. Conversely, if the patient is actually awake, the system may act not to initiate neural stimulation therapy or to pause or terminate neural stimulation therapy. In some examples, one characteristic feature associated with REM is a lack of bodily movement, which may sometimes be referred to as a paralysis or at least partial paralysis of voluntary muscle control.

[0232] In some examples, sleep-disordered breathing may occur during REM sleep, but at least some exemplary devices / methods allow sleep-disordered breathing to be distinguished from wakefulness and / or REM sleep to be distinguished from wakefulness. For example, in some such examples, sensing a lack of physical movement may prevent false positives when / if other parameters (e.g., HR) may otherwise indicate wakefulness. For example, during REM sleep, sensed information may indicate increased variability in breathing periods and / or in the patient's heart rate (HR).

[0233] In some such examples, and as previously described, the sleep state information (via the sleep state portion 2650) may be used to indicate, receive, track, evaluate, diagnose, etc., sleep-disordered breathing (SDB) behavior. In some such examples, and as previously described, the sleep state information may be used in a closed-loop manner to initiate, terminate, and / or adjust stimulation therapy for treating sleep-disordered breathing (SDB) behavior to enhance the effectiveness of the device. At least some example closed-loop implementations are further described below in connection with at least parameters 2910 in FIG. 32 .

[0234] For example, in some examples, stimulation therapy may be automatically terminated via sensing arousal (2664 in sleep state portion 2650). In some examples, stimulation therapy may be automatically initiated via sensing initiation of a particular sleep stage (2666). In some examples, the intensity of stimulation therapy may be adjusted and implemented according to particular sleep stages and / or particular characteristics within a sleep stage. In some examples, lower stimulation intensity levels may be implemented upon detecting a REM sleep stage. In some examples, stimulation intensity may be reduced in some sleep stages to conserve power and battery life and improve patient comfort and / or therapy utilization.

[0235] In some examples, in cooperation with at least the sleep stage parameters 2666 of the care engine 2500, the delivery of the stimulation signal can be switched between different predetermined intensity levels for each different sleep stage (e.g., N1, N2, N3 or S1, S2, S3, S4, REM).

[0236] 32, in some examples, the sensing portion 2510 of the care engine 2500 includes ECG parameters 2520, EMG parameters 2522, accelerometer parameters 2526, pressure parameters 2537, temperature parameters 2538, acoustic parameters 2539, and directs and / or receives, tracks, evaluates, etc., sensing signals from the ECG sensors 2020, EMG sensors 2022, accelerometers 2026, pressure sensors 2037, temperature sensors 2038, and / or acoustic sensors 2039 previously described in connection with FIG. 30A. In some examples, the EMG parameters 2522 may include detecting muscle activity and / or movement in the intercostal muscles, upper airway, and / or tongue, such as, for example, those described in connection with at least FIG. 31A and other examples throughout this disclosure.

[0237] In some examples, the sensing portion 2510 of the care engine 2500 (FIG. 32) includes impedance parameters 2536 to sense and / or track impedance within the patient's body to sense movement at or on the chest and / or neck and / or other parameters to determine sleep-wake state. In addition to, or instead of being used to determine sleep-wake state, the impedance parameters 2536 may also be used to sense respiratory information and / or other information in connection with sleep-disordered breathing (SDB) care. The impedance parameters 2536 may obtain impedance information from the impedance sensor 2036 in FIG. 30A and / or other sensors.

[0238] In some examples, the sensing portion 2510 of the care engine 2500 may include posture parameters 2540 to direct and / or receive, track, evaluate, etc., sensing of signals from posture sensors 2040, as previously described, in FIG. 30A or other posture, body position sensors, etc. Like other parameters of the sensing portion 2510, the posture parameters 2540 may be used alone or in combination with other parameters to determine a patient's sleep-wake state. As previously mentioned, however, in some example methods (and / or devices), sleep-wake state determination may be made without (or independent of) posture information.

[0239] In some examples, the sensing portion 2510 of the care engine 2500 includes snore parameters 2545 for directing the sensing of and / or receiving, tracking, evaluating, etc., snore information, which in some examples may be detected and obtained via motion sensing. This sensed snore information may in some examples be used to at least partially determine a sleep-wake state. In one aspect, snoring may be defined as a noise associated with each exhalation where the breathing periods are relatively stable and accompanied by stable frequency content. Conversely, speech lacks stable breathing periods and frequency content and therefore would not be detected as a snore. As discussed elsewhere, in some examples, snoring is sensed via an acoustic sensor 2039 ( FIG. 30A ) and / or acoustic parameters 2539 ( FIG. 32 ).

[0240] In some examples, the sensing portion 2510 of the care engine 2500 may include history parameters 2542 in which a history of sensed physiological information is maintained, which may be used to compare recent sensed physiological information with older sensed physiological information via comparison parameters 2544. At least some example implementations of using such history parameters 2542 and comparison parameters 2544 are described in connection with at least FIGS.

[0241] In some examples, at least some example methods of determining sleep-wake states via the care engine 2500 may include identifying sleep via trends (including variability) in respiration rate and / or in heart rate. In some examples, determining sleep-wake states may include identifying sleep via respiratory cycle morphology, via stability in respiration rate, and / or stability in respiration morphology. At least some of these examples are further described below in connection with at least the respiratory portion 2580 of the care engine 2500.

[0242] As shown in FIG. 32, in some examples, the care engine 2500 may include a respiratory portion 2580. In at least some examples, the respiratory portion 2580 may direct and / or receive, track, and / or evaluate sensing of respiratory morphology, including general patterns and / or specific criteria within respiratory signals, in general conditions. In some examples, the respiratory portion 2580 may operate in cooperation with or as part of the sensing portion 2510 and / or sensing portion 2000 (FIG. 30A) of the care engine 2500 in FIG. 32. At least some aspects of such respiratory morphology managed via the respiratory portion 2580 may include inhalation morphology (parameter 2582) and / or exhalation morphology (parameter 2584). In some examples, the respective inspiratory morphology parameters 2582 and / or expiratory morphology parameters 2584 may include amplitude, duration, peak (2586), onset (2588), and / or end (2590) of the respective inspiratory and / or expiratory phases of the patient's respiratory cycle. In some examples, the detected respiratory morphology may include transition morphology (2592), such as an inspiratory-to-expiratory transition and / or an expiratory-to-inspiration transition. In some examples, any one or more of these aspects (e.g., peak, onset, end, amplitude, etc.) of the respective inspiratory and expiratory phases may be used to at least partially determine sleep and / or wakefulness.

[0243] For example, the inhalation-to-exhalation transition associated with the respiratory portion 2580 of the care engine 2500 may be used as a criterion for detecting and / or tracking respiratory rate (and respiratory rate variability), which may indicate changes in wake-sleep state. In some examples, changes in the duration of the inhalation-to-exhalation transition, changes in peak-to-peak amplitude, and / or changes in respiratory rate may be indicative of sleep and / or wakefulness and thus may be used to determine the sleep-wake state.

[0244] With regard to sensing, tracking, etc. of respiratory morphology as described above, Figure 33 is a diagram 3350 that schematically depicts a respiratory cycle 3360 illustrating at least some aspects of respiratory morphology, with the respiratory cycle 3360 including an inspiratory phase 3362 and an expiratory phase 3370. The inspiratory phase 3362 includes an initial portion 3364 (e.g., beginning), an inspiratory peak 3365, and an end portion 3366 (e.g., end), while the expiratory phase 3370 includes an initial portion 3374 (e.g., beginning), an intermediate portion 3375 (including an expiratory peak 3377), and an end portion 3376 (e.g., end). The peak parameters 2586, start parameters 2588, and end parameters 2590 (in the respiratory portion 2580 of the care engine 2500) described above for the inhalation morphology 2582 correspond to the inhalation peak 3365, inhalation start 3364, and inhalation end 3366 of the respiratory cycle diagram 3350 in FIG. 33, while the peak parameters 2586, start parameters 2588, and end parameters 2590 (in the respiratory portion 2580 of the care engine 2500) described above for the exhalation morphology parameters 2584 correspond to the exhalation peak 3377, exhalation start 3374, and exhalation end 3376 of the respiratory cycle diagram 3350 in FIG. 33.

[0245] In the respiratory cycle diagram 3350 in FIG. 33 , a first transition 3380 occurs at the junction between the end inhalation portion 3366 and the early exhalation portion 3374. In some instances, this transition 3380 may sometimes be referred to as the inhalation-to-exhalation transition 3380, which may be used to determine a sleep-wake state by the parameters 2592 of the respiratory portion 2580 of the care engine 2500 in FIG. 32 , as described above. A second transition 3382 occurs at the junction between the end exhalation portion 3376 and the early inhalation portion 3364. In some instances, this transition 3376 may sometimes be referred to as the exhalation-to-inhalation transition 3376, which may be used to determine a sleep-wake state by the parameters 2592 of the respiratory portion 2580 of the care engine 2500 in FIG. 32 , as described above.

[0246] 32 , the respiratory portion 2580 may include chest wall parameters 2594 to indicate sensing of, and / or to receive, track, evaluate, etc., chest wall behavior of a patient. In some such examples, chest wall behavior may include chest wall motion (e.g., rib cage motion). In some examples, the sensed chest wall motion (e.g., used in determining a sleep-wake state) may include general chest wall motion (e.g., rise and fall) associated with inspiration and expiration of a respiratory cycle as the patient breathes. In some instances, this chest wall motion may include intercostal muscle contractions. In some examples, this sensed general chest wall motion (e.g., used in determining a sleep-wake state) does not include features, such as chest muscle contractions and / or signal information (that may be unrelated to respiratory and / or cardiac function). Among other uses, the sensed chest motion may be used to determine respiratory information, cardiac information, and / or other physiological information to determine a sleep-wake state, as further described throughout various examples of the present disclosure. For example, one use of sensed chest movement is to determine, at least in part, whether breathing is passive or active (e.g., forced), which can then be used to determine a sleep-wake state. As just one exemplary aspect of passive breathing, normal exhalation occurs without direct muscular effort, as during normal rhythmic breathing when air can be expelled from the lungs as a result of the recoil effect of elastic tissue in the chest, lungs, and diaphragm. This behavior would be expected in a sleep state. In contrast, an example of active breathing that may be associated with a wake state includes forced exhalation, which involves contraction of the abdominal wall, internal intercostal muscles, and diaphragm.

[0247] 32, the respiration portion 2580 may include neck parameters 2595 for indicating and / or receiving, tracking, evaluating, etc., sensed movement of the patient's neck, which may indicate respiratory and / or cardiac information about the patient, which may be used to determine a sleep-wake state. As previously described, such sensed movement of and / or at the neck may include movement, such as (but not limited to) movement from the airways and / or blood vessels, impedance, and / or other physiological phenomena. For example, at least a portion of the sensed impedance vector may be measured across the airways, across the blood vessels, and / or both.

[0248] In some examples, the respiratory portion 2580 may include respiratory rate parameters 2596 for sensing and / or receiving, tracking, evaluating, etc., respiratory rate information including respiratory rate, respiratory rate variability 2597, etc., which may be used to determine a sleep-wake state or a change in a sleep-wake state. In some examples, sensing respiratory rate (and any associated variability, trend, etc.) may be implemented via sensing and tracking one of the above-described identifiable parameters of respiratory morphology (e.g., peak, onset, end, transition) by the respiratory portion 2580 of the care engine 2500.

[0249] As shown in FIG. 32 , in some examples, the care engine 2500 may include a cardiac portion 2600. In some examples, the cardiac portion 2600 may be used to sense, track, determine, etc., cardiac information, which may indicate sleep-wake status, among other information relevant to SDB care, in general conditions. In some examples, the cardiac portion 2600 may operate in cooperation with or as part of the sensing portion 2510 and / or the sensing portion 2000 ( FIG. 30A ) of the care engine 2500 ( FIG. 32 ). The cardiac portion 2600 may be used alone or in combination with other elements, modalities, etc. of the care engine 2500. In some examples, the cardiac portion 2600 may use a single type of sensing or multiple types of sensing in the sensing portion 2510, and in some examples, the cardiac portion 2600 may use other sensing types, modalities, etc. in addition to or as an alternative to the particular sensing type, modality of the sensing portion 2510. Additionally, the cardiac portion 2600 may determine, track, etc., sleep-wake states in cooperation with or independently of the respiratory portion 2580 of the care engine 2500.

[0250] In some examples, in general conditions, the cardiac portion 2600 may direct sensing of, and / or receive, track, evaluate, etc., cardiac signal morphology to at least determine a sleep-wake state. As shown in FIG. 32 , in some examples, the cardiac portion 2600 includes atrial morphology parameters 2610 and / or ventricular morphology parameters 2612 that may be used alone or in combination to determine a sleep-wake state. In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include detecting atrial and ventricular contractions (parameter 2620) and / or relaxations (parameter 2622), respectively. In some such examples, tracking the respective contractions and / or relaxations may facilitate determining a sleep-wake state by providing readily identifiable portions of the cardiac waveform from which heart rate (HR) or heart rate variability (HRV) may be detected and tracked, from which heart rate or heart rate variability values, trends, etc., may be indicative of sleep or wakefulness.

[0251] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include peak atrial or ventricular contractions (2630) that may be used to determine a sleep-wake state.

[0252] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include an onset (e.g., beginning) 2632 of an atrial contraction, atrial relaxation, a ventricular contraction, or a ventricular relaxation. In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include an end (e.g., termination, completion) 2634 of an atrial contraction, atrial relaxation, a ventricular contraction, or a ventricular relaxation.

[0253] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) from which a sleep-wake state may be detected may include a combination of atrial and ventricular contractions.

[0254] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include transitions (2640), such as transitions between different phases of the cardiac cycle.

[0255] In some examples, at least some aspects of the cardiac information (from which a sleep-wake state may be determined) may include opening and / or closing of cardiac valves per parameters 2642. In some examples, such detection of opening and / or closing of cardiac valves (per parameters 2642) may also be used to help determine the timing and / or occurrence of the onset and / or end of atrial or ventricular contraction (or relaxation) in conjunction with parameters 2610, 2612, 2620, 2622, 2630, 2632, 2634.

[0256] In some examples, cardiac information may include cardiac motion 2644, from which the cardiac morphological parameters described above may be determined. Cardiac motion 2644 may be obtained via one or more of the various sensing modalities (e.g., accelerometer, EMG, etc.) described in connection with at least FIG.

[0257] 32 , in some examples, the cardiac information may direct sensing of and / or receive, track, evaluate, etc., heart rate information including heart rate parameters 2645, including heart rate (HR), heart rate variability (HRV) 2646, etc., which may be used to determine a sleep-wake state or a change in a sleep-wake state. In some examples, sensing heart rate (and any associated variability, trend, etc.) may be implemented via sensing and tracking one of the above-described identifiable parameters (e.g., peak, onset, end, transition) of cardiac morphology by cardiac portion 2600.

[0258] In some examples, at least some of the cardiac information described above may be determined, at least in part, according to heart sounds (e.g., S1, S2, etc.) that may be acoustically sensed (e.g., 2039 in FIG. 30A; 2539 in FIG. 32).

[0259] In some examples, the sleep-wake state may be determined via a combination of sensed breath color and sensed cardiac features. At least some aspects of the use of this combination of information are previously described in connection with at least FIGS. 13-20 and elsewhere throughout the examples of this disclosure.

[0260] 32 , in some examples, the care engine 2500 includes a sleep-disordered breathing (SDB) parameters portion 2800 for directing the sensing of, and / or receiving, tracking, evaluating, etc., parameters specifically associated with SDB care. For example, in some examples, the SDB parameters portion 2800 may include a sleep quality portion 2810 for sensing and / or tracking a patient's sleep quality, particularly in relation to the patient's sleep-disordered breathing behavior. Accordingly, in some examples, the sleep quality portion 2810 includes arousal state parameters 2812 for sensing and / or tracking arousals caused by sleep-disordered breathing (SDB) events, along with the number, frequency, duration, etc., of such arousals indicative of sleep quality (or lack thereof). In some such examples, such arousal states may correspond to microarousals described in connection with at least parameters 2674 in the sleep state portion 2650 of the care engine 2500 in FIG. 32 .

[0261] In some examples, the sleep quality portion 2810 includes state parameters 2814 to sense and / or track the occurrence of the patient's various sleep states (including sleep stages) during a treatment period or over a longer period of time. In some such examples, the state parameters 2814 may cooperate with, form part of, and / or include at least some of the substantially same features and attributes as the sleep state portion 2650 of the care engine 2500.

[0262] In some examples, SDB parameter portion 2800 includes AHI parameters 2830 for sensing and / or tracking apnea-hypopnea index (AHI) information, which may be indicative of the patient's sleep quality. In some examples, AHI information is sensed throughout each of the different sleep stages experienced by the patient, and such sensed AHI information is at least partially indicative of the degree of sleep-disordered breathing (SDB) behavior. In some examples, AHI information is obtained via sensing elements, such as one or more of various sensing types, modalities, etc., in association with at least sensing portion 2000 ( FIG. 30A ) and / or sensing portion 2510 ( FIG. 32 ), which may be implemented as described in various examples of the present disclosure. In some examples, AHI information may be sensed via sensing elements, such as an accelerometer positioned in either the torso or chin / neck region, which may be positionable and implemented as described in various examples of the present disclosure. In some examples, a combination of accelerometer-based sensing and other types of sensing may be used to sense and / or track AHI information. In some instances, AHI information is obtained via sensing modalities other than via an accelerometer (eg, ECG, impedance, EMG, etc.).

[0263] In some examples, the determination of the sleep-wake state may be implemented via the probability portion 3200 of the care engine 2500 in FIG. 32. In some such examples, via selection parameters 3210, the probability portion 3200 may allow for the selective inclusion or exclusion of at least some sleep-wake determination parameters without directly affecting the general operation of determining the sleep-wake state. In some examples, via the probability function 3200, the sensitivity parameters 3220 may be adjusted by the patient, clinician, or caregiver to increase or decrease the sensitivity of determining the sleep-wake state via a particular parameter. In some examples, data model parameters 3230 (e.g., machine learning) may be implemented to evaluate and modify adjustments to the probabilistic determination of the sleep-wake state, including, but not limited to, adjustments to any amplitude thresholds, duration thresholds, etc. associated with the probabilistic determination of the sleep-wake state. In some cases, the use of such probabilistic determinations may allow for more granular control of the patient's individual signals (used in combination to make a sleep-wake state determination), which in turn may allow for balancing simple control with complex control capabilities and sensor flexibility, if desired.

[0264] In some examples, the care engine 2500, via at least the data model parameters 3230, may include and / or have access to neural network resources (e.g., deep learning, convolutional neural networks, etc.) to identify patterns indicative of sleep from a single sensor or multiple sensors. As described further below in connection with at least FIGS. 40-42, data models may be constructed and / or trained via other exemplary methods. In some examples, decision tree-based expert resources may also be used to combine sensor or neural network outputs with other signals, such as time of day or remote input / usage. One exemplary implementation of using data models (e.g., machine learning, etc.), such as via the parameters 3230, is further described below in connection with at least FIGS. 40-42. At least some other exemplary implementations are described throughout this disclosure.

[0265] In some examples, via the probability portion 3200, the care engine 2500 may assign and apply weights (parameters 3240) associated with each sensor signal to increase (or decrease) the relative importance of that particular signal in determining the sleep-wake state.

[0266] In some examples, different thresholds may be selected for different times of a 24-hour daily period via the temporal emphasis parameters 3250. For example, during a first period (e.g., daytime, such as noon), some parameters may be de-emphasized and / or other parameters may be emphasized, while during a second period (e.g., nighttime, such as 10 p.m.), some parameters may be emphasized in determining a sleep-wake state, while other parameters are de-emphasized. Alternatively, during a first period, the sensitivity of most or all parameters (for determining a sleep-wake state) may be decreased, while during a second period, the sensitivity of some or all parameters (for determining a sleep-wake state) may be increased.

[0267] In some such instances, this adjustability via the temporal emphasis parameters 3250 may enhance sleep-wake determination for patients with non-standard sleep periods, such as night shift workers (e.g., 11 PM - 7 AM), because their intended sleep period (e.g., 8 AM - 3 PM) conflicts with traditional sleep periods (e.g., 10 PM - 6 AM).

[0268] In some examples, the probability function 3200 of the care engine 2500 may implement a probabilistic determination of a sleep-wake state based on sensing movement at (or over) the chest, neck, and / or head. In some such examples, an accelerometer and / or other sensors (e.g., impedance, EMG, etc.) may be used to sense movement at (or over) the chest, neck, and / or head. In some such examples, sensing is performed via a sensor (e.g., an accelerometer) with multiple signal components (e.g., a multi-axis accelerometer) or captures a signal (e.g., an ECG) from which multiple different signals may be derived, depending on the different parameters 3260; an exemplary method may include dividing a signal associated with sensing physiological information into multiple different signals, each respective signal representing a different sleep-wake determination parameter. In other words, multiple components within the signal are distinguished into different, separate signals, each of which may indicate a sleep-wake state. The probability of a sleep-wake state is then determined based on evaluating each different signal associated with each different sleep-wake determination parameter. As mentioned above, in some examples, each different signal may include one axis of a multi-axis accelerometer (e.g., where each axis is orthogonal to the other axes) or may include a single-axis accelerometer (when multiple single-axis accelerometers are used). In some such examples, different processing methods or techniques may be applied to at least some of the signal components (e.g., sleep determination parameters).

[0269] 32 , in some examples, the care engine 2500 includes an activation portion 3000, which may generally control activation of a medical device, such as a pulse generator, whether implanted (e.g., an IPG) or external, or a combination thereof. In some such examples, neural stimulation delivery via the medical device may be automatically activated and terminated 3010, with such activation and termination being based on the sleep-wake state. In some such examples, the sleep-wake state is automatically determined via the care engine 2500.

[0270] Thus, in some examples, at least some example methods and / or devices for determining sleep-wake states via the automatic parameters 3010 may be used to automatically start a treatment period (e.g., upon automatically detecting sleep) and automatically end a treatment period (e.g., upon automatically detecting wakefulness).

[0271] In some examples where automatic sleep-wake state determination is unavailable or deactivated by the patient (or clinician or caregiver), then the remote parameters 3012 may cause a treatment period to include a period that begins when the patient uses the remote control to turn on the therapy device and ends when the patient turns off the device via the remote control. In some examples, the remote parameters 3012 may cause a treatment period to begin and / or end based on at least one of the level of ambient light sensed via the remote control, the level or type of movement sensed via the remote control, and / or a therapy activation (e.g., on, off) described above implemented via the remote control. In some examples, the remote control may include the remote control 4340 shown in FIG. 36 . It will be appreciated that in some examples, detecting the level of ambient light and / or the level or type of movement of the remote control may be used as part of other features described herein to implement automatic sleep-wake state determination, which may in turn determine the automatic start, end, pause, adjustment, etc. of a treatment period during which neural stimulation therapy is applied. In some examples, the remote control parameters 3012 may be implemented in conjunction with the method 788 in FIG. 26I.

[0272] In some examples where automatic sleep-wake state determination is unavailable or deactivated by the patient (or clinician or caregiver), then the app parameters 3013 may cause a treatment period to include a period that begins when the patient uses the app to turn on the therapy device and ends when the patient turns off the device via the app. In some examples, the app parameters 3013 may cause a treatment period to begin and / or end based on at least one of the degree of ambient light sensed via the app, the degree or type of movement sensed by the mobile device, and / or a therapy activation (e.g., on, off) described above implemented via the app on the mobile device. In some examples, the app may include the app 4330 shown in FIG. 36, which may be implemented via a mobile device 4320 (FIG. 36), such as a mobile smartphone, tablet, phablet, smartwatch, etc. The mobile device may include a control portion, a user interface (e.g., a display) for operating the app, and the mobile device may include sensors for sensing the features described above (e.g., movement, ambient light, sound, etc.) in a manner that enables the app to perform a sleep-wake determination based at least in part on use (or non-use) of the mobile device.

[0273] In some examples, the sensors of the remote control and / or mobile device may include an accelerometer, a gyroscope, and / or other motion detector.

[0274] However, in some cases where automatic sleep-wake state determination is unavailable (or deactivated), via the temporal parameters 3014, the treatment period may automatically begin at a selectable predetermined start time (e.g., 10:00 PM) and end at a selectable predetermined stop time (e.g., 6:00 AM).

[0275] In one aspect, the treatment period corresponds to a period during which the patient is asleep, so that stimulation of upper airway patency-related nerves and / or central sleep apnea-related nerves is generally not perceived by the patient, and so that the stimulation is consistent with patient behavior (e.g., sleep) that would be expected to result in sleep-disordered breathing behavior (e.g., central or obstructive sleep apnea). Thus, to avoid enabling stimulation before the patient falls asleep, in some examples, stimulation can be enabled during the treatment period after expiration of a timer started upon automatic sleep detection. To avoid continuing stimulation after the patient awakens, stimulation can be disabled upon automatic awakening detection. Thus, at least in some examples, these periods can be considered outside of the treatment period, or as the respective activation and gradual termination portions of the treatment period.

[0276] In some examples, via boundary parameters 3016, a selectable predetermined first time marker (e.g., 10:00 PM) may be used as a limit or boundary to prevent automatic initiation of a treatment period (based on automatic detection of sleep) before the first time marker, while a selectable predetermined second time marker (e.g., 6:00 AM) may be used as a limit or boundary to ensure automatic termination of a treatment period to prevent continuation of the treatment period after the second time marker. Through such exemplary arrangements, a treatment period may be automatically initiated via automatic sleep detection and / or automatically terminated via automatic wake detection, while providing the patient with assurance of a treatment period not starting during a normal wake period or extending beyond their normal sleep period.

[0277] In some examples, determining the sleep-wake state in relation to the boundary parameter 3016 may include and / or be combined with at least the traits and attributes as previously described in relation to the method 780 in FIG. 26A and in relation to the temperature parameter 2038 (FIG. 30A) as previously described.

[0278] However, in some instances, via the physical parameters 3018, a user may take physical steps to cause activation (or deactivation) of a treatment period for the implantable medical device. For example, via the activation portion 3000 and the physical parameters 3018, the care engine 2500 may receive physical input, such as a tap on the chest (or neck or head) or on the implant, to activate or deactivate the device. Alternatively, a user may use the patient remote control function 3012 to activate or deactivate the implantable medical device, which may in turn activate or deactivate the delivery of neurostimulation. In some such instances, activation or deactivation of a treatment period (at which neurostimulation is applied) may be implemented via physical movement of a remote control or mobile device (e.g., hosting an app). In some instances, via a clinical programmer or remote control, this physical feature (3018) may be activated or deactivated at the discretion of the clinician or user.

[0279] 32, in some examples, the care engine 2500 includes a stimulation portion 2900 to control stimulation of a target tissue, such as, but not limited to, the upper airway nerve, to treat sleep disordered breathing (SDB) behavior. In some examples, the stimulation portion 2900 includes closed-loop parameters 2910 to deliver stimulation therapy in a closed-loop manner such that the delivered stimulation is in response to and / or based on sensed patient physiological information.

[0280] In some examples, closed-loop parameters 2910 may be implemented as using sensed information to control specific timing of stimulation according to respiratory information, where stimulation pulses are triggered by or synchronized with specific portions (e.g., respiratory phases) of the patient's respiratory cycle. In some such examples, and as previously described, this respiratory information may be determined via a single type of sensing or multiple types of sensing via sensing portion 2000 (FIG. 30A) and sensing portion 2510 (FIG. 32).

[0281] In some examples where sensed physiological information enables determining (at least) a sleep-wake state, closed-loop parameters 2910 may be implemented to initiate, maintain, pause, adjust, and / or terminate stimulation therapy based on the determined sleep-wake state (including the particular sleep stage).

[0282] 32 , in some examples, stimulation portion 2900 includes open-loop parameters 2925 whereby stimulation therapy is applied without a feedback loop of sensed physiological information. In some such examples, in open-loop mode, stimulation therapy is applied during a treatment period without (e.g., independently of) sensed information regarding the patient's sleep quality, sleep state, respiratory phase, AHI, etc. In some such examples, in open-loop mode, stimulation therapy is applied during a treatment period without (i.e., independently of) specific knowledge of the patient's respiratory cycle information.

[0283] However, in some such instances, some sensory feedback may be utilized to generally determine whether the patient should receive stimulation based on the severity of their sleep apnea behavior.

[0284] As further shown in FIG. 32, in some examples, the stimulation portion 2900 includes auto-titration parameters 2920, such that the intensity of the stimulation therapy can be automatically titrated (i.e., adjusted) to be more intense (e.g., higher amplitude, higher frequency, and / or higher pulse width) or less intense (e.g., lower amplitude, lower frequency, and / or lower pulse width) within a treatment period.

[0285] In some such examples, and as previously described, such automatic titration may be implemented based on sleep quality and / or sleep state information, which may in some examples be obtained via sensed physiological information. It will be understood that such examples may be used with synchronization of stimulation to sensed respiratory information (i.e., closed-loop stimulation) or without synchronization of stimulation to sensed respiratory information (i.e., open-loop stimulation).

[0286] In some examples, at least some aspects of the auto-titration parameters 2920 may include and / or be implemented via at least some of the substantially same features and attributes as Christopherson et al., SYSTEM FOR TREATING SLEEP DISORDERED BREATHING, issued January 20, 2015 as US8,938,299, which is hereby incorporated by reference in its entirety.

[0287] With regard to various examples of the present disclosure, in some instances, delivering stimulation to an upper airway patency nerve causes contraction of upper airway patency-associated muscles. In some such instances, the contraction comprises suprathreshold stimulation, as opposed to subthreshold stimulation (e.g., simple tension) of such muscles. In one aspect, suprathreshold intensity levels correspond to stimulation energies greater than the nerve excitation threshold, such that this suprathreshold stimulation may provide maximal upper airway clearance (i.e., patency) and efficacy of obstructive sleep apnea treatment.

[0288] In some examples, at least some exemplary methods may include identifying, maintaining, and / or optimizing a target stimulation intensity (e.g., a therapeutic level) without intentionally identifying a stimulation discomfort threshold at the time of implantation or at a later time point after implantation.

[0289] In some examples, when determining sleep according to a minimum predetermined confidence level, the amplitude (e.g., intensity) of the stimulation signal may start at a lower value and then increase to a higher value in a ramping manner. In some such examples, the increase in amplitude (to a desired / target value) may be made dependent on an additional or further predetermined confidence level. However, if it is later determined that sleep is not occurring, but rather that the patient is in a quiet, restful wakeful state, then the stimulation may be terminated or ramped down while still in the ramping phase before reaching the target stimulation amplitude. Among other applications, this exemplary method may be beneficial for patients with cardiac or respiratory disorders. At least because cardiac and / or respiratory morphologies (from which sleep can be detected) may be more complex, making accurate detection of actual sleep more difficult in such patients.

[0290] As discussed above in connection with the boundary parameters 3016 of the activation portion 3000, a clock or timekeeping element within (or in communication with) a medical device (e.g., which may be implanted in some examples, such as (but not limited to) the IPG2133) may be used to implement boundaries or limits for when stimulation therapy (within a treatment period) may be automatically initiated or terminated via automatic sleep detection (or wake detection) by determining a sleep-wake state. In some examples, the time-based boundaries may be based on patient behavior and / or direct clinician programming. In some examples, such tracked patient behavior may be used as input to a probabilistic model that determines the sleep-wake state. In some examples, the time-based boundaries may also be based at least in part on historical patient activity.

[0291] In some examples, the time-based boundaries may account for daylight saving time and travel (e.g., different time zones) and may be adjusted via a patient remote control or physical tapping on the chest. In some such examples, the time-based boundary parameters may include one of multiple inputs used to determine the sleep-wake state, which may increase reliability in determining the sleep-wake state in various environments (rather than a single time-location environment, such as only the patient's bedroom).

[0292] In some examples, the boundary parameters 3016 of the activation portion 3000 in FIG. 32 may include criteria that are not strictly time-based (e.g., time of day). For example, in some examples, the boundary parameters 3016 may be implemented based on the number, type, and / or duration of various sleep stages associated with a single treatment period (e.g., nighttime sleep). For example, an exemplary method may determine a boundary or termination limit for a treatment period according to observing a specific number (e.g., 4 or 5) of REM sleep periods, stage 4 sleep periods, or stage 3 sleep periods, etc. In some such examples, the number of specific sleep stage periods may be selectable. In some examples, the boundary may be based on a selectable percentage that the patient spends in one or more specific sleep stages.

[0293] In some examples, upon detecting the sleep state (by the sleep-wake state), a neural stimulation signal can be applied to the phrenic nerve to treat central sleep apnea. In some examples, determining the sleep-wake state can be used to control (in a coordinated manner with respect to each other) the onset and / or termination of stimulation of both the upper airway patency nerve (e.g., the hypoglossal nerve) and the diaphragm control nerve to treat sleep-disordered breathing.

[0294] In some examples, the stimulation portion 2900 may operate in cooperation with at least the respiratory portion 2580 and / or sensing portion 2510 of the care engine 2500 (e.g., in conjunction with the sensing portion 2000 in FIG. 30A ) to determine the effectiveness of stimulation and / or whether flow limitation exists by evaluating the flow response within a single respiratory cycle. Such evaluation is in contrast to performing such evaluation on a cycle-by-cycle basis, such as by looking at the respiratory signal from the peak of the inspiratory phase of one cycle to the peak of the inspiratory phase of another cycle.

[0295] For example, in an exemplary method, if the stimulation portion 2900 causes a change (e.g., an increase or decrease) in stimulation intensity level during the inspiratory phase, one feature of the care engine 2500 may include determining whether a substantial change in flow response (e.g., 10%, 15%, 20%, or more) has occurred.

[0296] In some such examples of the stimulation portion 2900 in assessing whether stimulation therapy is effective (based on the flow response of the inspiratory phase within a single respiratory cycle, from cycle to cycle), some exemplary methods may include determining whether a change (e.g., a substantial change) in the flow response occurs at the complete end of stimulation or at the start of stimulation during the inspiratory phase of a single respiratory cycle.

[0297] In some examples, the care engine 2500 in FIG. 32 may include an initial use function 3100, which may automatically enhance the determination of sleep-wake states. In some such examples, via the initial use function 3100, a method and / or device for SDB care may omit a manual training period and instead automatically "standardize" the use of the method and / or device for a particular patient. For example, in some examples, the determination of sleep-wake states may begin using default parameters or may begin using parameters collected at the time of implantation of the SDB care device in the patient. In some examples, the determination of sleep-wake states may initially be performed without default parameters. In some such examples, if a wakeful state is detected, the sensing portion 2000 ( FIG. 30A ) and / or the care engine 2500 ( FIG. 32 ) may collect respiratory information, movement information, and / or posture information associated with a wakeful state, which may then enable more sensitive detection of sleep when determining the sleep-wake state. In some examples, detecting arousal may include detecting gross body movements, such as, but not limited to, walking, swallowing, trunk movements, etc. In some examples, the gravity vector is established when implanting the SDB care device.

[0298] With this in mind, depending on the initial usage function 3100, such automatic standardization may include forgoing the use of absolute thresholds, but instead performing sleep-wake state determinations (e.g., sleep onset detection) based on percentage changes in sensed values. Additionally, in some examples, sensing of various physiological phenomena (e.g., respiration, cardiac, etc.) may be used to determine the highest or lowest values ​​of such physiological phenomena, and then use the values ​​at the ends of such ranges to adjust thresholds accordingly.

[0299] It will be understood that the various parameters, functions, portions, etc. shown and described in connection with Figure 32 are not limited to the particular groupings, relationships, etc. shown in Figure 32, but may be arranged in groupings, relationships, etc. other than those shown in Figure 32. Furthermore, it will be understood that the care engine 2500 (or portions thereof) in Figure 32 may be implemented using only some (i.e., not all) of the portions, elements, parameters, etc. shown in Figure 32.

[0300] With reference to at least the care engine 2500 in FIG. 32 and the exemplary methods and / or devices described throughout this disclosure, it will be understood that such engines, methods, and / or devices (and components, portions thereof) for determining sleep-wake states may also be used to quantify activity levels and assess associated health parameters.

[0301] 34A is a block diagram that schematically illustrates an example control portion 4000. In some examples, the control portion 4000 provides one example implementation of a control portion that forms part of, implements, and / or generally manages stimulation elements, power / control elements (e.g., pulse generators, microstimulators), sensors, and related elements, devices, user interfaces, instructions, information, engines, elements, functions, actions, and / or methods as described throughout the examples of this disclosure in connection with FIGS.

[0302] In some examples, the control portion 4000 includes a controller 4002 and a memory 4010. In general terms, the controller 4002 of the control portion 4000 includes at least one processor 4004 and associated memory. The controller 4002 is electrically coupled to and in communication with the memory 4010 to generate control signals to direct the operation of at least some of the stimulation elements, power / control elements (e.g., pulse generators, microstimulators), sensors, and related elements, devices, user interfaces, instructions, information, engines, elements, functions, actions, and / or methods, as described throughout the examples of this disclosure. In some examples, these generated control signals include using instructions 4011 and / or information 4012 stored in the memory 4010 to at least determine the patient's sleep-wake state, including, but not limited to, in some examples, specific sleep stages. In some examples, the sleep-wake determination may include determining early sleep onset, wake after sleep onset (WASO), and / or wake after sleep onset (e.g., WASO). Such sleep-wake determination may include portions that prescribe and manage treatment for sleep-disordered breathing, such as obstructive sleep apnea, hypopnea, and / or central sleep apnea, but may also include sensing physiological information, including, but not limited to, electrical brain activity, respiratory information, cardiac information, and / or monitoring sleep-disordered breathing, etc., as described throughout examples of this disclosure in connection with FIGS. 1A-33 and 34B-42. In some instances, the controller 4002 or control portion 4000 may sometimes be referred to as being programmed to perform the above-identified acts, functions, etc., such that the controller 4002, control portion 4000, and any associated processor may sometimes be referred to as being a special-purpose computer, control portion, controller, or processor. In some examples, at least a portion of the stored instructions 4011 may be implemented as or referred to as a care engine, sensing engine, monitoring engine, and / or treatment engine.In some examples, at least a portion of the stored instructions 4011 and / or information 4012 may form and / or be referred to as at least a portion of a care engine, a sensing engine, a monitoring engine, and / or a treatment engine.

[0303] In response to or based on commands received via a user interface (e.g., user interface 4040 in FIG. 35 ) and / or via machine-readable instructions, controller 4002 generates control signals as described above, according to at least some of the examples of the present disclosure. In some examples, controller 4002 is embodied in a general-purpose computing device, while in some examples, controller 4002 is incorporated into or associated with at least some of the stimulation elements, power / control elements (e.g., pulse generators, microstimulators), sensors, and related elements, devices, user interfaces, instructions, information, engines, functions, actions, and / or methods, etc., as described throughout the examples of the present disclosure.

[0304] For purposes of this application, the term “processor,” in reference to controller 4002, shall mean a currently or future-developed processor (or processing resource) that executes machine-readable instructions contained in a memory. In some examples, execution of machine-readable instructions, such as those provided via memory 4010 of control portion 4000, causes the processor to operate controller 4002 to perform the actions identified above, such as implementing sensing, monitoring, determining, processing, etc., as generally described in (or consistent with) at least some examples of the present disclosure. The machine-readable instructions may be loaded into random access memory (RAM) for execution by the processor from their storage location in read-only memory (ROM), a mass storage device, or some other persistent storage (e.g., a non-transitory or non-volatile tangible medium), as represented by memory 4010. In some examples, the machine-readable instructions may include a sequence of instructions, a processor-executable data model (e.g., machine learning, etc.), or the like. In some examples, memory 4010 includes a computer-readable tangible medium that provides non-volatile storage of machine-readable instructions executable by a process of controller 4002. In some examples, computer-readable tangible medium may sometimes be referred to as and / or may include at least a portion thereof as a computer program product. In some examples, hardwired circuitry may be used in place of or in combination with machine-readable instructions to implement the described functions. For example, controller 4002 may be embodied as part of at least one application-specific integrated circuit (ASIC), at least one field-programmable gate array (FPGA), and / or the like. In at least some examples, controller 4002 is not limited to any particular combination of hardware circuitry and machine-readable instructions, nor is it limited to any particular source for the machine-readable instructions executed by controller 4002.

[0305] In some examples, the control portion 4000 may be implemented entirely within or by a stand-alone device.

[0306] In some examples, the control portion 4000 may be partially implemented in one of the sensing device, monitoring device, stimulation device, apnea treatment device (or portion thereof), etc., and may be partially implemented in a computing resource that is separate and independent from, but in communication with, the apnea treatment device (or portion thereof). For example, in some examples, the control portion 4000 may be implemented via a server accessible via the cloud and / or other network path. In some examples, the control portion 4000 may be distributed or distributed among multiple devices or resources, such as between a server, an apnea treatment device (or portion thereof), and / or a user interface.

[0307] In some examples, the control portion 4000 includes and / or is in communication with a user interface 4040, as shown in FIG.

[0308] FIG. 34B is a diagram schematically illustrating an example implementation of at least a portion of the control portion 4020 in which the control portion 4000 (FIG. 34A) can be implemented, according to one example of the present disclosure. In some examples, the control portion 4020 is implemented entirely within or by an IPG assembly 4025, which has at least some of the substantially same features and attributes as a pulse generator (e.g., power / control element, microstimulator), as previously described throughout this disclosure. In some examples, the control portion 4020 is implemented entirely within or by a remote control 4030 (e.g., programmer) outside the patient's body, such as a patient control 4032 and / or a physician control 4034. In some examples, the control portion 4000 is implemented partially in the IPG assembly 4025 and partially in the remote control 4030 (at least one of the patient control 4032 and the physician control 4034).

[0309] FIG. 35 is a block diagram that schematically illustrates a user interface 4040 according to one example of the present disclosure. In some examples, the user interface 4040 forms part of and / or is accessible via a device external to the patient, by which the treatment system may be at least partially controlled and / or monitored. The external device hosting the user interface 4040 may be a patient remote (e.g., 4032 in FIG. 34B ), a physician remote (e.g., 4034 in FIG. 34B ), and / or a clinician portal. In some examples, the user interface 4040 includes a user interface or other display that provides simultaneous display, activation, and / or operation of at least some of the stimulation elements, power / control elements (e.g., pulse generators, microstimulators), sensors, and associated elements, devices, user interfaces, instructions, information, engines, functions, actions, and / or methods, etc., as described in connection with FIGS. 1A-42 . In some examples, at least some portions or aspects of the user interface 4040 may be provided via a graphical user interface (GUI), which may include a display 4044 and an input 4042 .

[0310] FIG. 36 is a block diagram 4300 that schematically illustrates some example implementations in which a medical device (MD) 4310, such as a pulse generator and / or a sensing monitor (either or both of which may be implantable in some examples), may communicate wirelessly with an external device external to the patient. As shown in FIG. 36 , in some examples, the IMD 4310 may communicate with at least one of a patient app 4330 on a mobile device 4320, a patient remote control 4340, a clinician programmer 4350, and a patient management tool 4360. The patient management tool 4360 may be implemented via a cloud-based portal 4362, the patient app 4330, and / or the patient remote control 4340. Among other types of data, these communication arrangements enable the IMD 4310 to communicate, display, manage, etc., sleep / wake data for patient management, as well as enable adjustments to detection methods if / when necessary.

[0311] It will be appreciated that at least some of the various devices / elements 4320, 4340, 4350, patient management tools 4360 may also communicate with each other, with or without communicating with the medical device 4310.

[0312] 37A, in some examples, the user interface 4040 of FIG. 35 may also display and / or report the use of ramped onset, ramped transitions during and off of therapy, and / or ramped termination of stimulation over a given treatment period. For example, as shown in the schematic representation in FIG. 37A, the day display portion 5400 may include various graphic identifiers, such as awake periods 5050, automatic onset (e.g., auto-start) instances 5070, on periods 5075, etc.

[0313] In some exemplary methods, at least a portion of the initiation, cessation, and pause of stimulation within a treatment period may be implemented in a ramped manner, and display portion 5400 in FIG. 37A schematically represents these implementations. For example, automatic initiation of stimulation may include a ramped increase (from zero) to a target stimulation intensity, as represented via a triangular ramp symbol shown at 5070. This display immediately indicates to the viewer the ramped manner in which the stimulation intensity has been implemented. The ramped increase may occur at the start of a treatment period (e.g., 5405). Similarly, a triangular ramp symbol 5410 represents a ramped decrease in stimulation intensity from a target level (or another non-zero level) to zero, such as when stimulation is terminated (e.g., at 5406) ​​or when stimulation is paused (e.g., at 5080). It will be understood that the indication of a ramped increase or decrease in stimulation intensity may be implemented via shapes other than a triangle.

[0314] A gradual ramped onset or termination of stimulation therapy may enhance patient comfort by avoiding abrupt onset, cessation, or discontinuation of stimulation therapy. Among other features, a ramped implementation may increase the likelihood of patient compliance and awareness of SDB care.

[0315] In some examples, at least some of the features and attributes associated with at least the methods and / or devices depicted via Figure 37A may be implemented via at least some features and attributes of the example methods described later herein in connection with Figures 37B-39. In some examples, the methods described in connection with Figures 37B-39 may be implemented via devices and elements other than those shown in at least Figure 37A.

[0316] It will be understood that Figure 37A schematically represents at least some aspects of the experience, operation of a device, and / or method of treating a patient for sleep apnea. Accordingly, at least some aspects of Figure 37A are schematically represented via a method, such as an exemplary method, as shown at 5500 in Figure 37B, that includes automatically taking action when the probability of sleep exceeds a sleep detection threshold according to a sleep-wake state determination or the probability of wakefulness exceeds a wakefulness detection threshold according to a sleep-wake state determination. In some examples, automatically taking action includes at least one of automatically starting a stimulation treatment period and automatically stopping a stimulation treatment period, as shown at 5510 in Figure 37C. In some such examples, the term "non-sleep" may correspond to the probability of sleep remaining below the sleep detection threshold, while in some such examples, the term "non-wakefulness" may correspond to the probability of wakefulness remaining below the wakefulness detection threshold.

[0317] In some such examples (at 5510 in FIG. 37C ), the exemplary method may further include receiving an input to selectively start a treatment period and / or selectively stop a treatment period, as shown at 5520 in FIG. 38 ; and suspending automatic initiation upon receiving the input to selectively start, and suspending automatic termination upon receiving the input to selectively stop.

[0318] In some examples, as shown at 5530 in Figure 39, a method (associated with the acts / methods in Figures 37B-38) may further include tracking at least one of the following patterns, trends, and averages for at least one of automatic initiation; automatic termination; selective initiation; and selective termination for a plurality of nighttime usage periods. It will be understood that other (or additional) nighttime usage parameters described in connection with Figure 37A may be tracked by method 5530 in Figure 39.

[0319] 40 is a block diagram that schematically represents an example arrangement 7400 for implementing a data model, such as (but not limited to) a machine learning model to support and / or implement the determination of sleep-wake states (e.g., early sleep onset, WASO, and / or sleep onset after WASO). In some examples, the data model may include machine learning elements, which may include convolutional neural networks, deep neural networks, deep neural learning, and the like. It will be understood that in some examples, the machine learning elements may be implemented via other forms of artificial intelligence tools. The data model elements may be implemented as part of the data model parameters 3230 in FIG. 32 or in a complementary manner thereto.

[0320] In some examples, the data models may include heuristic or other data models that may be manually adjusted. For example, the inputs and outputs of a heuristic or other data model may be manually selected and / or the weights applied to each input and / or output may be manually adjusted. In some examples, the heuristic or other data model may be manually adjusted by a physician, patient, and / or others based on observations of and / or from the patient (e.g., sleep studies), feedback (e.g., surveys), etc.

[0321] In some examples, such data model arrangements may be used in analyzing sensed physiological phenomena (e.g., respiratory signals, cardiac signals, etc.) to determine patterns indicative of sleep states (e.g., onset, onset delay, variation in onset delay, termination, various sleep stages) and / or patterns indicative of arousal (e.g., onset, termination). In some examples, at least a portion of this analysis may include comparing stored signal patterns with current or recent signal patterns.

[0322] The output of the data model arrangement 7400 may be provided to or as a global sleep-wake state determination at 7643. It will be appreciated that in some examples, the output of the data model arrangement 7400 may be the sole basis on which the global sleep-wake determination is implemented. However, in some examples, the output of the data model arrangement 7400 may comprise just one input in the global sleep-wake determination.

[0323] In some examples, the data model arrangement 7400 depicted in FIG. 40 may include a trained (or constructed) data model (e.g., a trained deep learning model), which may be trained (or constructed) prior to its operation. As further shown in the example arrangement (e.g., example method or device) in FIG. 40, in some examples, training may be performed at least in part via resources 7410. In some examples, resources 7410 may be external to the patient's body and / or to the medical device 7420 (whether implantable and / or external). The medical device 7420 may include sensors (e.g., sensors of sensing portion 2000 of FIG. 30A) and control portion 4000 (FIG. 34A), among other components, features, etc. In some examples, after such training, the trained (or constructed) data model may be imported into the medical device 7420 for use in determining sleep-wake state and / or sleep onset latency information (including, but not limited to, initial sleep onset, WASO, and sleep onset after WASO).

[0324] In some examples, resources 7410 ( FIG. 40 ) may include computing resources 7414 sized and scaled to perform various forms of training / building and / or maintaining data models. In some examples, resources 7410 may include a data store 7412, such as (without limitation) a large dataset of stored sleep information for many patients, which may include, for example, acceleration signal component information related to different non-physiological and physiological parameters, such as, but not limited to, cardiac information, respiratory information, motion / activity information, posture information, etc. It will be understood that any one or more of the sensor modalities disclosed within and throughout this disclosure may also contribute to data store 7412. In some examples, the stored sleep-related data may be patient-specific, whereby a trained data model may be imported, such as into or as an element within a medical device (e.g., 7420).

[0325] With this in mind, in some examples, data model elements may be trained (i.e., constructed) via resources 7410 according to the example arrangement (e.g., method and / or device) 7500 in Figure 41. As shown in Figure 41, known inputs 7510 sensed via accelerometers (e.g., implantable in some examples) and / or other sensing modalities and known outputs 7540 are both provided to trainable (or constructable) data model 7530. It will be appreciated that in some examples, at least a portion of the various sensing modalities of known inputs 7510 may be external to the patient. In some examples, known output 7540 may include a determined sleep-wake state 7542 (e.g., such as that used to determine initial sleep onset, WASO, and sleep onset after WASO), which may include any number of internally measurable and / or externally measurable physiological parameters used to determine the sleep-wake state, such as, but not limited to, any one (or combination thereof) of EEG, EOG, EMG, ECG, cardiac information, respiratory information, movement / activity, posture, etc.

[0326] 41 , in some examples, at least some of the known inputs (obtained via accelerometer or other sensors) may include a wide variety of sensed physiological signals and / or information (e.g., sensing portion 2000), such as, but not limited to, cardiac information 7512, respiratory information 7514, movement / activity information 7516, posture information 7518, and / or other information 7519. These inputs are merely examples, and it will be understood that the known inputs (from accelerometer signals or other sensors) may include any sensed physiological information relevant to determining a sleep-wake state.

[0327] By providing such known inputs (7510) and known outputs (7540) to the trainable data model 7530, a trained data model 7631 (FIG. 42) may be obtained. In some examples, only one or a portion of the known inputs 7510 may be used, while in some examples, all of the known inputs 7510 may be used. As noted elsewhere, the trainable / trained data models (7530, 7631) may include deep learning models.

[0328] 42 is a schematic diagram illustrating an example method 7600 (and / or an example device) for using a trained (or constructed) data model 7631 to determine a sleep-wake state (e.g., initial sleep onset, WASO, and post-WASO sleep onset) using internal measurements, such as (without limitation) in some examples via an accelerometer (e.g., implantable and / or external), and / or other internal or external measurements, such as any one or more of the sensing modalities described in and throughout this disclosure. As shown in FIG. 42, current sensed inputs 7611 are fed into the trained data model 7631, which then produces a determinable output 7641, such as a current sleep-wake state determination 7643, based on the current inputs 7611. In some examples, the current inputs 7611 correspond to the same type and / or number of known inputs 7510 ( FIG. 41 ) used to train the data model. In some instances, only one or a portion of the current inputs 7611 may be used, while in some instances all of the current inputs 7611 may be used.

[0329] As previously mentioned, once the trained data model 7631 is obtained, in some examples it is imported into and / or otherwise forms part of the control portion 4000 in FIG. 34A (and / or the care engine 2500 in FIG. 32).

[0330] In some examples, other information 7519 (shown in FIGS. 41-42) may include inputs from external sensors, etc. associated with, for example, the remote control 4340, the app 4330 on the mobile consumer device 4320, etc. (as shown in FIGS. 36 and 34B) and / or the remote, app, physical parameters 3012, 3013, 3018 in FIG. 32. External sensors / inputs may include ambient light, movement / manipulation of the remote control or of the app / mobile consumer device, etc. Other inputs may include time of day, time zone, geographic latitude, etc., but are as previously described in connection with at least FIGS. 26E-26F, temporal parameters 3014 (FIG. 32), boundary parameters 3016 (FIG. 32), and the like, relating to inputs used to determine the sleep-wake state, at least in part, pursuant to detecting the probability of sleep and / or the probability of wakefulness.

[0331] In some examples, implementing at least some aspects of the example methods and / or devices described in connection with Figures 1A-42 may include using, determining at least some of the information therein, and / or implementing the methods in the examples of Figures 43-45B. Additionally, Figures 43-45B may also include example implementations of at least some of the features of the example methods and / or devices associated with Figures 1A-42.

[0332] FIG. 43 is a chart 8000 that schematically illustrates exemplary motion signals of a patient over a 90-minute period. In some examples, the motion signals may be obtained from a sensing element (e.g., internal element 128 of FIG. 1B and / or external sensors 171, 150 of FIG. 1B) or a sensing portion (e.g., 2000 of FIG. 30A). In some examples, the motion signals may correspond to measurement signal 284 of FIG. 3A and measurement signal 294 of FIG. 3B. Chart 8000 includes patient states on a first vertical axis 8002, including AWAKE, FALL, SLEEP, DEEP, REM, and WAKE states. Chart 8000 also includes milligrams per second (mg / sec) on a logarithmic scale on a second vertical axis 8004 and time in minutes on a horizontal axis 8006. The chart 8000 includes a motion signal 8008 over time corresponding to a logarithmic scale and a patient condition signal 8009 over time corresponding to the patient's condition. The patient condition signal 8009 may be derived from the motion signal 8008. A portion 8030a of the motion signal 8008 is enlarged at 8030b.

[0333] In some examples, the motion signal 8008 may be obtained from a triaxial accelerometer by low-pass filtering the X, Y, and Z components and downsampling (anti-aliasing) the filtered components. The filtered components may be downsampled, for example, to a 2 Hz sample rate. Downsampling the filtered components may reduce power consumption for processing the motion signal. In some examples, instead of downsampling the accelerometer components, the accelerometer may directly provide X, Y, and Z component samples at a 2 Hz sample rate. The downsampled X, Y, and Z components are low-pass filtered and differentiated using a single filter (similar to a band-pass filter) to generate the velocity components. In some examples, the low-pass section of the filter may have a cutoff of approximately 0.06 Hz so that an average velocity over a period of approximately 15 seconds is calculated. The root-sum-square (RSS) of the X, Y, and Z velocity components may then be calculated to generate the motion signal 8008.

[0334] The magnitude of the peaks in the motion signal 8008 can be related to different types of patient motion. A peak value equal to approximately 10 mg / sec, indicated at 8010, is indicative of cardiac and respiratory motion. A peak value equal to approximately 100 mg / sec, indicated at 8012, is indicative of a respiratory event. A peak value equal to approximately 100 mg / sec, indicated at 8014, is indicative of a respiratory event. 3 A peak value equal to 10 mg / sec indicates a waking state. 4 Peak values ​​equal to mg / sec indicate postural changes or arousal movements.

[0335] 44A and 44B, the magnitude and duration of the peaks in the motion signal 8008 are indicative of the patient's state. Prior to time 8020, the patient is determined to be awake, as indicated by state signal 8009. Between times 8020 and 8022, the patient is determined to be asleep, and after time 8022, the patient is determined to be asleep.

[0336] FIG. 44A is a chart 8040 that schematically represents an example movement signal (e.g., 8008 of FIG. 43) for detecting patient sleep onset. In some examples, chart 8040 illustrates an example implementation for automatically initiating electrical stimulation in response to detecting early sleep onset or sleep onset after WASO, as previously described with reference to method 310 of FIG. 4B. Chart 8040 includes movement magnitude on vertical axis 8042 versus time on horizontal axis 8044. A timer reset method can be used to detect sleep onset by monitoring the decline in the magnitude of the movement signal peaks over time. Each peak in the movement signal casts a shadow, which is implemented by a counting down timer. Larger peaks, indicated at 8050, cast longer shadows, as indicated at 8051. Smaller peaks, indicated at 8052, 8054, and 8056, cast shorter shadows, indicated at 8053, 8055, and 8057, respectively. The percentage of shaded time over the last N minutes indicated by window 8060 is monitored, where "N" can be in the range between 3 and 10. If window 8060 is completely covered by the shadow from the large peak, the percentage is 100%. If the window is partially covered by one or more shadows from smaller peaks, the percentage drops to a lower value. If the shaded percentage drops below a threshold (e.g., 5%), a delay period is initiated as shown at 8062. If no large peaks (e.g., peaks that cast a shadow of more than a predefined percentage (e.g., 5%, 10%, 15%) of the current window) are detected during the delay period 8062, the patient is determined to be asleep (e.g., sleep onset is detected), and therapy may be initiated as shown at 8064. If a large peak is detected during the delay period 8062, the process is restarted with the current window.

[0337] FIG. 44B is a chart 8070 that schematically represents an example movement signal (e.g., 8008 of FIG. 43) for detecting a patient's wake after sleep onset (WASO). In some examples, chart 8070 illustrates an example implementation for automatically pausing and / or stopping electrical stimulation in response to detecting a WASO, as previously described with reference to method 312 of FIG. 4B. Chart 8070 includes movement magnitude on a vertical axis 8042 versus time on a horizontal axis 8044. Therapy can be automatically paused or automatically stopped based on the movement signal. WASO can be detected in response to the sustained occurrence of large peaks in the movement signal over a window of time. Sustained movement can distinguish arousals from mild arousals. At the same time, therapy should be paused quickly in response to the patient's arousal so that the patient does not need to manually pause therapy. Thus, therapy can be automatically paused for a short period of time in response to a single movement, but can be quickly restarted if sustained movement is not detected.

[0338] A peak below threshold 8071 (e.g., due to a respiratory event), as indicated by peak 8072, results in a determination that the patient remains asleep and that therapy may continue. A peak above threshold 8071, as indicated by peak 8074, results in therapy being paused for a window of time (e.g., 30 seconds), as indicated by 8075. If the peak does not exceed the threshold during this window of time, therapy is restarted. If another peak exceeds the threshold during this window of time, as indicated by peak 8076, the pause continues for another window of time, as indicated by window 8077. If therapy is paused for a threshold number of windows (e.g., three in this example), as indicated by window 8080, the patient is determined to be awake (e.g., a WASO is detected), and therapy is stopped, as indicated by 8082. With therapy paused, the timer reset method described above with reference to FIG. 44A may then be used to detect sleep onset after a WASO. In some examples, the threshold 8071 may be selected by a physician and / or based on a data model (e.g., 7631 in FIG. 42).

[0339] FIG. 45A is a diagram that schematically illustrates an example method 8100 for determining a patient's fall asleep. In some examples, method 8100 is an example implementation of the timer reset method previously described with reference to FIG. 44A. In some examples, method 8100 may be implemented by a control portion, such as control portion 4000 of FIG. 34A. The time in minutes is indicated at 8102. An input to method 8100 may be a sensor (e.g., a three-axis accelerometer) signal indicative of patient movement, such as from internal sensing element 128 of FIG. 1B, external sensors 171, 150 of FIG. 1B, and / or sensing portion 2000 of FIG. 30A. At time T0, the patient is determined to intend to sleep, for example, by manually indicating their intention to sleep (e.g., via a remote control, mobile device, user interface, etc.) or by sensing that the patient intends to sleep via internal sensors (e.g., accelerometer, gyroscope, microphone, etc.) and / or external sensors (e.g., accelerometer, light sensor, motion sensor, sleep mat, wearable device, air pressure sensor, low-power radar sensor, etc.). In response to determining that the patient intends to sleep at time T0, at 8110, any sensor signal values ​​received before time T1 are classified as indicating an awake state. Time T1 may be selected such that T1 minus T0 equals the minimum amount of time for the patient to fall asleep, such as within a range of 10 to 30 minutes. Thus, for a first predetermined period of time (i.e., T1 - T0) from determining that the patient intends to sleep, the patient is determined to be awake. At 8112, this first predetermined period of time may include a data quality check and a calibration period. During this calibration period, any value of the sensor signal is classified as indicative of a wakefulness state. The sensor signal values ​​collected during this calibration period may be checked against known values ​​of a data model (e.g., 7631 in FIG. 42) to ensure that the sensor signal quality is sufficient to detect sleep. If the sensor signal quality is sufficient, the calibration period may be used to further train the data model (e.g., 7530 in FIG. 41). After a predetermined period at time T1, active sleep detection may begin.

[0340] Steps 8114-8124 process the most recent N minutes of sensor signals, where "N" is in the range between 0.5 and 10 (e.g., 7). At 8114, the sensor signals are filtered (e.g., low-pass filtered or band-pass filtered). For example, the X, Y, and Z components of a 10-50 Hz three-channel accelerometer sensor signal may be band-pass filtered to provide a filtered signal. At 8116, the magnitude of motion is calculated from the filtered signal to provide a motion magnitude signal. In some examples, the magnitude of motion may be calculated over the previous M minutes, where "M" is in the range between 3 and 10 (e.g., 7). For example, the root-sum-square (RSS) of the X, Y, and Z components of the filtered three-channel accelerometer sensor signal may be calculated using all three channels to provide a motion magnitude signal. At 8118, the movement magnitude signal over the previous M minutes is downsampled to provide measurements every D seconds over the previous M minutes, where "D" is in the range between 1 and 10, such as 5 (e.g., 1 / 5 Hz). At 8120, the values ​​of the measurements from 8118 are checked to determine whether all values ​​are below a threshold (e.g., 6). In response to any values ​​exceeding the threshold, the patient is determined to be awake, and at 8122, the process waits W minutes, where W is in the range between 0.5 and 2 minutes, before repeating the process beginning at 8114. In response to all values ​​falling below the threshold, the patient is determined to be asleep (e.g., sleep onset is detected), and therapy can be initiated at 8124.

[0341] FIG. 45B is a diagram that schematically illustrates an example method 8200 for determining patient sleep onset. In some examples, method 8200 is an example implementation for automatically initiating electrical stimulation in response to detecting early sleep onset or sleep onset after WASO, as previously described with reference to method 310 of FIG. 4B. In some examples, method 8200 may be implemented by a control portion, such as control portion 4000 of FIG. 34A. Time in minutes is indicated at 8202. Input to method 8200 may be sensor (e.g., triaxial accelerometer) signals indicative of patient movement from internal sensing element 128 of FIG. 1B, external sensors 171, 150 of FIG. 1B, and / or sensing portion 2000 of FIG. 30A. At time T0, the patient is determined to intend to sleep, for example, by manually indicating that they intend to sleep (e.g., via a remote control, mobile device, user interface, etc.) or by sensing that the patient intends to sleep via internal sensors (e.g., accelerometer, gyroscope, microphone, etc.) and / or external sensors (e.g., accelerometer, light sensor, motion sensor, sleep mat, wearable device, air pressure sensor, low-power radar sensor, etc.). In response to determining that the patient intends to sleep at time T0, at 8210, any sensor signal values ​​received before time T1 are classified as indicating an awake state. Time T1 may be selected such that T1 minus T0 equals the minimum amount of time for the patient to fall asleep, such as within a range of 10 to 30 minutes. Thus, for a first predetermined period of time (i.e., T1 - T0) from determining that the patient intends to sleep, the patient is determined to be awake. At 8212, this first predetermined period of time may be used to select a subset of sensor signal channels based on the patient's posture. For example, if the patient is prone for the first two minutes and then on their left side for the remainder of the time, the accelerometer channel associated with those postures may be selected (e.g., the channel with the maximum value for each posture). In some examples, this first predetermined period may also include the data quality check and calibration period described above with reference to 8112 in FIG. 45A. After the predetermined period at time T1, active sleep detection may begin.

[0342] Steps 8214-8236 process the most recent N minutes of sensor signals, where N is in a range between 0.5 and 10 (e.g., 7). At 8214, the sensor signals are filtered (e.g., low-pass filtered). For example, the X, Y, and Z components of a 10-50 Hz three-channel accelerometer sensor signal may be low-pass filtered to provide a filtered signal. At 8216, an angle (e.g., relative to gravity) is calculated from the filtered signal corresponding to each selected channel over the period the patient was in that position to generate an angle signal. At 8218, a roll average of the angle signals from 8216 is calculated. In some examples, a second roll average of angle R is calculated over the previous M minutes, where "R" is in a range between 1 and 10 (e.g., 5) and "M" is in a range between 3 and 10 (e.g., 7). At 8220, the turnover average signal is downsampled to provide a measurement every D seconds over the previous M minutes, where "D" is in the range between 1 and 10, such as 5 (e.g., 1 / 5 Hz). At 8222, the absolute difference between successive measurements of the downsampled signal is calculated. At 8224, the absolute difference between successive measurements is normalized according to the mean and standard deviation of the data model (e.g., 7631 in FIG. 42).

[0343] At 8226, the current time is compared to time T2. Time T2 may be selected so that T2 minus T0 equals the maximum amount of time for the patient to fall asleep, such as within a range of 30 to 60 minutes. In response to the current time being less than T2, at 8228, the normalized values ​​from 8224 are checked to determine whether all values ​​are below a first threshold (e.g., 0). In response to any of the values ​​being above the first threshold, the patient is determined to be awake, and at 8230, the process waits W minutes, where "W" is within a range of 0.5 to 2 minutes, before repeating the process beginning at 8214. In response to all values ​​being below the first threshold, the patient is determined to be asleep (e.g., sleep onset is detected), and therapy may be initiated at 8232. In response to the current time being greater than T2 (e.g., greater than a second predetermined period) and the patient being jittery at 8226 (e.g., a substantial variation in the normalized values ​​from 8224), at 8234 the normalized values ​​from 8224 are checked to determine whether all values ​​are below a second threshold (e.g., 1). In response to any of the values ​​being above the second threshold, the patient is determined to be awake and at 8230 the process waits W minutes before repeating the process beginning at 8214. In response to all values ​​being below the second threshold, the patient is determined to be asleep (e.g., sleep onset is detected) and therapy may be initiated at 8236. Method 8200 accounts for cases where the patient is experiencing jittery or light sleep by increasing the threshold for detecting sleep onset after a second predetermined period (e.g., T2 - T0).

[0344] While specific examples have been illustrated and described herein, various alternative and / or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein.

Claims

1. 1. A method comprising: receiving, via the control portion, a plurality of inputs, each input corresponding to a different sleep-wake determination parameter; detecting, via the control portion, early sleep onset based on at least a first subset of the plurality of inputs and a first threshold for the at least first subset of the plurality of inputs; detecting wake after sleep onset (WASO) based on at least a second subset of the plurality of inputs and a second threshold for the at least a second subset of the plurality of inputs via the control portion; and detecting sleep onset after WASO based on at least a third subset of the plurality of inputs and a third threshold for the at least a third subset of the plurality of inputs; The method, wherein the third threshold is different from the first threshold and the second threshold.

2. The method of claim 1 , wherein the first threshold and / or the third threshold are time-dependent.

3. 3. The method of claim 2, wherein during a sleep period, the third threshold is initially at a minimum value at the beginning of the sleep period, increases from the minimum value to a maximum value, and then decreases from the maximum value back to the minimum value by the end of the sleep period.

4. The method of claim 1 , wherein the second subset of the plurality of inputs is different from the first subset of the plurality of inputs.

5. 5. The method of claim 4, wherein the first subset of the plurality of inputs comprises at least one of heart rate variability or body temperature, and the second subset of the plurality of inputs comprises at least one of locomotor inactivity during activity or sleep (LIDS).

6. 10. The method of claim 1, wherein the third subset of the plurality of inputs comprises at least one of activity, locomotor inactivity during sleep (LIDS), a light sensor signal, or time elapsed since initial sleep onset.

7. The method of claim 1 , wherein the first subset of the plurality of inputs is different from the third subset of the plurality of inputs.

8. automatically initiating electrical stimulation of upper airway patency-related nerves via the electrodes in response to detecting early sleep onset or sleep onset after WASO; and 10. The method of claim 1, further comprising automatically pausing electrical stimulation via the electrode in response to detecting a WASO.

9. The method of claim 1 , wherein the plurality of inputs includes an accelerometer sensor signal.

10. The method of claim 9 , wherein the plurality of inputs includes an angle of the accelerometer.

11. The plurality of inputs may include: Accelerometer sensor signal; physiological signals; environmental signals; time; or The method of claim 1 , including at least one of patient information.

12. The plurality of inputs may include: Breathing rate; Respiratory rate variability; electromyography (EMG); Microneurography; Heart rate; Heart rate variability; body temperature; posture; Activities; or 10. The method of claim 1, comprising at least one of locomotor inactivity during sleep (LIDS).

13. The plurality of inputs may include: geographical location; proximity to sleep areas; optical sensor signal; Noise sensor signal; motion sensor signals; the proximity and / or charging status of the patient's electronic devices; the time elapsed since initial sleep onset; or 10. The method of claim 1, comprising at least one of the patient's circadian rhythms.

14. The plurality of inputs may include: dietary intake; Meal timing; indicated drowsiness; Demographics; or The method of claim 1 , including patient input of at least one comorbidity.

15. 9. The method of claim 8, further comprising setting the first threshold, the second threshold, and the third threshold based on a general population model.

16. 16. The method of claim 15, further comprising adjusting the first threshold, the second threshold, and the third threshold based on patient feedback.

17. The patient feedback investigation; manual control of electrical stimulation; Polysomnography; home sleep testing; a wearable device; or 17. The method of claim 16, including at least one of a sleep mat.

18. The method of claim 1 , further comprising applying a different weight value to each of the plurality of inputs.

19. A medical device comprising: at least one sensor for sensing physiological information; and A control portion, receiving a plurality of inputs, each corresponding to a different sleep-wake determination parameter, the plurality of inputs including the sensed physiological information; detecting early sleep onset based on at least a first subset of the plurality of inputs and a first threshold for the at least first subset of the plurality of inputs; detecting wake after sleep onset (WASO) based on at least a second subset of the plurality of inputs and a second threshold for the at least second subset of the plurality of inputs; a control portion for detecting sleep onset after WASO based on at least a third subset of the plurality of inputs and a third threshold for the at least third subset of the plurality of inputs; The medical device, wherein the third threshold is different from the first threshold and the second threshold.

20. 20. The medical device of claim 19, wherein the medical device comprises an implantable medical device.

21. further comprising a pulse generator for applying the electrical stimulation; The control portion comprises: for automatically initiating a first function of the pulse generator in response to detecting early sleep onset or sleep onset after WASO; and 20. The medical device of claim 19, for automatically pausing the first function of the pulse generator in response to detecting a WASO.

22. 22. The medical device of claim 21, wherein the pulse generator comprises an implantable pulse generator.

23. 22. The medical device of claim 21, wherein the first function of the pulse generator is to apply electrical stimulation to upper airway patency-associated nerves.

24. 20. The medical device of claim 19, wherein the first threshold and / or the third threshold are time-dependent.

25. 25. The medical device of claim 24, wherein during a sleep period, the third threshold is initially at a minimum value at the beginning of the sleep period, increases from the minimum value to a maximum value, and then decreases from the maximum value back to the minimum value by the end of the sleep period.

26. 20. The medical device of claim 19, wherein the second subset of the plurality of inputs is different from the first subset of the plurality of inputs.

27. The at least one sensor for sensing physiological information comprises: an accelerometer sensor; or 20. The medical device of claim 19, comprising at least one temperature sensor.

28. 20. The medical device of claim 19, wherein the at least one sensor for sensing physiological information is implantable.

29. 20. The medical device of claim 19, wherein the at least one sensor for sensing physiological information is external to the patient's body.

30. 20. The medical device of claim 19, further comprising at least one sensor external to the patient's body for sensing environmental information.

31. The at least one sensor for sensing environmental information, optical sensors; a noise sensor; or 31. The medical device of claim 30, comprising at least one motion sensor.

32. A medical device comprising: a sensor for providing a sensor signal indicative of patient movement; and A control portion, calculating a magnitude of the sensor signal to generate a motion magnitude signal; downsampling the motion magnitude signal; and a control portion for detecting the onset of sleep in the patient in response to the movement magnitude signal remaining below a threshold for a predetermined period of time.

33. A medical device comprising: a sensor for providing a sensor signal indicative of patient movement; and A control portion, Calculating an angle with respect to gravity based on the sensor signal to generate an angle signal; downsampling the angle signal; calculating the difference between successive values ​​of the angle signal; and a control portion for detecting the patient's onset of sleep in response to the calculated difference remaining below a threshold for a predetermined period of time.

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