Sleep monitoring for sleep-disordered breathing (SDB) care

Abstract

Devices and / or methods for determining sleep-wake states, such as those associated with sleep-disordered breathing (SDB) care.

Description

[0001] A significant portion of the population suffers from various forms of sleep-disordered breathing (SDB). In some patients, external respiratory therapy devices and / or surgical intervention alone may not be able to treat sleep-disordered breathing behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0002] Figure 1 is a flow chart schematically illustrating an example method of determining sleep-wake states.

[0003] Figure 2A is a diagram schematically illustrating an example method of sensing physiological information by sensing motion.

[0004] Figure 2B is a diagram schematically illustrating an example method including sensing motion about a blood vessel.

[0005] Figure 3A 、 Figure 3B is a diagram schematically illustrating an example method of determining a sleep-wake state with respect to posture information.

[0006] Figure 3C is a diagram schematically illustrating an example method for determining sleep-wake states with respect to different example sensed physiological parameters.

[0007] Figure 4A is a flow chart schematically illustrating an example method of detecting sleep and / or maintaining stimulation therapy.

[0008] Figure 4B is a diagram schematically illustrating an example method of detecting sleep.

[0009] Figure 4C is a diagram schematically illustrating an example method including distinguishing body movements, postures, etc.

[0010] Figure 5 、 Figure 6 、 Figure 7 、 Figure 8 is a diagram schematically illustrating an example method for determining sleep-wake state with respect to respiratory phase information.

[0011] Figure 9 is a flow chart schematically illustrating an example method for determining sleep-wake state based on variability of a respiratory signal and / or a cardiac cycle.

[0012] Figures 10 to 14 is a diagram schematically illustrating an example method for determining sleep-wake state with respect to various example cardiac cycle characteristics, cardiac waveform morphologies, and the like.

[0013] FIG. 15A to FIG. 15B is a diagram schematically illustrating an example method for determining sleep-wake state relative to example motion information.

[0014] Figures 15C to 15F is a diagram schematically illustrating an example method for determining sleep-wake state with respect to various example cardiac cycle information.

[0015] 16A to 16B is a diagram schematically illustrating an example method for determining sleep-wake states by identifying variability in sensed physiological information relative to a threshold.

[0016] Figure 17 and Figure 18 is a diagram schematically illustrating an example method for determining sleep-wake states by tracking parameters related to time, activity, non-movement parameters, etc.

[0017] Figure 19 is a diagram schematically illustrating an example method for determining a sleep-wake state based on a sleep probability and / or a wake probability.

[0018] Figure 20A is a diagram schematically illustrating an example method for taking action to determine sleep-wake states based on sleep probability and / or wake probability.

[0019] Figures 20B to 20H is a diagram schematically illustrating examples of actions taken with respect to a method of determining sleep-wake state, including initiating or terminating stimulation, with respect to various example boundaries with respect to time, temperature, sleep stage, etc.

[0020] Figure 20I is a schematic representation of receiving at least Figures 20B to 20H A diagram of an example method for input of some example boundaries represented in .

[0021] Figure 21 is a diagram schematically illustrating an example method of determining sleep-wake state, the method including segmenting a sensing signal to enable evaluation of different sleep-wake determination parameters.

[0022] Figure 22 is a diagram schematically illustrating an example method for determining a sleep-wake state based on a sleep probability and / or a wake probability.

[0023] Figure 23A 、 Figure 23B 2 is a diagram schematically illustrating an example method of determining a sleep-wakefulness state based on wakefulness information and snoring information, respectively.

[0024] Figure 24 is a block diagram schematically representing an example apparatus and / or example sensing portion for use as part of an example method for determining sleep-wake states.

[0025] Figure 26is a block diagram schematically representing an example processing portion that may form part of and / or be in communication with an example sensing portion.

[0026] Figure 25A is a diagram including a front view schematically illustrating a patient's body and an example implantable medical device for treating sleep-disordered breathing and / or determining sleep-wake states.

[0027] Figure 25AA is a diagram including diagrams schematically illustrating a front view of an example implantable medical device having a sensor.

[0028] Figures 25B to 25F are diagrams schematically illustrating different example embodiments of an example implantable medical device as a micro stimulator implanted in the head and neck region.

[0029] Figure 27A is a block diagram schematically representing an example care engine.

[0030] Figure 27B is a diagram including graphs schematically showing example cardiac waveform morphologies.

[0031] Figure 27C is a diagram including graphs schematically showing example respiratory waveform morphologies.

[0032] Figure 27D and Figure 27E is a diagram schematically illustrating an example method of determining sleep-wake state, the method including determining a change in flow response associated with initiating, changing, or terminating a stimulus.

[0033] Figure 28A and Figure 28B is a block diagram schematically illustrating an example control portion.

[0034] Figure 29 is a block diagram schematically representing an example user interface.

[0035] Figure 30 is a block diagram schematically representing an example communication arrangement between an implantable medical device and an external device.

[0036] Figure 31A 、 Figure 31B is a diagram schematically illustrating an example user interface including example therapeutic usage modes, sleep-wake states, a sleep quality section, usage metrics, etc., which may be used in conjunction with example methods and / or example apparatus for determining sleep-wake states.

[0037] Figures 31C to 31D is a diagram schematically illustrating an example method including taking action regarding sleep-wake state determination.

[0038] Figure 31E is a diagram that schematically illustrates an example method of receiving input regarding initiating and / or stopping therapeutic treatment.

[0039] Figure 31F is a diagram schematically representing an example method including tracking information regarding use, initiation, cessation, etc. of a therapy.

[0040] Figure 31G is a diagram schematically illustrating an example method for determining the effectiveness of automatic sleep-wake determination.

[0041] Figure 31H is a diagram schematically illustrating an example method and / or example apparatus for displaying information regarding the use, start, stop, etc. of a therapy via a graphical user interface.

[0042] Figure 32 is a diagram schematically illustrating an example timeline of sleep-wake related events according to an example method of sleep-wake determination.

[0043] Figure 33 is a diagram schematically illustrating an example method and / or an example apparatus for determining sleep-wake states.

[0044] Figure 34 is a diagram schematically illustrating example methods and / or example apparatus, including an implantable medical device relative to external resources for determining sleep-wake states, including training a machine learning model, and the like.

[0045] Figure 35 is a diagram schematically illustrating an example method and / or example apparatus for training a machine learning model for determining sleep-wake states.

[0046] Figure 36 is a diagram schematically illustrating an example method and / or example apparatus for determining sleep-wake states based on a trained machine learning model. DETAILED DESCRIPTION

[0047] In the following detailed description, reference is made to the accompanying drawings, which form a part thereof, in which specific examples in which the present disclosure may be implemented are shown by way of illustration. It should 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. Therefore, the following detailed description should not be construed as limiting. It should be understood that, unless otherwise specifically stated, the features of the various examples described herein may be combined with each other in part or in whole.

[0048] At least some examples of the present disclosure relate to devices for diagnosis, treatment, and / or other care of medical conditions.At least some examples may include implantable devices and / or methods of using implantable devices.

[0049] At least some example devices and / or example methods may relate to sleep-disordered breathing (SBD) care, which may include monitoring, diagnosis, and / or stimulation therapy. In some such instances, SBD care may include automatically determining sleep-wake states, which in turn may include detecting sleep and / or detecting wakefulness. In some instances, detecting sleep includes detecting the onset of sleep and / or detecting ongoing sleep after the onset of sleep. In some such instances, sleep-wake determination may be used to initiate (and / or maintain) a treatment cycle in which sleep-disordered breathing is treated using neurostimulation therapy. In some instances, automatic detection of such wakefulness (as part of automatic sleep-wake determination) may be used to terminate a treatment cycle. In this way, the patient is able to forgo and / or significantly reduce daily use of the patient remote control, such that the patient remote control may be used less frequently to initiate and / or terminate a treatment cycle in which stimulation may be applied. In one aspect, this automatic detection of sleep onset, which may be used to initiate stimulation therapy within a treatment cycle, is distinct from sleep staging, which is used solely for diagnostic purposes.

[0050] In some such instances, such automatic sleep detection can be used in place of (or in combination with) other methods of identifying the start and / or end of a treatment cycle (e.g., a selectable predetermined date and time (e.g., 10 PM to 6 PM)) or the patient turning the device on (at the start of sleep) and off (at the end of sleep). Various example methods, elements, and / or devices that can be used in conjunction with or in place of automatic sleep detection to identify the start or end of a treatment cycle will be described later.

[0051] Considering at least some of the previously described examples, in some examples, a method for determining sleep-wake state includes detecting sleep at the following times: (1) a date and time; and (2) detecting a lack of body movement indicative of sleep within an optional predetermined time period. The date and time can be optional and / or can be based on patient data. In at least some examples, the date and time corresponds to a time period when the patient is likely to be sleeping or intending to sleep. Once at least these two criteria are met, the method includes initiating stimulation therapy at a low intensity and gradually increasing the intensity of the therapy to a target intensity level. Stimulation at the target intensity level continues as long as the sensed physiological information indicates that sleep is continuing. However, upon detection of patient body movement (indicative of wakefulness) or upon detection of the patient mechanically indicating wakefulness, the method terminates any stimulation therapy and remains in a no stimulation mode (or in some examples, a lower stimulation mode) for an optional predetermined time (e.g., 15 minutes). In other words, after an interruption, the method delays the resumption of stimulation therapy (or the change from low stimulation to target stimulation) for a set time period (e.g., 15 minutes). The length of the delay period is programmable. In some instances, the method can be performed using additional parameters, such as sensing additional physiological phenomena (e.g., breathing, heart, posture, etc.), implementing additional conditions, and / or enhancing the sensitivity or specificity of the physiological phenomena being sensed. For example, in one non-limiting example, the method can include detecting posture, and includes detecting certain postures of sleep (but not other postures) and / or certain changes in posture. In some instances, certain postures and / or changes in posture can be selected by the patient and / or clinician. For example, in the method, a specified posture in which sleep can be detected can include a lying posture (e.g., supine, left side, right side), while a specified posture in which sleep cannot be automatically detected can include the patient being in a sitting posture.

[0052] In one aspect, at least some example methods of automatic sleep detection (and / or arousal detection) can enhance SDB care in situations where some sleep cycles are unexpected or occur due to an irregular schedule. In contrast, initiating SDB care solely based on a preset time and / or based on manual control can miss opportunities to provide SDB care, and automatic sleep detection can increase the number and / or types of situations in which SDB care can be implemented. For example, in situations where a patient may want to sleep while sitting (where lying down is the only allowed posture for which automatic sleep detection is authorized), according to the described methods, the patient can activate a treatment cycle (e.g., via a remote control or by tapping the chest near the IPG) or can modify the sleep detection feature to implement automatic detection of sleep in a sitting position.

[0053] In some instances, determining the sleep-wake state may also include determining the patient's sleep stage, which in turn may enable modification of stimulation (eg, increase, decrease, etc.) within the treatment cycle.

[0054] In some instances, automatic determination of sleep-wake states can be automatically customized based on a particular patient's breathing patterns, activity patterns (e.g., how and when they move), sleep patterns (e.g., time of day, day of the week, etc.), etc.

[0055] In some instances, determining sleep-wake state can be implemented without posture or body position information. Thus, in some instances, sleep in a particular posture or a particular position is not used to determine sleep-wake state. For example, even though the patient may be in a position other than supine or lateral position, the example methods and / or devices of the present disclosure can still determine sleep-wake state. Thus, the example methods and / or devices can provide more robust and more accurate determination of sleep-wake state, and therefore provide more useful automatic start and / or termination of treatment cycles, regardless of sleeping posture. In some instances, automatic start, automatic pause features, etc. (e.g., resulting from automatically detecting sleep-wake state) can be selectively activated or deactivated by a clinician or patient, for example, via a clinician programmer or patient remote control.

[0056] At least some instances of determining sleep-wake state may also relate to cardiac care, medication delivery, and / or other forms of care, either alone or in conjunction with sleep disordered breathing (SDB) care.

[0057] As part of determining sleep-wake state, in some instances, sleep time is collected and provided to clinicians and patients for diagnostic, compliance monitoring, and patient engagement purposes. In some instances, sleep time may include sleep duration per day, week, etc., as well as the date and time of sleep onset and / or sleep termination for each weekday, weekend, average, etc. The collected sleep time may also include additional information about sleep pauses, such as their frequency, duration, etc., as well as other sleep time information.

[0058] In some instances, when determining sleep-wake states, detecting sleep can be different from detecting wakefulness, at least because at least some of the specific sensing modalities used to optimally detect each state (sleep versus wakefulness) can be different, and / or the specific values ​​of parameters sensed during sleep can be different from the specific values ​​of parameters sensed during wakefulness. For example, during sleep, example methods and / or apparatus of the present disclosure can successfully distinguish REM sleep from wakefulness.

[0059] In some instances, at least some of the above-described features and attributes substantially the same as those used to determine sleep-wake states can be used to detect non-sleep states and / or non-wake states. In some such instances, the term "non-sleep" can correspond to the probability that sleep remains below a sleep detection threshold, while in some such instances, the term "non-wakefulness" can correspond to the probability that wakefulness remains below a wakefulness detection threshold. In some instances, detection of non-sleep states and / or non-wakefulness states can enhance overall tracking, monitoring, etc. of a patient's sleep-wake states, which in turn can enhance care for sleep-disordered breathing.

[0060] Combined with at least Figures 1 to 36 These examples and additional examples are further described.

[0061] like Figure 1 As schematically represented at 500 in , in some examples, a method includes sensing physiological information (502) via at least one implantable sensor, and determining a sleep-wake state (504) via the sensed physiological information. As described above, determining the sleep-wake state includes sleep detection, by which a treatment cycle for SDB care (e.g., neurostimulation therapy) can be automatically initiated. Conversely, determining the sleep-wake state may include wake detection, by which a treatment cycle for SDB care can be automatically terminated. In some instances where a brief awakening (as opposed to a prolonged awakening) is detected and sleep is expected to resume thereafter, the treatment cycle is not terminated. Rather, the brief awakening may be considered a pause in the treatment cycle. Some example methods may include a time-based threshold (which may be only one of multiple factors) to determine whether the duration of the awakening includes a brief awakening or a prolonged awakening.

[0062] In some instances, detecting arousal can include detecting a trend from NREM sleep to arousal. In some such instances, upon detecting such a trend, some example methods can include gradually reducing the stimulation intensity, for example, by ramping, which can improve patient comfort at the end of the treatment period and / or during a brief awakening. Additionally, when resuming stimulation after a brief awakening, the stimulation intensity can be gradually increased (e.g., by ramping). As mentioned elsewhere, changes in stimulation intensity can include changes in the amplitude, frequency, pulse width, etc., of the electrical stimulation signal.

[0063] More specific example methods, devices, and / or arrangements for determining sleep-wake states in combination with at least Figures 2A to 36 Describe and show.

[0064] As in Figure 2A In some examples, sensing physiological information may include sensing movement at or around the chest, neck, and / or head, which in turn may be used to determine sleep-wake state. Figures 24 to 27B At least some aspects of such determinations are further described. For example, Figure 24 Sensing portion 2000 and / or Figure 27A The sensing portion 2510 (of the care engine 2500) includes a plurality of sensor types, modalities, etc., at least some of which can be used to sense motion at the chest, neck, and / or head or motion of the chest, neck, and / or head, and utilize such sensed motion to determine sleep-wake state (e.g., detect sleep). One such example modality can include employing an accelerometer to sense motion at the chest, neck, and / or head, as further described later. In some instances, the accelerometer can be implanted at the chest, neck, and / or head, while in some embodiments, the accelerometer can be fixed externally to the patient's body at such locations.

[0065] The sensed movement at the chest, neck and / or head may include movement of the chest, neck and / or head, or may include movement phenomena at these respective locations without necessarily involving total movement of the chest, neck and / or head, such as later combined with at least Figures 25A to 25F In one non-limiting example, sensing motion phenomena at the neck or other location may include sensing blood circulation within a blood vessel / vasculature (e.g., arterial motion within a blood vessel), such as in Figure 2B 525 in FIG. In some such examples, a sensing element (e.g., accelerometer, impedance, other) can be at least partially incorporated into a micro stimulator (or other implantable pulse generator) sized and shaped to be implanted within a blood vessel. Example methods can include sensing ballistic motion of a blood vessel caused by a patient's heartbeat. In some examples, the blood vessel can include the external jugular vein, and thus, in some examples, sensing of motion can occur at the neck, rather than necessarily motion of the neck (e.g., bending, tilting, twisting, etc.).

[0066] As in Figure 3A In some examples, the sensed posture information and / or body position information can be used to perform a method for determining sleep-wake state, as schematically represented at 528 in FIG. The sensed posture information can include a static posture, or can include changes in posture that can be considered as forms of whole-body movement as mentioned above. As mentioned elsewhere, the sensed posture can be used to help determine whether the patient is likely sleeping (e.g., lying down) or awake (e.g., sitting up), which can be combined with other sensed information (e.g., heart rate, respiratory rate, etc.).

[0067] As in Figure 3B As schematically represented at 530 in FIG. , in some examples, the method of determining sleep-wake state may be performed without using posture information and / or body position information.

[0068] For example, a patient may sometimes 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 cases, determining sleep-wake state without using posture information can enhance faster or more accurate sleep detection for patients sleeping in a sitting position because the example method can avoid false negative indications that the patient is awake (through posture-based determination).

[0069] Conversely, a patient may sometimes intentionally be awake while lying horizontally and therefore not wish to receive SDB care. In such cases, determining sleep-wake state without using posture information can enhance faster or more accurate sleep detection for patients who are awake in a lying down position because the example method (through posture-based determination) avoids false positive indications that the patient is asleep because the patient is in a horizontal position typically associated with sleep.

[0070] As in Figure 3C In some examples, the method of determining the sleep-wake state includes sensing at least one of a respiratory rate, a heart rate, and a body movement, and performing the sleep-wake state determination at least based on the corresponding at least one of the sensed respiratory rate, heart rate, and body movement. In some examples, the sensed body movement may correspond to Figure 2A and / or the sensed motion in 2B. In combination with at least Figures 24 to 27B Various aspects of determining sleep-wake states based on such sensed physiological information are further described elsewhere in the various examples of this disclosure.

[0071] Taking into account Figures 1 to 3C In some instances, detecting sleep (and / or wakefulness) in conjunction with delivering stimulation therapy may include Figure 4A The method shown at 540 in FIG. Figure 4A As shown at 542 in the method 540, the method 540 may include detecting sleep in the following circumstances: (1) date and time; and (2) detecting the lack of body movement indicative of sleep within a selectable predetermined time period. The date and time may be selectable and / or may be based on patient data. Once at least these two criteria are met, as in Figure 4AAs shown at 544 in , the method includes increasing the intensity of the stimulation therapy from a lower initial intensity level to a target intensity level, for example, in a ramp manner. As long as the sensed physiological information indicates that sleep is continuing, the stimulation at the target intensity level continues. However, when the patient's body movement is detected (which indicates wakefulness) or when the patient is detected to mechanically indicate wakefulness (e.g., physically tapping the chest near the IPG), the method can terminate any stimulation therapy and can remain in the non-stimulation mode for an optional predetermined time (e.g., 15 minutes). In other words, after the interruption, the method can delay the start of the treatment for a set time period (e.g., 15 minutes). The length of the delay period is programmable.

[0072] As in Figure 4B As shown in 550 in, in some instances, method 540 can also include sensing the onset of sleep by additional parameters, such as sensing posture, respiratory information (e.g., stability with respect to period, depth, etc.), cardiac information (e.g., stability of each RR interval, HR, etc.), and / or other information. For example, in one non-limiting example, portion 542 of method 540 can include detecting posture (550), and can include detecting certain specific postures of sleep (but not other postures) and / or specific changes in certain postures (but not other postures). In some instances, the specific postures and / or specific changes in posture can be selected by the patient and / or clinician. For example, a specified posture in which sleep can be detected can include a lying posture (e.g., supine, left side, right side), but the method does not allow for automatic detection of sleep while the patient is sitting.

[0073] In some cases, example methods can detect (e.g., identify) REM sleep and thereby avoid false positive detections of arousals. In particular, while breathing during REM sleep does not exhibit the same stability as non-REM sleep, such sensed less stable breathing can be confirmed as occurring during REM sleep (rather than arousals) based on the patient having slept for an extended period of time (e.g., through multiple sleep stages, S1-S4) and when the patient exhibits a lack of body movement (e.g., a lack of body movement of the type observed when the patient is awake).

[0074] Implementation of method 540 may also include enhancing sensitivity and / or specificity with respect to the sensed physiological phenomenon.

[0075] In some instances, Figure 4A The sleep detection in method 540 (e.g., at 542) may also include distinguishing the degree and / or type of body movement, posture, etc., as in Figure 4C, as shown at 552 in . This distinction can be performed in conjunction with ramping up stimulation (e.g., at 544), ramping down stimulation, terminating stimulation (e.g., at 546), and the like. For example, via aspect 552 of method 540, the method can distinguish between spontaneous body movement and jostling of the patient caused by vehicular motion (e.g., airplane, car, etc.) or by a bed partner. In some such instances, upon detection of such jostling, method 540 may include temporarily reducing stimulation therapy or pausing therapy, and then resuming the method at 544 to quickly 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 spontaneous body movement / cause, or may identify a significant change in posture (e.g., a change from lying down to sitting down) as spontaneous (e.g., not unintentional), and then as Figure 4A At 546 , treatment is terminated (or a longer pause is caused) because such detected behavior is indicative of arousal, whether transient or prolonged.

[0076] At least Figures 5 to 8 Provided are at least some example methods for determining sleep-wake states based on respiratory morphology characteristics. Figure 24 and Figures 27A to 27C At least some aspects of such sensing and related determinations (of sleep-wake states) related to respiratory morphology characteristics are further described.

[0077] In one aspect, the following Figures 5 to 8 Various features of the breathing morphology discussed in

[0015] (e.g., inspiratory onset, inspiratory cutoff, magnitude, etc.) can enhance the determination of sleep-wake state (e.g., at least sleep detection). In one aspect, these features of the breathing morphology are readily identifiable and, therefore, useful for tracking breathing rate, which can indicate sleep (vs. wakefulness) based on the value, trend, and / or variability of the breathing rate. In some instances, at least some of these features of the breathing morphology can exhibit stability, which can be characteristic of sleep (vs. wakefulness). Some examples of such stability that can be used to detect sleep / wake transitions can include a stable breathing rate, stability in the amplitude of the breathing signal, stability in the percentage of the breathing cycle corresponding to inspiration, and / or stability in the percentage of the breathing cycle corresponding to exhalation.

[0078] As in Figure 5 As schematically represented at 555 in , in some example methods, determining the sleep-wake state, for example by tracking at least some of the identified breathing rate information described above, may include sensing at least one of the start of inspiration, the start of exhalation, and the end of an expiratory pause, and performing the determination of the sleep-wake state at least based on at least one of the sensed start of inspiration, the sensed start of exhalation, and the end of the sensed expiratory pause.

[0079] As in Figure 6 As schematically represented at 560 in , in some example methods, determining the sleep-wake state by, for example, tracking at least some of the respiratory rate information identified above may include sensing at least one of an exhalation cutoff and an end of an exhalation pause, and performing the determination of the sleep-wake state by at least one of the sensed exhalation cutoff and the end of the exhalation pause.

[0080] It should be understood that other combinations may be used, such as Figures 5 to 8 different combinations of benchmarks (e.g., start of inspiration, end of expiratory pause, etc.), or using only Figures 5 to 8 One of these benchmarks.

[0081] As in Figure 7 In some example methods, as schematically represented at 570 in FIG, determining the sleep-wake state (e.g., by tracking at least some of the identified respiratory rate information) may include sensing an inhalation-to-exhalation transition, and performing the sleep-wake state determination based at least on the sensed inhalation-to-exhalation transition. Conversely, in some examples, sensing physiological information includes sensing an exhalation-to-inhalation transition, and performing the sleep-wake state determination based on the sensed exhalation-to-inhalation transition.

[0082] As in Figure 8 As schematically represented at 580 in , in some example methods, determining the sleep-wake state (e.g., by tracking at least some of the respiratory 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 based on at least one of the sensed inspiratory peak and the sensed expiratory peak.

[0083] In some instances, at least some of the sensing of respiratory characteristics, morphology, etc. can be detected by sensing bioimpedance, such as later combined with Figure 27A Of course, as mentioned elsewhere, in addition to or in addition to sensing bioimpedance, such sensing of respiratory characteristics, etc., can be implemented through sensing modalities. For example, in some instances, at least some of the sensing of respiratory characteristics, morphology, etc. can be detected by sensing electrocardiogram (ECG) information, as later combined with at least Figure 27A ECG parameters 2520 and / or Figure 24 2020 is further described.

[0084] In combination with at least Figures 5 to 8 and / or at least Figures 10 to 13In some such instances, methods and / or apparatus for determining sleep-wake state by sensing variability in respiratory behavior and / or cardiac behavior may include identifying some 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 and / or cardiac behavior (e.g., those indicative of sleep or wakefulness).

[0085] For example, as in Figure 9 As schematically represented in the block diagram of , in some instances, a method 580 for determining a sleep-wake state (or an apparatus for determining a sleep-wake state) may include sensing a physiological signal including at least a respiratory characteristic and / or a cardiac characteristic as shown at 582. At 583, the method 580 may include applying filtering and processing (F / P) to the sensed signal to produce: (1) filtered / processed signal information at 584, the filtered / processed signal information including variability in respiratory characteristics and / or cardiac characteristics that are characteristic of sleep disordered breathing (SDB); and (2) filtered / processed signal information at 585, the filtered / processed signal information including variability in respiratory characteristics and / or cardiac characteristics that are different from those characteristic of sleep disordered breathing (SDB). From the signal information at 585, at 590, the method may include determining a sleep-wake state. In some such instances, the sleep-wake state determination may include at least some of the processing in combination with at least Figures 5 to 8 and / or Figures 10 to 13 Or substantially the same features described in other examples described throughout this disclosure.

[0086] In some examples, output 586 of information 584 may be used to monitor, diagnose, treat, etc., sleep-disordered breathing (SDB). However, in some examples, such respiratory and / or cardiac information characteristic of sleep-disordered breathing (SDB) may be used to confirm a determination of a sleep-wake state via path 592, such as 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 (but not limited to) at least some of the periodic properties of SDB, such as recurring sequences of flow restriction, apnea (or hypopnea), and recovery. This identification may also include identifying similar periodic changes in heart rate that occur without detecting any overall change in posture.

[0087] Alternatively, the method may include at least partially confirming that the patient is awake (as determined primarily by other information) by confirming the absence of sleep-disordered breathing (e.g., due to the cyclical nature of changes in breathing patterns and heart rate without overall posture changes).

[0088] In some examples, sleep-wake state determination can be performed by sensing cardiac morphological characteristics. Figures 10 to 13 At least some example methods are provided by which sleep-wake state determination can be performed based on such example cardiac morphological features. Figure 24 and Figures 27A to 27C At least some aspects of such sensing and related determinations (of sleep-wake states) related to cardiac morphological characteristics are further described.

[0089] In one aspect, the following Figures 10 to 13 The various features of cardiac morphology discussed in

[15] (e.g., atrial contraction, ventricular contraction, etc.) can enhance the determination of sleep-wake states (e.g., at least sleep detection), at least because these features of cardiac morphology are easily identifiable and therefore advantageous for use in tracking heart rate, which can indicate sleep (vs. wakefulness) based on heart rate values, heart rate trends, and / or heart rate variability (HRV). In some instances, at least some of these features of cardiac morphology can exhibit increased stability, which can be characteristic of sleep (vs. wakefulness). In some instances, as described above, at least some sleep stages can exhibit greater or less variability in heart rate variability (HRV) and / or greater or less variability in respiratory characteristics. For example, greater variability in cardiac characteristics (e.g., heart rate, etc.) and respiratory characteristics (e.g., respiratory rate, etc.) can be expected during REM sleep. At least some instances of determining sleep-wake states can identify such variability in cardiac and respiratory signals as characteristic of REM sleep stages in a manner that distinguishes it from variability (or, in some instances, lack of variability) in cardiac and respiratory signals that is characteristic of wakefulness. For example, example methods may identify that a patient is in REM sleep when a moderate increase in variability in sensed respiratory and / or cardiac characteristics is followed by other sleep stages (eg, S3, S4) accompanied by a sensed lack of body movement.

[0090] In some instances, at least some of the sensing of cardiac characteristics, morphology, etc. can be detected by sensing bioimpedance, such as later combined with at least Figure 24 The impedance parameters in 2036 and Figure 27A In some examples, at least some of the sensing of cardiac characteristics, morphology, etc. can be detected by sensing electrocardiogram (ECG) information, such as later combined with at least Figure 24 and Figure 27A The ECG parameters 2020, 2520 described further in the text. Bioimpedance and / or ECG used to sense cardiac characteristics, morphology, etc. can also be used to sense respiratory characteristics, morphology, etc. (as previously described), or can be used to sense cardiac and respiratory characteristics, morphology, etc. It will also be understood that Figure 24 、 Figure 27A Additional example sensing types, modalities, etc. are provided by which cardiac information (including but not limited to heart rate and / or heart rate variability) can be sensed, and which can then be used to determine sleep-wake state.

[0091] As in Figure 10 As schematically represented at 600 in , in some example methods, determining the sleep-wake state may include sensing at least one of atrial contraction or ventricular contraction, and performing the determination of the sleep-wake state by at least one of the sensed atrial contraction and the sensed ventricular contraction.

[0092] As in Figure 11 As schematically represented at 605 in the example, in some instances, determining the sleep-wake state may include sensing at least one of a peak of atrial contraction or a peak of ventricular contraction, and performing the sleep-wake state determination based on at least one of the sensed peak of atrial contraction and the sensed peak of ventricular contraction. In some example methods, determining the sleep-wake state may include sensing the onset of at least one of atrial contraction and ventricular contraction, and performing the sleep-wake state determination based on the sensed onset of at least one of atrial contraction and ventricular contraction. In some example methods, determining the sleep-wake state may include sensing the onset of at least one of atrial relaxation and ventricular relaxation, and performing the sleep-wake state determination based on the sensed onset of at least one of atrial relaxation and ventricular relaxation.

[0093] As in Figure 12 In some example methods, as schematically represented at 630 in FIG, determining sleep-wake state may include sensing both atrial contraction and ventricular contraction, and performing sleep-wake state determination based on both the sensed atrial contraction and ventricular contraction. By sensing these two features of heart morphology, more robust heart rate (and heart rate variability) tracking may be performed, which in turn may provide more robust sleep-wake state determination.

[0094] As in Figure 13As schematically represented at 640 in , in some example methods, determining the sleep-wake state may include sensing heart valve closure, and performing the sleep-wake state determination by the sensed heart valve closure. It should be understood that in some examples, the sensed heart valve closure may involve closure of the semilunar valve and / or the atrioventricular (AV) valve. Moreover, in some such examples, by using timing information, the sensed heart valve closure can be used as a surrogate for other cardiac cycle morphologies (e.g., ventricular contraction, etc.). For example, because ventricular contraction begins just before the AV valve closes and before the semilunar valve opens, heart sounds (e.g., S1, S2) can be used to track ventricular contraction and / or other cardiac morphological features.

[0095] Combine Figure 27B and Figure 27A The cardiac portion 2600 of the care engine 2500 in

[00106] further describes at least some of these relationships to cardiac morphology.

[0096] As in Figure 14 As schematically represented at 700 in some example methods, determining the sleep-wake state may include sensing respiratory information and sensing cardiac motion information, and performing the determination of the sleep-wake state through both the sensed respiratory information and the sensed cardiac motion information.

[0097] As in Figure 15A As schematically represented at 705 in FIG, in some example methods, determining a sleep-wake state (eg, onset of sleep, etc.) may include comparing subsequent second motion information with the first motion information. Figure 15B As further shown at 710 in FIG. 7 , in some examples, method 705 may include determining a sleep-wake state (e.g., onset of sleep, etc.) after determining, based on the comparison, that a second value of the subsequent second motion information and a first value of the first motion information are less than a predetermined difference. The predetermined difference value may be selectable.

[0098] In some examples of methods 705 , 710 , each of the respective first motion information and second motion information includes at least one of sensed respiratory information, sensed cardiac information, and sensed body motion.

[0099] In some examples of methods 705 and 710, the subsequent second information includes information obtained in the most recently sensed respiratory cycle, and the first information includes information obtained in the previous respiratory cycle. In some examples, the subsequent second information includes information about respiratory activity in at least the most recent 30 seconds. In some examples, the information may relate to respiratory activity in at least the most recent 60 seconds. In some examples, the information may relate to respiratory activity in at least the most recent 7 breaths.

[0100] In some instances, the previous breathing cycle includes the breathing cycle immediately preceding the most recently sensed breathing cycle. In some instances, the previous breathing cycle includes the respiratory activity in the 30 seconds (or 60 seconds or 7 breaths) preceding the most recently sensed breathing cycle. In some instances, the first information includes respiratory information within at least one breathing cycle or at least 30 seconds or at least 60 seconds.

[0101] In some instances of methods 705 and 710, recent motion information is compared to target values ​​indicative of sleep. In some instances, a lower and / or more stable respiratory rate and heart rate are more likely to be associated with sleep. Figure 9 As noted, determining sleep-wake state may include isolating (e.g., filtering, discarding) breathing features that are characteristic of sleep-disordered breathing (SDB) and / or breathing features that are characteristic of a particular sleep stage that are not necessarily helpful for general sleep detection (e.g., detecting the onset of sleep).

[0102] However, as previously mentioned Figure 9 As noted in at least aspects 584, 592 of the method, detection of sleep-disordered breathing (SDB) may also be used to sense or confirm the presence of sleep, or in some cases the onset of sleep.

[0103] In some examples, the method ( FIG. 15A to FIG. 15B 705, 710 in the embodiment of the present invention may include determining subsequent second motion information based on a second average value of the motion information in the respiratory cycles of the sensed second respiratory cycle, and determining the first motion information based on the first average value of the motion information in the respiratory cycles of the first respiratory cycle. In some such examples, the second average value of the motion information corresponds to an average value of a parameter, such as, but not limited to: an average amplitude of the sensed second respiratory cycle; an average respiratory rate of the sensed second respiratory cycle; and / or an average ratio of an inspiratory period to an expiratory period of the sensed second respiratory cycle.

[0104] In some instances, previously combined FIG. 15A to FIG. 15B At least some of the described example methods may be accomplished by combining Figures 15C to 15F The invention may be implemented with or in conjunction with the examples shown and described.

[0105] In one example, Figure 15C As shown in FIG714 , a diagram 714 includes a respiratory signal 715 having a series of respiratory cycles 716, each of which has an inspiratory phase 717A, an active expiratory phase 717B, and an expiratory pause 717C. Each respiratory cycle 716 can define a period R1, such as the duration of the respiratory cycle 716. However, the period R1 can be defined by other aspects of one or more cardiac cycles. As also shown in FIG714 , in conjunction with FIG. 15A to FIG. 15BIn some example embodiments of the described example methods, a second capture window 718B (e.g., a second respiratory cycle) can be used to obtain the sensed subsequent motion information, and a first capture window 718A can be used to obtain the sensed first motion information. The capture windows 718A, 718B can correspond to samples of a single biological cycle (e.g., a respiratory cycle), or can correspond to samples of multiple biological cycles (e.g., a series of several respiratory cycles). In some instances, the first capture window and the second capture window can have the same size, i.e., duration. In some instances, the capture window can sample biological cycles other than the entire respiratory cycle, for example, sampling peak inspiratory to peak inspiratory, which can be represented as a 30 second period, 3 breath groups, 10 heartbeat groups, start of exhalation to start of exhalation, etc.

[0106] By comparing the respiratory motion information in the second capture window 718B with the respiratory motion information in the first capture window 718A, the above (in combination with the respiratory motion information) can be implemented when comparing the second (i.e., subsequent) motion information with the first (i.e., earlier) motion information. FIG. 15A to FIG. 15B ) described in an example method to at least partially determine sleep-wake state.

[0107] Similarly, as in Figure 15D As shown in FIG. 720 , in some examples, a cardiac waveform signal 721 may be a signal of interest that includes a series of cardiac cycles 722. Each cardiac cycle 722 may define a period R2, such as the duration of the cardiac cycle 722. However, the period R2 may be defined by other aspects of one or more cardiac cycles. Figure 15C In a manner, the first capture window 723A can be used to obtain first (i.e., earlier) heart-related motion information, and the second capture window 723B can be used to obtain second (i.e., subsequent) heart-related motion information, which can be compared with the first motion information (as described above) to at least partially determine the sleep-wake state (e.g., the onset of sleep, etc.). The capture windows 723A, 723B can correspond to samples of a single biological cycle (e.g., a cardiac cycle), or can correspond to samples of multiple biological cycles (e.g., a series of several cardiac cycles). In some examples, the first capture window and the second capture window have the same size, i.e., duration.

[0108] refer to Figure 15C and / or Figure 15D It will also be understood that more than two capture windows may be applied as part of obtaining and comparing motion information from the sensed signals of interest as part of determining sleep-wake state.

[0109] In some examples, by using corresponding first and second capture windows (e.g., Figure 15C 、 Figure 15D The information obtained in (i) can be analyzed or plotted in various forms to graphically highlight the comparison. For example, in only one instance and as Figure 15E As shown in FIG. 730 , cardiac motion information capture windows 723A, 723B ( FIG. 723B ) can be analyzed in a histogram format by placing the values ​​of a particular parameter (e.g., heart rate) in groups 733A, 733B, 733C to evaluate the values ​​of a cardiac parameter (e.g., heart rate) within a first time period (e.g., a first capture window), and optionally evaluating the values ​​of the same cardiac parameter (e.g., heart rate) within a second time period (e.g., a second capture window). Figure 15D ). In this way, it can be determined whether a change in the biological value of the parameter is at least partially indicative of a change in sleep-wake state (e.g., the onset of sleep) because changes in breathing rate and heart rhythm are associated with transitions between sleep and wakefulness. This indication can be supplemented by other indicators of sleep / wake state, such as body movement, position, date and time, time since the onset of possible sleep, and other parameters that affect the confidence level of the determination of the sleep / wake state. It will be understood that the formation and / or use of the results of such example histograms can be performed by the control portion 4000 ( Figure 28A ) implementation.

[0110] Similarly, in Figure 15F In another example shown in FIG. 735 , respiratory motion information from capture windows 718A, 718B can be evaluated in the form of a histogram, by which the values ​​of a particular parameter (e.g., respiratory rate) can be placed in groups 738A, 738B, thereby graphically illustrating the respiratory motion information within a first time period (e.g., first capture window 718A) juxtaposed with the respiratory motion information within a second time period (e.g., second capture window 718B).

[0111] Some example methods of at least partially determining sleep-wake state by comparing subsequent motion information with first motion information may use only respiratory motion information alone ( Figure 15C and Figure 15E ), you can use only the heart information ( Figure 15D and Figure 15F ), or both respiratory information and cardiac information as described throughout the various examples of this disclosure may be used. Additionally, cardiac and / or respiratory motion may be enhanced with other physiological information.

[0112] It should be understood that for simplicity of illustration, Figure 15C and Figure 15DThe waveforms in do not show significantly different respiratory rates and heart rates between different capture windows, but it should be understood that one capture window (e.g., 718A) may correspond to a respiratory rate or other waveform feature that is significantly different from the respiratory rate (or other waveform feature) in another capture window (e.g., 718B).

[0113] It should also be understood that in some instances, Figures 15A to 15E The associated example embodiments may be used with any biological signal of interest that may be useful in determining sleep-wake state in various examples throughout the present disclosure.

[0114] In some instances, the above Figures 15A to 15E At least some aspects of the description may be implemented via historical parameters 2542 and / or comparative parameters in the sensing portion 2510 of the care engine 2500, as later described in conjunction with at least Figure 27A Descriptive.

[0115] As in Figure 16A As schematically represented at 720 in FIG, in some example methods, determining a sleep-wake state may include identifying a wake state (or lack thereof) by identifying variability in sensed physiological information, the variability in the sensed physiological information including variability in at least one of: respiratory rate; heart rate; inhalation and / or exhalation portions of a respiratory cycle; duration of the inhalation portion; amplitude of a peak in the inhalation portion; duration of a peak in the inhalation portion; duration of the exhalation portion; physical activity; and amplitude of a peak in the exhalation portion. In some examples, for at least some parameters, variability may be assessed relative to a threshold value, which in some examples may be fixed.

[0116] As in Figure 16B As schematically represented at 730 in FIG, in some example methods, determining a sleep-wake state may include identifying a sleep state (or lack thereof) by identifying variability in sensed physiological information, the variability in the sensed physiological information including variability in at least one of: respiratory rate; heart rate; inhalation and / or exhalation portions of a respiratory cycle; duration of the inhalation portion; amplitude of a peak in the inhalation portion; duration of a peak in the inhalation portion; duration of the exhalation portion; physical activity; and amplitude of a peak in the exhalation portion. In some examples, for at least some parameters, variability may be assessed relative to a threshold value, which in some examples may be fixed.

[0117] As in Figure 17As schematically represented at 750 in , in some instances, performing determination of sleep-wake state includes tracking at least one second parameter in addition to movement at the chest, neck, and / or head (or movement of the chest, neck, and / or head), wherein the second parameter includes at least one of: date and time; daily activity pattern; and (typical) breathing pattern.

[0118] As in Figure 18 As schematically represented at 760 in FIG. , in some examples, performing the sleep-wake state determination includes tracking at least one second parameter in addition to movement at the chest, neck, and / or head (or movement of the chest, neck, and / or head), wherein the second parameter includes a physiological parameter. For example, one such physiological parameter may include temperature (e.g., Figure 24 2038, Figure 27A 2538 in ).

[0119] As in Figure 19 As schematically represented at 770 in FIG. , in some examples, determining the sleep-wake state includes assessing at least one of a sleep probability and a wake probability based on the sensed physiological information.

[0120] As in Figure 20A As schematically represented at 780 in FIG, some example methods (and / or apparatus) include taking an action when the sleep probability or wake probability exceeds a threshold. In some such instances, some example methods (and / or apparatus) include taking an action when the sleep probability or wake probability exceeds the threshold by a selectable predetermined percentage and for a selectable predetermined duration.

[0121] In method 780 ( Figure 20A ), taking action may include at least one of initiating a stimulation therapy cycle and terminating a stimulation therapy cycle, such as in Figure 20B In some such examples, (as in Figure 20A In 780, taking action (when the probability of sleep exceeds a threshold) may include initiating a therapeutic treatment cycle (e.g., applying stimulation), resuming stimulation within a therapeutic cycle after pausing or suspending stimulation, and / or other actions. In some examples, taking action (when the probability of wakefulness exceeds a threshold) may include terminating a therapeutic treatment cycle, suspending stimulation within a therapeutic cycle, and / or other actions.

[0122] In some such examples, initiating and / or resuming stimulation therapy may include employing a stimulation ramp, wherein the initial stimulation intensity is low and then increased to the target intensity level. In some examples, terminating therapy may include employing a stimulation ramp, wherein the stimulation intensity is gradually decreased from the target treatment intensity level until stimulation is no longer applied (i.e., the stimulation intensity is equal to zero).

[0123] Further references at least Figure 20A In some instances, taking action in method 780 may include using an observer for an additional period of time to ensure the patient is asleep, and / or using a start timer to start counting an optional predetermined period of time (e.g., a delay) until stimulation is initiated as part of a treatment cycle.

[0124] In method 780 ( Figure 20A ) in some such instances, the method further comprises applying the boundary as in Figure 20C The corresponding start and end shown at 782 in FIG. Figure 27A Boundary parameters 3016 for the activation portion 3000 in further describe at least some aspects of such boundaries.

[0125] In some example methods associated with method 780, applying boundaries includes setting a start boundary before which initiation is not performed, and / or setting a stop boundary at which termination is performed, such as in Figure 20D As shown in 783.

[0126] In some examples, methods for determining sleep-wake states based on boundaries (e.g., 782, 783) may include implementing corresponding start boundaries and stop boundaries based on date and time, such as in Figure 20E In some examples, such as in Figure 20F As shown at 785 in , the method may include implementing the date time based on at least one of: time zone; ambient light sensed by external means; daylight saving time; geographic latitude; and seasonal calendar.

[0127] In some instances, such as Figure 20G As shown at 786 in , the method (eg, 783 ) may include implementing a stopping boundary based on at least one of the number, type, and duration of sleep stages.

[0128] In some instances, such as Figure 20H As shown at 787 in the method (e.g., 783), the method (e.g., 783) may include implementing at least one of a start boundary parameter and a stop boundary parameter based on sensing a temperature via an implantable sensor. In some instances, the method may include implementing at least one of starting a stimulation therapy cycle and terminating a stimulation therapy cycle based on sensing a body temperature via an implantable sensor. In some instances, the method may include disposing an implantable sensor within an implantable pulse generator, and the implantable sensor includes a temperature sensor. In some such instances, the method 787 may include implementing at least one of a start boundary parameter and a stop boundary parameter based on sensing a body temperature via an implantable sensor. Figure 24 At least the temperature sensor 2038, Figure 27A Temperature parameter 2538 and / or Figure 27A At least boundary parameters 3016 are implemented in, and / or further described later in conjunction with them.

[0129] In some instances, such as Figure 20I As shown at 788 in the method (including determining the sleep-wake state, such as Figure 19 770 of them and / or Figure 20A 780 in the remote control) may include receiving input from at least one of the remote control and the application on the mobile consumer device regarding at least one of: the level of ambient lighting; the level or type of motion of the remote control or the mobile consumer device; and the frequency, type, or level of use of the remote control or the mobile consumer device.

[0130] As in Figure 21 As schematically represented at 800 in FIG. , some examples of determining a sleep-wake state may include: separating a signal associated with sensed physiological information into a plurality of different signals, wherein each respective signal represents a different sleep-wake determination parameter; and determining a probability of the sleep-wake state based on evaluating the respective different signals associated with the respective different sleep-wake determination parameters. In some examples, this example method may include voting, whereby each signal provides input to an overall sleep probability. In some such examples, the various individual signals may be weighted differently so that each respective sleep-wake determination parameter is applied relatively more or less than other respective sleep-wake determination parameters.

[0131] In some examples, method 800 ( Figure 21 ) can be achieved by combining at least Figures 33 to 36 at least some of the features and attributes of the described arrangements.

[0132] As in Figure 22 As schematically represented at 810 in , in some instances, determining the sleep-wake state includes at least one of: evaluating at least one of a sleep probability and a wake probability based on sensing physiological information by sensing movement at the chest, neck, and / or head (or movement of the chest, neck, and / or head).

[0133] As in Figure 23AIn some instances, as schematically represented at 820 in , sensing physiological information includes obtaining and identifying arousal information (e.g., during normal wake cycles), and includes performing a determination of sleep-wake state at least in part through the arousal information. In some such instances, the arousal information is used to better characterize sleep and, therefore, more easily determine the sleep-wake state (e.g., detect sleep or the lack thereof). However, in this context, the identified arousal information is not used to adjust therapy (e.g., stimulation parameters, etc.) and / or is not used to characterize breathing disorders. In some instances, identification of arousals can be performed by sensing at least one of whole-body motion and movement. In some instances, sensing physiological information includes obtaining sleep information, and includes performing a determination of sleep-wake state through the sleep information.

[0134] As in Figure 23B In some examples, the method includes sensing snoring and using the snoring information as part of determining sleep-wake state. In some such examples, the method may include quantifying the sensed snoring and reporting the snoring information to at least one of the patient, a physician, or a caregiver. In some examples, snoring can be distinguished from normal speech. In some examples, the method may be combined with acoustic sensor 2039 ( Figure 24 ) and / or acoustic parameters 2539( Figure 27A ) Sense snoring, track snoring, etc.

[0135] In some instances, the combination of at least Figures 1 to 23B The various features and attributes of the described example methods (and / or care devices) for determining sleep-wake states may be combined and implemented in a complementary or additive manner.

[0136] Will combine at least Figures 24 to 36 Further description and Figures 1 to 23B These and additional features and attributes associated with Figures 24 to 36 At least some of the described examples may include combining Figures 1 to 23B Example implementations of the described examples.

[0137] Figure 24 2000 is a block diagram schematically illustrating an example sensing portion. In some examples, an example method may employ and / or an example SDB care device may include a sensing portion 2000 to sense physiological information and / or other information, wherein such sensed information relates to sleep-wake detection and other uses. The sensed information may be used to implement a method in conjunction with at least Figure 1 To Figure 23 and / or Figures 25A to 36 At least some of the example methods and / or example apparatus described.

[0138] It will be understood that the 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 sensors. Figure 24 The various types of sensing schematically represented in FIG. 5 may correspond to sensors and / or sensing modalities.

[0139] For example, in one non-limiting example, Figure 24 The electrocardiogram (ECG) sensor 2020 in the embodiment may include a sensing element (e.g., an electrode) or a plurality of sensing elements arranged relative to the patient's body (e.g., implanted in the transthoracic region) to obtain ECG information. In some examples, the ECG information may include an example embodiment of 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) to determine sleep-wake states as described in examples of the present disclosure.

[0140] However, in some cases, ECG sensor 2020 may generally represent an ECG sensing element, regardless of the particular manner in which sensing ECG information may be implemented.

[0141] In some instances where multiple electrodes are used to obtain ECG signals, the ECG electrodes may be mounted on or form at least a portion of a housing (e.g., an outer shell) of an implantable pulse generator (IPG), such as to be later incorporated into at least one Figure 25A In such cases, the other ECG electrodes are spaced apart from the ECG electrodes associated with the IPG. Figure 25A In some examples further described, at least some of the ECG sensing electrodes may also be used to deliver stimulation to nerves or muscles, such as, but not limited to, nerves associated with upper airway patency (eg, the hypoglossal nerve) or other nerves or muscles.

[0142] In some examples, multiple ECG sensing electrodes may be mounted on the housing of the IPG or form different parts of the housing, such as to be later incorporated into at least one Figure 25B 、 Figure 25C 、 Figure 25D In such examples, the corresponding ECG electrodes are arranged electrically independently of each other on the housing of the IPG so that a suitable ECG signal can be obtained.

[0143] In some instances, ECG sensing electrodes may be used for sensing only (eg, single purpose), but positioned along the lead body of the stimulation lead, such as later incorporated into the stimulation lead. Figure 25AIt will be appreciated that such dedicated ECG sensing electrodes are positioned along the stimulation lead in a manner to avoid contact with the housing of the IPG, particularly in instances where the exposed conductive portions of the housing of the IPG can be used as electrodes and thus a sensing vector can be obtained by a combination of the sensor electrodes along the lead and the conductive portions of the IPG. Similarly, the same / similar electrode arrangement can be used for sensing bioimpedance, as will be discussed later in conjunction with Figures 25A to 25F 、 Figure 27A More fully described.

[0144] In some instances, 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 by Figure 24 2026 to obtain cardiac information. In some examples, such sensing is based on and / or implemented by accelerometer-based sensing, as further described below in conjunction with accelerometer 2026.

[0145] In one aspect, in some instances, the ballistocardiogram sensor 2023A senses cardiac information resulting from cardiac output (e.g., the forceful ejection of blood from the heart into the aorta with each heartbeat). The sensed ballistocardiogram information may include heart rate (HR), heart rate variability (HRV), and / or other cardiac morphology. As described above in at least Figure 4A As noted in the context of

[0015] , in some instances, such ballistocardiogram-type information can be sensed from within a blood vessel, where a sensor (e.g., an accelerometer) senses the movement of the vessel wall caused by the pulsation of blood moving through the vessel with each heartbeat. This phenomenon is sometimes referred to as arterial motion.

[0146] In one aspect, the seismocardiogram sensor 2023B can provide cardiac information similar to that described for the ballistocardiogram sensor 2023A, except that it is obtained by sensing vibrations in or along the chest wall caused by cardiac output based on an accelerometer (e.g., single-axis or multi-axis). In particular, the seismocardiogram measures compression waves generated by the heart (e.g., based on heart wall motion and / or blood flow) and transmitted to the chest wall during cardiac motion. Thus, the sensor 2023B can be placed in the chest wall.

[0147] In some such instances where sensing is performed according to sensors 2023A, 2023B, such methods and / or apparatus may also include sensing respiratory rate and / or other respiratory information.

[0148] like Figure 24As also shown in FIG, in some instances, the sensing portion 2000 may include an electroencephalogram (EEG) sensor 2012 for obtaining and tracking EEG information. In some instances, in addition to sensing EEG information, the EEG sensor 2012 may also sense and / or track central nervous system (CNS) information. In some instances, the EEG sensor 2012 may be implanted subcutaneously beneath the scalp, or may be implanted in a head and neck area otherwise suitable for sensing EEG information. Thus, the EEG sensor 210 is located near the brain and can detect frequencies associated with brain electrical activity.

[0149] In some instances, the sensing element for sensing EEG information is implantable for a long term, for example, in a subcutaneous location (e.g., a subcutaneous location outside the skull) rather than in an intracranial location (e.g., inside the skull). In some instances, 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 may exacerbate sleep apnea, particularly with respect to obstructive sleep apnea. Similarly, the EEG sensing element can be used in a device in which a stimulating element delivers stimulation to the hypoglossal nerve or other upper airway patency nerves without stimulating the vagus nerve to avoid exacerbating obstructive sleep apnea.

[0150] In some examples, the sensing portion 2000 may include an electromyography (EMG) sensor 2022 for obtaining and tracking 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 in turn may indicate arousal. In some examples, the sensed EMG signals may be used to identify sleep and / or obstructive events. In combination with at least Figure 25A At least some additional aspects regarding EMG sensing are described.

[0151] In some instances, such as Figure 24 As shown in , 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 cases, such sensed EOG information may be used to distinguish REM sleep from non-REM sleep or wakefulness. In some instances, the sensing element for obtaining EOG information may be implanted in the head and neck portion, such as near the eyes, eye muscles and / or eye nerves. In some instances, the sensing element may transmit the EOG information wirelessly or via implanted leads to a control element (e.g., a monitor, a pulse generator, etc.) implanted in the head and neck region. In some such instances, the sensing element may include electrodes implanted near one or both eyes of the patient.

[0152] However, in some instances, EOG information may be obtained by external sensing elements worn on the head or that can observe eye movements, positions, etc., for example, via a mobile phone, a monitoring station close to the patient, etc. Such externally obtained EOG information may be wirelessly transmitted to an implanted monitor, pulse generator, etc. that controls the sensing elements and / or stimulation elements implanted in the patient. Figure 27A Some aspects of sensing by the EOG sensor are further described.

[0153] In some examples, the combination can be implemented by a single sensing element 2014 Figure 24 Any one or combination of the various sensing modalities described (e.g., EEG, EMG, etc.).

[0154] In some examples, the sensing portion 2000 can include an accelerometer 2026. In some examples, the accelerometer 2026 and associated sensing (e.g., motion at the chest, neck, and / or head (or motion of the chest, neck, and / or head), respiration, heart, posture, etc.) can be implemented according to at least some substantially the same features and properties as described in Dieken et al., “ACCELEROMETER-BASED SENSING FOR SLEEP DISORDERED BREATHING (SDB) CARE,” published as US2019-0160282 on May 30, 2019, which is incorporated herein by reference in its entirety. In some examples, the accelerometer can include a single-axis accelerometer, while in some embodiments, the accelerometer can include a multi-axis accelerometer.

[0155] In addition to other types and / or manners of sensing information, the accelerometer sensor 2026 can be used to sense or obtain a ballistocardiogram (2023A), a seismocardiogram (2023B), and / or a tachycardiogram (2023C), which can be used to sense (at least) heart rate and / or heart rate variability (in some cases, in addition to other information such as respiratory rate), which in turn can be used as part of determining sleep-wake state as described in examples of the present disclosure.

[0156] In some examples, accelerometer 2026 may be used to sense activity, posture, and / or body position as part of determining sleep-wake state, which may sometimes be at least partially indicative of sleep-wake state.

[0157] In some examples, sensing portion 2000 can include an impedance sensor 2036 that can sense the patient's transthoracic impedance or other bioimpedance. In some examples, impedance sensor 2036 can include a plurality of sensing elements (e.g., electrodes) spaced apart from one another across a portion of the patient's body, e.g., Figure 25A Electrodes 2120, 2135, 2130 and / or Figures 25B to 25F In some such examples, one of the sensing elements (e.g., Figure 25A The electrodes 2135 in FIG. 2 may be mounted on or form part of an outer surface (e.g., housing) of an implantable pulse generator (IPG) or other implantable sensing monitor, while other sensing elements (e.g., Figure 25A The electrodes 2120, 2130 in the embodiment of the present invention may be located at a spaced distance from the sensing element of the IPG or sensing monitor. In at least some such examples, the impedance sensing arrangement integrates all body motion / variations (e.g., respiratory effort, cardiac motion, etc.) between the sensing electrodes (including the case of an IPG when present). Some example implementations of the impedance measurement circuit will include separate drive and measurement electrodes to control the electrode-to-tissue access impedance at the drive node.

[0158] In some instances, sensing portion 2000 may include a pressure sensor 2037 that senses respiratory information, such as, but not limited to, respiratory cycle information. In some such instances, the respiratory pressure sensor may include at least some substantially identical features and properties as described in U.S. Patent Publication US2011 / 0152706, "METHOD AND APPARATUS FOR SENSING RESPIRATORY PRESSURE IN AN IMPLANTABLE STIMULATION SYSTEM," published by Ni et al., on June 23, 2011, which is incorporated herein by reference in its entirety. In some instances, pressure sensor 2037 may be positioned directly or indirectly continuous with a respiratory organ or airway or tissue supporting a respiratory organ or airway to sense respiratory information.

[0159] In some examples, one sensing modality within sensing portion 2000 can be implemented at least in part by another sensing modality within sensing portion 2000 .

[0160] In some examples, the sensing portion 2000 may include an acoustic sensor 2039 to sense acoustic information, such as, but not limited to, cardiac information (including heart sounds), respiratory information, snoring, etc.

[0161] In some examples, the sensing portion 2000 may include body motion parameters 2026 by which patient body motion may be detected, tracked, etc. Body motion may be detected, tracked, etc., by a single type of sensor or by multiple types of sensing. For example, in some examples, body motion may be sensed by an accelerometer 2026, and in some examples, body motion may be sensed by EMG 2022 and / or other sensing modalities, as described in various examples of the present disclosure.

[0162] In some instances, Figure 24 The sensing portion 2000 in the device may include posture parameters 2040 to sense and / or track sensed information about posture, which may also include sensing of the patient's body position, activity, etc. In some instances, such sensed information may indicate the patient's wakefulness or sleep state. In some such instances, such information may be sensed by the accelerometer 2026 and / or other sensing modalities described above. In some instances, such posture information (and / or body position, activity) may sometimes be used alone and / or in combination with other sensing information to determine sleep-wakefulness state. As described elsewhere herein, in some instances, posture may be considered as one of several parameters when determining sleep (or wakefulness) probability.

[0163] For example, sensing an upright posture is often associated with a wakeful state (e.g., standing or walking). However, as noted elsewhere, a person can be in an upright sitting position and still be in a sleep state (e.g., falling asleep in a chair). Thus, posture may be only one parameter used to determine sleep-wakefulness and may be combined with other parameters. Figure 24 The sensing portion 2000 and / or Figure 27A In contrast, sensing a supine or lateral (i.e., side-lying) posture is typically associated with a sleeping state. However, a patient may be in such a position without being asleep that other parameters in addition to or in lieu of posture (e.g., Figure 24 、 Figure 27A ) can significantly enhance the determination of sleep-wake states.

[0164] Furthermore, sensing posture may be extended beyond static posture to include sensing simple changes in posture (or body position), which may indicate a sleep-wake state, at least because certain changes in posture (e.g., from supine to upright) are likely to indicate a wake state. Similarly, more complex or frequent changes in posture and / or body position may further indicate a wake state, while maintaining a single stable posture for an extended period of time may indicate a sleep state.

[0165] In some examples, the sensing portion 2000 ( Figure 27A) includes other parameters 2041 to guide sensing information, and / or receive, track, evaluate, etc. sensed information in addition to the information sensed by the sensing portion 2510 previously described.

[0166] In some example methods and / or devices, the sleep-wake state can be determined without using posture information or body position information through the sensing portion 2000. In some such instances, determining the sleep-wake state without taking into account posture information (or body position) can allow the device to provide effective sleep-disordered breathing (SDB) care even in situations where the patient may be asleep in an upright position (e.g., sitting in a chair, in a zero-gravity environment, etc.), which is contrary to the conventional assumption that sleep occurs in a horizontal body position. Such embodiments can allow SDB care to be performed while the patient is sleeping during travel (e.g., sitting in an airplane seat, car seat, train seat, etc.). In some such instances, the SDB care method and / or SDB care device can sometimes be referred to as being posture-insensitive.

[0167] like Figure 24 As also shown in FIG. 2 , in some examples, the sensing portion 2000 may include a temperature sensor 2038. In some examples, the sensing portion 2000 may include a temperature sensor 2038. Figure 27A The temperature parameters 2538 in the sensing portion 2510 of the care engine 2500 in tracks such sensed temperatures, evaluates such sensed temperatures, and the like.

[0168] In some instances, the sensed temperature can be used as a factor in determining sleep-wake states according to examples of the present disclosure. In one aspect, the temperature sensor 2038 can sense and track normal fluctuations in a patient's body temperature over a 24-hour daily cycle (e.g., a temperature profile), which can be expressed in the order of 2 degrees Fahrenheit (F) changes. For most patients, their body temperature may reach and remain at the high end of its range (e.g., 99.5F) during midday and evening (e.g., 7 p.m.), dropping to the low end of its range (e.g., 97.5F) through late evening and overnight into the early morning hours (e.g., 5 or 6 a.m.). In some instances, the temperature sensor 2038 can sense changes in the sensed temperature that occur within a selectable time window of the 24-hour daily cycle and exceed a selectable threshold. In some instances, the selectable time window may include one hour, two hours, or other time periods. In some such instances, a method includes selecting a predetermined number of degrees of change within the selectable time window to correspond to a transition from wakefulness to sleep or a transition from sleep to wakefulness.

[0169] In some example methods, sensing temperature changes during a treatment cycle (e.g., by sensor 2038) can be used to identify sleep-disordered breathing behavior. In some such examples, in addition to the sensed temperature, additional sensed information (as described in examples of the present disclosure) can also be used to identify sleep-disordered breathing (SDB) behavior.

[0170] In some examples, this temperature fluctuation information sensed by the temperature sensor 2038 can be used in conjunction with the boundary parameters 3016 to automatically enforce boundaries or limits at the beginning and end of a treatment cycle, such that the lowest sensed body temperature can be used to at least partially mark the boundary of the end of a treatment cycle for a typical patient sleeping at night. Similarly, the highest sensed body temperature (e.g., maintained for an extended period of time) can be used to at least partially enforce boundaries at the beginning of a treatment cycle. In some such examples, these features can be used to enforce Figure 20H Method 787 in.

[0171] In some instances, these same temperature-based boundaries can be used as a factor (among other factors) in determining sleep-wake state. At least some other factors that can be used with this sensed temperature fluctuation information to determine sleep-wake state can include date and time parameters, accelerometer information, cardiac information, respiratory information, etc.

[0172] In some instances, small but detectable temperature changes during a treatment cycle can be used to at least partially determine sleep-wake state. For example, a detectable temperature change can be sensed as a result of a patient laboring to breathe in response to an apnea event, given the greater muscle effort involved in attempting to breathe.

[0173] Furthermore, in some instances, this sensed temperature fluctuation information can provide a more unique or characteristic indication of a sleep or wake cycle when compared to heart rate or body position, which, at least in some cases, may exhibit more variation, some of which are not necessarily indicative of a sleep or wake cycle.

[0174] In some instances, combined Figure 24 (and / or Figure 27A ) At least some of the sensors and / or sensor modalities described may be incorporated into a pulse generator ( Figure 25A 2133 in the IPG 2133) or on the pulse generator, or incorporated into a micro stimulator (e.g., Figures 25A to 25E ) or on a micro stimulator.

[0175] Figure 25A21 is a diagram schematically illustrating several example embodiments of sensing elements and neural stimulation devices 2113 implanted in a patient. The sensing elements and / or neural stimulation devices may be used in at least some of the example methods and / or example devices described throughout this disclosure. Figure 25A As shown in , 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 subcutaneously implanted and engaged relative to an upper airway patency-related nerve 2105 (e.g., the hypoglossal nerve). In some instances, the IPG 2133 is implanted in the pectoral region 2101, with the stimulation lead 2117 extending upward into the head and neck region 2103. In some instances, the stimulation electrode 2112 may be implanted chronically and may include a cylindrical arrangement that is at least partially wrapped around a nerve, may include a paddle-shaped electrode, may include a non-skin-attached configuration, or other configurations in which the electrode may be chronically implanted in a neuro-stimulating relationship with the nerve.

[0176] In some instances, the stimulation electrode 2112 can include at least some substantially the same features and properties as those described in US Pat. No. 8,340,785, “SELF EXPANDING ELECTRODE CUFF,” issued Dec. 25, 2012, to Bonde et al., and US Pat. No. 9,227,053, “SELF EXPANDING ELECTRODE CUFF,” issued Jan. 5, 2016, to Bonde et al., both of which are incorporated herein by reference in their entirety. In some instances, the stimulation electrode 2112 can include at least some of the substantially same features and properties as described in Johnson's US Pat. No. 8,934,992, “NERVE CUFF,” issued Jan. 13, 2015, and / or Rondoni's “CUFFELECTRODE,” disclosed as WO 2019 / 032890 on Feb. 14, 2019 (and filed as U.S. application Ser. No. 16 / 485,954 on Aug. 14, 2019), both of which are incorporated herein by reference in their entireties. In addition, in some instances, the stimulation lead 2117 can include at least some of the substantially same features and properties as the stimulation lead described in Christopherson et al., U.S. Pat. No. 6,572,543, which is incorporated herein by reference.

[0177] However, it should be understood that in some instances, the IPG 2133 may also be in the form of a microstimulator sized to be placed in the head and neck region 2103 proximate to the upper airway patency-related nerve 2105 to be stimulated. In some such instances, the microstimulator 2133 may also be combined with and / or include stimulation electrodes 2112 and / or sensing electrodes. In some example embodiments in which the IPG 2133 may include a microstimulator, placing the microstimulator in the head and neck region 2103 (e.g., proximate to the upper airway patency-related nerve) also places any exposed electrodes (e.g., 2135) closer to the nerve 2105 and closer to the head 2105 on the microstimulator, whereby EEG information (including sleep information) may be determined via such electrodes 2135. In some instances, such example microstimulators can include at least some of the same features and properties as described in connection with at least "MICROSTIMULATION SLEEP DISORDERED BREATHING (SDB) THERAPY DEVICE" disclosed as PCT publication WO2017 / 087681 on May 26, 2017, filed on November 17, 2016, with application number PCT / US2016 / 062546, and filed on May 8, 2018, with U.S. application serial number 15 / 774,471, which are incorporated herein by reference in their entirety. In such examples including a microstimulator as an IPG 2133, the stimulation lead 2117 can be omitted (while still retaining the stimulation electrode 2112), or the stimulation lead 2117 can be significantly shortened.

[0178] By such a neurostimulation device (2133, 2112), delivery of stimulation signals to the upper airway patency-related nerves 2105 can cause contraction of at least some upper airway patency muscles (e.g., the genioglossus muscle) to cause at least tongue protrusion, thereby maintaining or restoring upper airway patency and thereby providing therapeutic treatment for obstructive sleep apnea. Figures 27A to 36 At least some additional example implementations are described with respect to such stimulation.

[0179] In some instances, an example micro stimulator can be implanted in the head and neck region of a patient (e.g., 2103) to sense at least some desired sleep-wake related information that can be used to perform sleep-wake determinations. In some instances, sleep-wake determinations can be used to implement, control, adjust, etc., treatment of sleep-disordered breathing based on neural stimulation of nerves, muscles, tissues, etc. associated with upper airway patency. At least some example embodiments of micro stimulators implanted in the head and neck can be employed in conjunction with at least Figures 25B to 25F Descriptive form.

[0180] In such cases as later Figures 25B to 25F In some examples described in , the device implanted in the head and neck region may include a sensing element that forms part of and / or is associated with the micro stimulator. In some such examples, the sensing element may be used to determine sleep-wakefulness by detecting a cardiac signal (e.g., heart rate) based on an ECG or arterial motion. In some examples, the sensing element may be used to determine sleep-wakefulness by detecting a respiratory signal (e.g., a respiratory motion or a subset of such motion), which may be considered as a sound, including but not limited to snoring. In some such examples, the sensing element may detect both a cardiac signal and a respiratory signal.

[0181] In some instances, whether involving microstimulation or other implantable pulse generators ( Figure 25A IPG 2133 in the literature), changes in the signal sensed after and / or during stimulation can be used to quantify therapeutic effectiveness and / or can be used to enable automatic titration of stimulation, such as later in combination with at least Figures 27A to 27E Further described.

[0182] In some examples, the stimulation electrode 2112 can also be used as a sensing element to sense physiological information. In some such examples, the electrode 2112 can be used as the only sensing element to sense physiological information according to each sensing portion 2000 ( Figure 24 ) or sensing portion 2500 ( Figure 27A ) senses physiological information, the sole sensing element being, for example, a single-channel EEG electrode or a single-channel ECG electrode or other sensing modality. As noted below, in some instances, the stimulation electrode 2112 can be used for sensing in combination with other sensing elements and / or sensing modalities.

[0183] In some examples, the stimulation lead body 2118 may include a sensing element (e.g., an electrode) 2120, which may be used as the sole sensing element to detect the presence of a signal according to the sensing portion 2000 ( Figure 24 )、2500( Figure 27A ) senses physiological information, such as cardiac information, EEG information, EMG information, movement information, etc. Thus, in some examples, the sensing element 2120 may include an accelerometer. However, in some examples, when used in conjunction with a conductive external portion (e.g., at least a portion of a housing / casing) of an implantable stimulator (e.g., an IPG or microstimulator), the sensing element (e.g., electrode) 2120 may be considered the sole sensing element.

[0184] In some instances, the single / only sensor may include a pressure sensor (e.g., Figure 24 2037), and in some instances, by Figure 27AThe pressure parameter 2537 in the sensing portion 2510 of the care engine 2500 tracks, evaluates, etc. the pressure sensed by the sensor 2037.

[0185] In some instances, the EMG information sensed by one of the electrodes (e.g., 2120, 2112, etc.) may include detecting upper airway patency to assess obstruction (e.g., degree, location, etc.) and / or assessing stimulation effectiveness, as well as detecting (and / or assessing) inspiration / expiration during respiration. In some instances, the sensed EMG information may include sensing intercostal muscle activity to identify respiratory cycle information (e.g., inspiration, expiration, expiratory pause) and / or identify or differentiate between central sleep apnea and obstructive sleep apnea.

[0186] However, in some examples, one or both of electrode 2112 and electrode 2120 can be used in conjunction with other sensing elements (eg, electrodes) to sense physiological information.

[0187] In some examples, the IPG 2133 includes a sensing element 2135. In some such examples, the sensing element 2135 is located on a surface of (or forms a portion of) the housing of the IPG 2133, and one of the electrodes 2112 and 2120 can be used in conjunction with the electrode 2135 to measure bioimpedance ( Figure 24 2036 in; Figure 27A 2536) to obtain ECG signals, EMG signals, etc., to sense cardiac information (including cardiac morphology), respiratory information (including respiratory morphology), movement / movement of the chest, neck and / or head, etc.

[0188] 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 accelerometer. The accelerometer may be located internally to the IPG 2133, externally to the IPG 2135, or may extend a short distance from the IPG 2133 via a small lead body.

[0189] If combined separately Figure 24 and Figure 27AAs discussed in at least parameters 2026, 2526 of , the accelerometer can be used to sense movement at the chest, neck, and / or head (or movement of the chest, neck, and / or head), cardiac information, respiratory information, and the like. In some instances, the accelerometer can be used to sense physical activity / movement / motion, such as whole-body movement (e.g., walking, talking), which can indicate activity associated with wakefulness. Alternatively, in some instances, sensing a lack of activity via the accelerometer can indicate a sleep-wake state. In some such instances, the accelerometer can be used to sense physiological information without being used to sense posture or body position for use in at least some of the example methods for determining sleep-wake state, as previously described herein. However, in some instances, the accelerometer can be used to sense such posture or body position.

[0190] Further references Figure 25A In some examples of determining sleep-wake states, electrodes 2110 may be subcutaneously implanted in a head portion 2105 (e.g., above the shoulders) of a head and neck region 2103 to sense brain electrical activity and obtain EEG information and / or other central nervous system (CNS) information, wherein such sensed information is used to determine sleep-wake states. Among other aspects, the onset of sleep, the termination of sleep, and / or various sleep stages may be determined by the sensed EEG information. In some examples, multiple electrodes 2110 may be placed subcutaneously around the head portion 2015 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, electrodes 2110 may comprise a sole sensing element for determining sleep-wake states.

[0191] In some example methods and / or devices for determining sleep-wake states, electrodes 2114 may be implanted in or proximate to the tongue 2115 to sense electromyographic (EMG) information. Such sensed EMG information may include signals indicative of voluntary control of the tongue (e.g., speaking, eating, etc.), which in turn may indicate wakefulness. Additionally, such sensed EMG information may include signals indicative of sleep and / or sleep-disordered breathing (e.g., obstructive events), such as when the tongue may relax into a position that blocks the upper airway.

[0192] It should be understood that Figure 25AOnly some of the various electrodes shown in the drawings may be implanted or present in certain example embodiments. Furthermore, while some of the electrodes (if present) may be used in combination with one another, some of the electrodes may be used to implement a particular sensing modality periodically or selectively, rather than at all times. For example, there may be time periods in which some electrodes are used to sense one modality (e.g., cardiac information, such as ECG or other), while some electrodes are used to sense another modality (e.g., impedance) during certain time periods, wherein such time periods overlap, coincide, or are independent of one another.

[0193] With this in mind, in some instances, one or more sensing modalities for determining wakefulness-sleep state may be implemented in some circumstances, while a different sensing modality (or a different combination of sensing modalities) may be implemented in other circumstances. For example, certain sensing modalities may be employed only or infrequently during a portion of the daily cycle (e.g., a normal wakefulness cycle, e.g., 6 a.m. to 10 p.m.), and not at all or infrequently during another portion of the daily cycle (e.g., a normal sleep cycle), or vice versa.

[0194] In some example methods and / or apparatus, normal wake cycles may be identified by at least one of clinician input, patient input, machine learning, and other observational criteria. In the instance of clinician input or patient input, a user may directly specify a start time and / or end time for a normal wake cycle (and conversely, a normal sleep cycle). In some examples, a normal wake cycle (or conversely, a normal sleep cycle) may be determined at least in part by historical data for a particular patient and / or historical data for multiple patients or a general population. In some such examples, machine learning (e.g., Figure 27A 3230 in ) can be applied to historical data to make the determination. In some such instances, machine learning can be performed continuously every day using at least the most recent historical data (e.g., the past 30 days).

[0195] In some examples, as described later, a sleep probability (or sleep-wake state) can be determined based on multiple sleep-wake state parameters, where different sleep-wake state parameters can be weighted differently. Such different weightings for a given sleep-wake state parameter can depend on the time of day, clinician / patient input, etc. Figures 33 to 36 These and / or other aspects of determining sleep probability are further described.

[0196] like Figure 25AA As schematically shown in FIG. , in some examples, Figure 25AThe IPG 2133 may include a plurality of sensing elements (e.g., electrodes 2145, 2147) mounted on or formed as part of an outer surface (e.g., housing) of the IPG 2133. As previously described elsewhere, this arrangement can be used to sense cardiac information (e.g., ECG, other), impedance, etc.

[0197] In some instances, whether mounted in a single housing (e.g., Figure 25AA 2133) or in multiple locations (or on different components), Figure 25A 、 Figure 25AA The electrodes shown in FIG can be used to sense cardiac information (including cardiac morphology), respiratory information (including respiratory morphology), chest and / or neck motion / movement, etc., as combined with the sensing portion 2000 ( Figure 24 ) and / or sensing portion 2510 ( Figure 27A ) described. In some instances, the sensed information may include respiratory rate and / or heart rate. In some such instances, the sensed respiratory information and / or cardiac information may include at least some Figure 27A The respiratory part 2580 and the cardiac part 2600, Figure 27B Heart diagram 3500 and / or Figure 27C Essentially the same features and properties as described in the breathing diagram 150 in FIG.

[0198] In some instances, respiration information is obtained by measuring transthoracic impedance through electrodes on the IPG only or through electrodes in addition to electrodes present on the surface of the IPG. However, in some instances, respiration information may be derived from ECG information.

[0199] With this in mind, in some examples described elsewhere in this disclosure, respiratory information and / or cardiac information may be acquired via an accelerometer ( Figure 24 As previously noted, sometimes an accelerometer may be used to sense respiratory information, cardiac information, and / or other information to determine a sleep-wake state, and sometimes sensing modalities other than an accelerometer (e.g., ECG electrodes, EMG electrodes, etc.) may be used to sense respiratory information, cardiac information, and / or other information to determine a sleep-wake state.

[0200] Unless otherwise specifically stated, it should be understood that Figure 25A The electrodes described in include exposed conductive portions to engage body tissue within a patient's body, among other things.

[0201] In some instances, a single SDB care device includes a single housing. In some instances, the single device includes an onboard power supply. In some instances, the single device includes multiple sensing elements (e.g., electrodes). In some instances, at least one sensing element (e.g., electrode) is located on two separate portions of the device. For example, one electrode can be located on the IPG 2133, while another electrode can be located on the stimulation lead body 2118.

[0202] As previously combined with at least Figure 25A 、 Figure 25AA As described, in some examples, an implantable pulse generator (IPG) may take the form of a micro stimulator and may be used to implement various sensing modalities as previously described. At least some example embodiments of such micro stimulators may be implemented in at least Figure 25B and Figure 25E Shown in.

[0203] like Figure 25B As shown in FIG, an example device 2359 including an example micro stimulator 2355 can be implanted in the head and neck region 2302 of a patient, specifically in the neck region 2303 in this example. The micro stimulator 2355 is implanted subcutaneously through an access incision 2311. In the specifically shown example, a stimulation electrode 2310 is electrically connected to the micro stimulator 2355 and extends from the micro stimulator, wherein the stimulation electrode 2310 is coupled to the nerve 2305 to stimulate the nerve, which causes the muscle system (e.g., tongue) to contract, thereby maintaining or restoring upper airway patency to treat sleep-disordered breathing. In some examples, the stimulation electrode 2301 may include at least some Figure 25A The stimulation electrode 2112 has substantially the same features and properties as in FIG, including being used as a sensing electrode in some instances.

[0204] As in Figure 25C As also shown in the schematic representation of the example device 2400 in FIG. 2 , in some instances, the micro stimulator 2355 may include at least one electrode (e.g., 2402 and / or 2404), relative to which a sensing vector V1, V2, and / or V3 in the electrodes 2310, 2402, 2404 may be established to sense physiological phenomena (e.g., ECG, bioimpedance, movement at (or of) the neck 2303, etc.), as previously described. This sensed physiological information may be used to determine sleep-wake states, for example, to implement stimulation therapy. It should also be understood that in some instances, in combination with Figure 25A 、 Figure 24 and Figure 27A The additional sensing modalities described (e.g., EMG) can be Figures 25B to 25F At least a portion of the microstimulation device is implemented. Figure 25B Not fully shown, Figure 25CIt is shown that the electrode 2310 can be arranged on the lead 2313 extending from the micro stimulator 2355.

[0205] As in Figure 25D As also shown in the schematic representation of the example apparatus 2420 in FIG. 2 , in some examples, the micro stimulator 2355 may include an accelerometer 2422, and through the accelerometer, sensing physiological information (e.g., by sensing movement at the neck or movement of the neck, etc.) may be implemented as previously described throughout this disclosure. Additionally, Figure 25D The micro stimulator 2355 in the embodiment may further include an electrode 2402 (e.g. Figure 25C ), through which at least some of the previously described sensing (e.g., cardiac, ECG, bioimpedance, motion, etc.) can be implemented via sensing vector V2. This sensed physiological information can be used to determine sleep-wake state, for example, and to implement stimulation therapy.

[0206] Figure 25E A schematic representation 2450 of an example apparatus 2459 is provided that includes at least some Figures 25B to 25D The invention also includes a dedicated sensing lead 2433 extending from the micro stimulator 2355 (subcutaneously) into the tissue to support connection to the micro stimulator 2355 and / or other electrodes (e.g., Figure 25F 2431, 2432 or 2404) spaced apart from each other. This arrangement can be used to provide a similar arrangement to that for at least Figures 25B to 25D The example arrangement described in the embodiment is carried out by means of vectors V1, V2, V3, V5, V6 and / or V7 ( Figure 7 ) senses physiological information (e.g., ECG, bioimpedance, movement at the neck 2303 or movement of the neck).

[0207] In some instances, Figures 25B to 25F The microstimulator device of the present invention facilitates SDB care, including sleep-wake determination, in a compact arrangement in which sensing, stimulation, implant access, etc. can be implemented in a single body part (e.g., the neck) rather than being dispersed among several body parts (e.g., the neck and the torso), thereby simplifying implantation and SDB care. For example, a microstimulator device located in the neck can sense physiological phenomena (e.g., breathing, heart, etc.), which may sometimes be primarily associated with different parts of the body (e.g., the chest), while conveniently placing the stimulation element in the neck region where the microstimulator is located.

[0208] Figure 26 is a block diagram schematically illustrating an example processing portion 2200 that may form part of and / or be in communication with at least the sensing portion 2000. Figure 24A) In general, the processing portion processes the data generated by a single sensor, a single sensor type, or a combination of at least Figure 24 The signals and / or information obtained by the various types of sensors described are as follows. Figure 26 As shown in , the processing portion 2200 may include a filtering function 2210 for filtering the sensed signal to exclude noise, irrelevant information, etc. In some examples, the processing portion 2200 may include an interpretation function 2212, which may interpret the information sensed by the sensing portion 2000 based on the sensed physiological information present in a typical sleep pattern. In some such examples, the interpretation may be performed at least in part with respect to information associated with the reference parameters 2220. In some such examples, the information obtainable via the reference parameters 2220 (for interpreting the sensed information) may include respiratory rate and / or respiratory signal morphology, and / or may include heart rate and cardiac signal morphology. Normalization may or may not be used.

[0209] In some examples, the sensing portion 2000 ( Figure 24 ) and / or processing portion 2200( Figure 26 ) can be used in methods of extracting important features from sensor signals. Such feature extraction may include bandpass filtering, frequency analysis, power spectrum analysis, signal amplitude analysis, differential signal analysis, use of thresholds, and differential signal analysis. In addition, in some instances, such feature extraction may also include amplification and gain control, outlier removal methods based on physiological rate and / or wavelet analysis and combinations of the aforementioned parameters. In some instances, feature extraction may involve analyzing periods of periodic behavior and / or be performed to enable analysis of periods of periodic behavior. For example, feature extraction may be performed on the sensed signal and the extracted features may be analyzed as a moving average or as distributed discrete time blocks to determine whether a particular extracted feature (e.g., heart rate, heart rate variability, respiratory rate, etc.) has reached a stability threshold or shows a change from previous behavior.

[0210] In some examples, at least a portion of processing portion 2200 may include and / or be implemented with at least some combination of Figures 33 to 36 Describes the characteristics and properties.

[0211] In some instances, all or a portion of processing portion 2200 may be incorporated into Figure 27A The sensing portion 2510 or other portion of the care engine 2500 and / or may be incorporated into the control portion 4000 ( Figure 28A )Inside.

[0212] Figure 27A is a block diagram schematically illustrating an example care engine 2500. In some examples, the care engine 2500 may form part of the control portion 4000, such as later incorporated into at least Figure 28A As described, for example, but not limited to, including at least a portion of instructions 4011 and / or information 4012. In some examples, the care engine 2500 can be used to implement at least some of the previously described Figures 1 to 26 described and / or later combined Figures 27B to 36 In some examples, the care engine 2500 ( Figure 27A ) and / or control section 4000( Figure 28A ) can form a pulse generator (e.g., FIG. 25A to FIG. 25B 2133) as a part of and / or in communication with the pulse generator, regardless of whether such element includes a micro stimulator or other arrangement.

[0213] In one aspect, Figure 27A At least the sensing portion 2510 of the care engine 2500 directs the sensing of information, and / or receives, tracks, and / or evaluates information transmitted by the sensing portion 2510 ( Figure 24 )'s sensing modalities, sensing elements, etc., wherein the care engine 2500 utilizes such information to determine sleep-wake state, as well as other actions, functions, etc. as further described below.

[0214] like Figure 27A As shown in FIG, in some examples, the care engine 2500 includes a sensing portion 2510, a sleep state portion 2650, a sleep disordered breathing (SDB) parameter portion 2800, and / or a stimulation portion 2900. In some examples, the sensing portion 2510 may include EEG parameters 2512 to sense EEG information, such as a single channel (2514) or multiple channels of EEG signals. Such sensed EEG information may be sensed by the EEG sensor 2012 ( Figure 24 ) or derived from information sensed by another sensing modality. In some instances, the EEG information sensed according to the parameters 2512 includes sleep state information. In some such instances, the sleep state information may include parameters provided in the sleep state section 2650 of the care engine 2500 described later.

[0215] In some examples, the sensing portion 2510 may include electrooculogram (EOG) parameters 2524 related to, for example, the presence of a molecule detected by an EOG sensor (e.g., Figure 24 2024) receives, tracks, evaluates, and / or directs sensing of eye movements, eye positions, etc. In some such examples, the sensing element may include an optical sensor.

[0216] In some instances, this EOG information can be used as part of determining and / or confirming sleep state information and other CNS information (2532) that can be used to sense, diagnose, and / or treat sleep-disordered breathing (SDB) behavior. For example, in some such instances, this EOG information can include sleep patterns during sleep according to parameters 2668 ( Figure 27A ) detects and / or tracks rapid eye movement (REM), which in turn can be used to distinguish between wake states, REM states and / or other sleep states, including various sleep stages.

[0217] like Figure 27A As also shown in FIG, the care engine 2500 may include a sleep state portion 2650 for sensing and / or tracking sleep state information, which in some instances may be obtained via EEG information parameters 2512. In some instances, the sleep state portion 2650 may identify and / or track the onset of sleep (2660) and / or the end of sleep (2662), as well as identify and / or track sleep stages once the patient falls asleep. Thus, in some instances, the sleep state portion 2650 includes sleep stage parameters 2666 to identify and / or track the various sleep stages (e.g., REM and N1, N2, N3 or S1, S2, S3, S4) of the patient during the treatment portion or over a longer period of time. In some cases, the various stages other than REM sleep (e.g., N1-N3 or S1-S4) may sometimes be referred to as non-REM sleep. In some examples, the sleep state section 2650 may also include separate rapid eye movement (REM) parameters 2668 to sense and / or track REM information associated with various aspects of sleep disordered breathing (SDB) care, as further described below and throughout various examples of this disclosure. In some examples, the REM parameters 2668 may form part of or be used in conjunction with the sleep stage parameters 2666.

[0218] In some examples, the sleep state section 2650 may include wakefulness parameters 2664 to guide sensing of the patient's wakefulness state and / or to receive, track, evaluate, etc. The patient's wakefulness state may indicate general periods of non-sleep (e.g., daytime hours) and / or interrupted sleep events, such as long awakenings (according to parameters 2672) associated with the patient waking up to use the restroom (e.g., to urinate, etc.), turning over in bed, waking up in the morning to turn off an alarm, etc.

[0219] Conversely, in some instances, the sleep state portion 2650 may include a micro-arousal parameter 2674 by which neural arousals associated with sleep-disordered breathing (SDB) events may be detected and / or tracked, wherein the patient experiences short neural arousals due to sleep apnea, such as, but not limited to, obstructive sleep apnea, central sleep apnea, and / or hypopnea. Such SDB-related micro-arousals typically do not cause the patient to wake up in the traditional sense familiar to the layperson. In at least some instances, the stimulation intensity within a treatment cycle is not changed in response to such SDB-related micro-arousals, as one goal of treatment is to electrically stimulate to prevent or substantially reduce sleep-disordered breathing, which in turn reduces the frequency and volume of such SDB-related micro-arousals.

[0220] In some instances, the sleep detection method / apparatus, through at least the sleep state portion 2650 of the care engine 2500, can distinguish between arousals occurring during sleep and sleep-disordered breathing (SDB). In other cases, such a distinction can enable effective neurostimulation therapy, for example, when the patient is in a sleeping position (e.g., lying horizontally or flexed), and the sleep detection device detects a change in the sensed data that can be interpreted as turning over (e.g., from a supine position to their side (e.g., lateral position) or vice versa) or consistent with SDB behavior. In the event that the patient actually turns over, such as when getting out of bed, the system will suspend the neurostimulation therapy. However, if the detected change can be confirmed as legitimate SDB behavior, the system / method does not suspend the neurostimulation therapy in at least some instances.

[0221] With this in mind, in some instances, the apparatus / method can distinguish between REM sleep (even in the absence of sleep-disordered breathing (SDB)) and wakefulness, at least because the system avoids pausing neurostimulation therapy for sleep-disordered breathing if the patient is in REM sleep. Conversely, if the patient is actually awake, the system should not initiate neurostimulation therapy, or can pause or terminate neurostimulation therapy. In some instances, a characteristic feature associated with REM is a lack of body movement, which can sometimes be referred to as paralysis or at least partial paralysis of voluntary muscle control.

[0222] In some instances, sleep-disordered breathing can occur during REM sleep, such that at least some example apparatuses / methods can distinguish sleep-disordered breathing from arousal, and / or distinguish REM sleep from arousal. For example, in some such instances, sensing a lack of body movement can prevent false positives if / when other parameters (e.g., HR) may otherwise indicate arousal. For example, during REM sleep, the sensed information can indicate increased variability in the patient's breathing cycle and / or heart rate (HR).

[0223] In some such instances and as previously described, sleep state information (according to sleep state portion 2650) can be used to guide, receive, track, assess, diagnose, etc. sleep disordered breathing (SDB) behavior. In some such instances and as previously described, sleep state information can be used in a closed-loop manner to initiate, terminate, and / or adjust stimulation therapy to treat sleep disordered breathing (SDB) behavior to enhance device efficacy. Later in conjunction with at least Figure 27A Parameters 2910 in further describe at least some example closed-loop implementations.

[0224] For example, in some instances, stimulation therapy can be automatically terminated by sensing arousal (2664 in the sleep state portion 2650). In some instances, stimulation therapy can be automatically initiated by sensing the onset of a particular sleep stage (2666). In some instances, the intensity of stimulation therapy can be adjusted and implemented based on a particular sleep stage and / or specific characteristics within a sleep stage. In some instances, a lower stimulation intensity level can be implemented when a REM sleep stage is detected. In some instances, stimulation intensity can be reduced during some sleep stages to save power and battery life, as well as to improve patient comfort and / or treatment utilization.

[0225] In some instances, in conjunction with at least the sleep stage parameters 2666 of the care engine 2500, delivery of stimulation signals can be switched between different predetermined intensity levels for each different sleep stage (e.g., N1, N2, N3 or S1, S2, S3, S4, REM).

[0226] In addition to the above Figure 27A In addition to the sensing parameters, modalities, etc. described above, 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 to guide sensing from previously combined Figure 24 The ECG sensor 2020, EMG sensor 2022, accelerometer 2026, pressure parameter 2037, temperature sensor 2038, and / or acoustic sensor 2039 described herein may be used to sense and / or receive, track, evaluate, etc. In some examples, EMG parameters 2522 may include detecting muscle activity and / or movement of intercostal muscles, upper airway, and / or tongue, for example, in combination with at least Figure 25A and other examples described throughout this disclosure.

[0227] In some examples, the sensing portion 2510 ( Figure 27A) includes impedance parameters 2536 for sensing impedance within the patient and / or tracking sensing of impedance within the patient to sense motion at the chest and / or neck (or motion of the chest and / or neck) and / or other parameters to determine sleep-wake state. In addition to or in lieu of determining sleep-wake state, impedance parameters 2536 may also be used to sense respiratory information and / or other information associated with sleep disordered breathing (SDB) care. Impedance parameters 2536 may be obtained from Figure 24 The impedance sensor 2036 and / or other sensors in the device obtain impedance information.

[0228] In some examples, the sensing portion 2510 of the care engine 2500 may include posture parameters 2540 to guide the sensing of Figure 24 The method of the present invention may include sensing the signals of the posture sensor 2040 or other posture, body position sensor, etc. described previously in the present invention, and / or receiving, tracking, evaluating, etc. the sensing of the signals. As with the other parameters of the sensing portion 2510, the posture parameter 2540 can be used alone or in combination with other parameters to determine the sleep-wake state of the patient. However, as previously noted, in some example methods (and / or devices), the sleep-wake state can be determined without (or independently of) posture information.

[0229] In some instances, the sensing portion 2510 of the care engine 2500 includes snoring parameters 2545 to guide the sensing of snoring information and / or the receipt, tracking, evaluation of snoring information, etc., which in some instances can be detected and obtained through motion sensing. In some instances, this sensed snoring information can be used to at least partially determine the sleep-wake state. In one aspect, snoring can be defined as the noise associated with each exhalation when the respiratory cycle is relatively stable and the frequency content is stable. In contrast, speaking lacks a stable respiratory cycle and frequency content and therefore is not detected as snoring. As mentioned elsewhere, in some instances, the snoring information is detected by the acoustic sensor 2039 ( Figure 24 ) and / or acoustic parameters 2539( Figure 27A ) Sense snoring.

[0230] In some examples, the sensing portion 2510 of the care engine 2500 may include a history parameter 2542 by which a history of sensed physiological information is saved, and the history parameter may be used to compare recent sensed physiological information with older sensed physiological information via a comparison parameter 2544. FIG. 15A to FIG. 15B At least some example implementations using such history parameters 2542 and comparison parameters 2544 are described.

[0231] In some examples, at least some example methods of determining sleep-wake state by the care engine 2500 may include identifying sleep by trends (including variability) in respiratory rate and / or heart rate. In some examples, sleep-wake state determination may include identifying sleep by the morphology of the respiratory cycle, the stability of the respiratory rate, and / or the stability of the respiratory morphology. At least some of these examples are further described below in conjunction with at least the respiratory portion 2580 of the care engine 2500.

[0232] like Figure 27A As shown in FIG, in some examples, the care engine 2500 can include a respiration portion 2580. In at least some examples, the respiration portion 2580 can generally be directed to sensing respiration patterns (including general patterns and / or specific benchmarks within a respiration signal), and / or receiving, tracking, and / or evaluating respiration patterns. In some examples, the respiration portion 2580 can be coupled with Figure 27A The sensing portion 2510 and / or sensing portion 2000 of the care engine 2500 ( Figure 24 ) operates in collaboration with or as part of a respiratory system. At least some aspects of such respiratory morphology managed by the respiratory portion 2580 may include an inspiratory morphology (parameter 2582) and / or an expiratory morphology (parameter 2584). In some instances, the respective inspiratory morphology parameters 2582 and / or expiratory morphology parameters 2584 may include the amplitude, duration, peak (2586), start (2588), and / or cutoff (2590) of the respective inspiratory and / or expiratory phases of the patient's respiratory cycle. In some instances, the detected respiratory morphology may include a transition morphology (2592), such as an inhalation-to-expiration transition and / or an exhalation-to-inhalation transition. In some instances, any one or more of these aspects of the respective inspiratory and expiratory phases (e.g., peak, start, cutoff, magnitude, etc.) may be used to, at least in part, determine sleep and / or wakefulness.

[0233] For example, the inhalation-to-expiration transition associated with the respiration portion 2580 of the care engine 2500 can be used as a baseline for detecting and / or tracking respiratory rate (and respiratory rate variability), which can indicate changes in wakefulness-sleep state. At least one such example inhalation-to-expiration transition 180 is shown in FIG. Figure 27C In some examples, changes in the duration of the inspiration to expiration transition, changes in the peak-to-peak amplitude, and / or changes in the respiratory rate can be indicative of sleep and / or wakefulness and, therefore, used to determine sleep-wake state.

[0234] Regarding the above-described sensing and tracking of respiratory patterns, Figure 27C150 is a diagram schematically representing a breathing cycle 150 illustrating at least some aspects of a breathing morphology, wherein the breathing cycle 150 includes an inspiratory phase 162 and an expiratory phase 170. The inspiratory phase 162 includes an initial portion 164 (e.g., a start), an inspiratory peak 165, and an ending portion 166 (e.g., a cutoff), while the expiratory phase 170 includes an initial portion 174 (e.g., a start), a middle portion 175 (including an expiratory peak 177), and an ending portion 176 (e.g., a cutoff). The peak parameter 2586, the start parameter 2588, and the cutoff parameter 2590 of the inspiratory morphology 2582 described above (in the sensing portion 2510 of the care engine 2500) correspond to Figure 27C The inspiratory peak 165, inspiratory start 164, and inspiratory cutoff 166 of the respiratory cycle graph 150 in FIG. 150 correspond to the peak parameter 2586, the start parameter 2588, and the cutoff parameter 2590 of the expiratory morphology parameter 2584 described above (in the sensing portion 2510 of the care engine 2500) Figure 27C 16. Exhalation peak 177, exhalation start 164, and exhalation stop 166 of respiratory cycle graph 150 in FIG.

[0235] exist Figure 27C In the breathing cycle diagram 150 in FIG. 1 , the first transition 180 occurs at the junction between the end of the inspiration portion 166 and the beginning of the expiration portion 174. In some cases, this transition 180 may sometimes be referred to as an inspiration to expiration transition 180, which, as described above, may be used to determine the breathing cycle according to the present invention. Figure 27A The sleep-wake state is determined by the parameters 2592 of the respiratory portion 2580 of the care engine 2500 in FIG. The second transition 182 occurs at the junction between the end of the exhalation portion 176 and the initial inspiration portion 164. In some cases, this transition 182 may sometimes be referred to as an exhalation to inspiration transition 182, which, as described above, can be used to determine the sleep-wake state according to the Figure 27A Parameters 2592 of the respiratory portion 2580 of the care engine 2500 in determine the sleep-wake state.

[0236] In some instances, such as Figure 27AAs shown in , the respiratory portion 2580 may include chest wall parameters 2594 to guide sensing of the patient's chest wall behavior, and / or receiving, tracking, evaluating, etc. the patient's chest wall behavior. In some such instances, the chest behavior may include chest wall motion (e.g., rib cage motion). In some instances, the sensed chest wall motion (e.g., for determining sleep-wake state) may include general motion of the chest wall associated with inspiration and expiration of the respiratory cycle (e.g., rising and falling) as the patient breathes. In some cases, the chest wall motion may include intercostal muscle contraction. In some instances, this sensed general chest wall motion (e.g., for determining sleep-wake state) does not include features and / or signal information such as chest muscle contraction (which may be unrelated to respiratory and / or cardiac function). In other uses, the sensed chest motion may be used to determine respiratory information, cardiac information, and / or other physiological information in order to determine sleep-wake state, as further described in various examples of the present disclosure. For example, one use of the sensed chest movement is to at least partially determine whether breathing is passive or active (e.g., forced), which in turn can be used to determine sleep-wake state. As just one example aspect of passive breathing, normal exhalation occurs without direct muscle effort, such as during normal Cheyne-Stokes breathing, where air can be expelled from the lungs due to the recoil effect of elastic tissue in the chest, lungs, and diaphragm. This behavior is expected to occur during sleep. In contrast, an example of active breathing that may be associated with a wake state includes forced exhalation involving contraction of the abdominal wall, internal intercostal muscles, and diaphragm.

[0237] In some instances, such as Figure 27A As shown in , the respiratory portion 2580 may include neck parameters 2595 to guide sensing of the patient's neck movement and / or receiving, tracking, evaluating the patient's neck movement, etc., which may indicate respiratory information and / or cardiac information about the patient, which may be used to determine the sleep-wake state. As previously described, such sensed neck movement and / or movement at the neck may include, for example, (but not limited to) movement from the airway and / or blood vessels, impedance, and / or other physiological phenomena. For example, at least some of the sensed impedance vectors may be measured on the airway, on the blood vessels, and / or on both.

[0238] In some examples, the respiration portion 2580 may include a respiration rate parameter 2596 to guide sensing and / or receiving, tracking, evaluating, etc., respiration rate information, including respiration rate, respiration rate variability 2597, etc., which may be used to determine sleep-wake state or changes in sleep-wake state. In some examples, sensing respiration rate (and any associated variability, trends, etc.) may be implemented by sensing and tracking one of the aforementioned identifiable parameters of respiration pattern (e.g., peak, onset, cutoff, transition) according to the respiration portion 2580 of the care engine 2500.

[0239] like Figure 27A As shown in FIG, in some examples, the care engine 2500 may include a cardiac portion 2600. In some examples, generally speaking, the cardiac portion 2600 may be used to sense, track, determine, etc. cardiac information that may indicate sleep-wake state and other information related to SDB care. In some examples, the cardiac portion 2600 may be used in conjunction with the sensing portion 2510 ( Figure 27A ) and / or sensing portion 2000( Figure 24 ) in conjunction with or as part of the care engine 2500. The cardiac portion 2600 can be used alone or in combination with other components, modalities, etc. of the care engine 2500. In some examples, the cardiac portion 2600 can employ a single type of sensing or multiple types of sensing in the sensing portion 2510, and in some examples, the cardiac portion 2600 can employ other sensing types, modalities, etc. in addition to or as an alternative to the specific sensing type, modality of the sensing portion 2510. Furthermore, the cardiac portion 2600 can determine, track, etc., sleep-wake state in conjunction with or independently of the respiratory portion 2580 of the care engine 2500.

[0240] In some examples, generally speaking, cardiac portion 2600 can be directed to sensing cardiac signal patterns and / or receiving, tracking, evaluating, etc. cardiac signal patterns to at least determine sleep-wake state. Figure 27AAs shown in , in some examples, cardiac portion 2600 includes atrial morphology parameters 2610 and / or ventricular morphology parameters 2612 that can be used alone or in combination to determine sleep-wake state. In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) can include detecting contraction (parameter 2620) and / or relaxation (parameter 2622) of the atria and ventricles, respectively. In some such examples, tracking the respective contraction and / or relaxation can facilitate determining sleep-wake state by providing an easily identifiable portion of the cardiac waveform from which heart rate (HR) and / or heart rate variability (HRV) can be detected and tracked, and based on the easily identifiable portion of the cardiac waveform, a value, trend, etc. of the heart rate or heart rate variability can indicate sleep or wakefulness.

[0241] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include peaks in atrial or ventricular contraction (2630), which may be used to determine sleep-wake state.

[0242] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include the onset (e.g., initiation) 2632 of atrial contraction, atrial relaxation, ventricular contraction, or ventricular relaxation. In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) may include the termination (e.g., end, completion) 2634 of atrial contraction, atrial relaxation, ventricular contraction, or ventricular relaxation.

[0243] In some examples, at least some aspects of the respective atrial and ventricular morphologies (2610, 2612) by which sleep-wake states may be detected may include a combination of atrial contraction and ventricular contraction.

[0244] 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.

[0245] In some examples, at least some aspects of cardiac information (from which sleep-wake states may be determined) may include opening or closing of a cardiac valve according to parameters 2642. In some examples, such detection of opening and / or closing of a cardiac valve (according to parameters 2642) may also be used to help determine the timing and / or occurrence of the onset and / or termination of contraction (or relaxation) of the atria or ventricles in conjunction with parameters 2610, 2612, 2620, 2622, 2630, 2632, 2634.

[0246] In some examples, the cardiac information may include cardiac motion 2644, and the cardiac morphology parameters described above may be determined based on the cardiac motion. Figure 24 The sensory information can be obtained by using one or more of the various sensing modalities described (e.g., accelerometer, EMG, etc.).

[0247] like Figure 27A As also shown in FIG. 2 , in some examples, cardiac information may include heart rate parameters 2645 to guide sensing heart rate information and / or receiving, tracking, evaluating, etc., the heart rate information including heart rate (HR), heart rate variability (HRV) 2646, etc., which may be used to determine sleep-wake states or changes in sleep-wake states. In some examples, sensing heart rate (and any associated variability, trends, etc.) may be achieved by sensing and tracking one of the aforementioned identifiable parameters of cardiac morphology (e.g., peak, onset, cutoff, transition) according to cardiac portion 2600.

[0248] In some examples, at least some of the above cardiac information can be based at least in part on acoustically sensed (e.g., Figure 24 2039 in; Figure 27A 2539) of the heart sounds (e.g. Figure 27B S1, S2, etc. in the .

[0249] In some examples, the sleep-wake state may be determined by a combination of sensed respiratory characteristics and sensed cardiac characteristics. Figures 5 to 9 and Figures 10 to 13 And at least some aspects of using combinations of this information are described elsewhere in the examples of this disclosure.

[0250] like Figure 27A As also shown, in some instances, the care engine 2500 includes an SDB parameters portion 2800 to guide the sensing of parameters particularly related to sleep disordered breathing (SDB) care, and / or to receive, track, evaluate, etc., parameters particularly related to sleep disordered breathing (SDB) care. For example, in some instances, the SDB parameters portion 2800 may include a sleep quality portion 2810 to sense and / or track the patient's sleep quality, particularly sleep quality related to the patient's sleep disordered breathing behavior. Thus, in some instances, the sleep quality portion 2810 includes wakeup parameters 2812 to sense and / or track wakeups caused by sleep disordered breathing (SDB) events, wherein the number, frequency, duration, etc. of such wakeups are indicative of sleep quality (or lack of sleep). In some such instances, such wakeups may correspond to, for example, a combination of at least Figure 27A A micro-wakeup described by parameter 2674 in the sleep state section 2650 of the care engine 2500.

[0251] In some examples, the sleep quality portion 2810 includes state parameters 2814 to sense and / or track the occurrence of various sleep states (including sleep stages) of the patient during a treatment cycle or over a longer period of time. In some such examples, the state parameters 2814 can cooperate with, form part of, and / or include at least some substantially the same features and attributes as the sleep state portion 2650 of the care engine 2500.

[0252] In some examples, the SDB parameter portion 2800 includes an AHI parameter 2830 to sense and / or track apnea-hypopnea index (AHI) information, which may be indicative of the patient's sleep quality. In some examples, AHI information is sensed during each of the different sleep stages experienced by the patient, wherein such sensed AHI information is at least partially indicative of the degree of sleep-disordered breathing (SDB) behavior. In some examples, the AHI information is obtained by a sensing element, such as a sensor in conjunction with at least the sensing portion 2000 ( Figure 24 ) and / or sensing portion 2510 ( Figure 27A ), which can be implemented as described in various examples of the present disclosure. In some examples, AHI information can be sensed by a sensing element, such as an accelerometer located in the torso or chin / neck area, where the sensing element can be positioned 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 can be used to sense and / or track AHI information. In some examples, AHI information is obtained by sensing modalities (e.g., ECG, impedance, EMG, etc.) in addition to accelerometers.

[0253] In some instances, the Figure 27AThe probability portion 3200 of the care engine 2500 in FIG. 1 implements sleep-wake state determination. In some such instances, by selecting parameters 3210, the probability portion 3200 can enable the selective inclusion or exclusion of at least some sleep-wake determination parameters without directly affecting the general operation of determining sleep-wake state. In some instances, the probability function 3200 can allow the patient, clinician, or caregiver to adjust sensitivity parameters 3220 to increase or decrease the sensitivity of sleep-wake state determination by a specific parameter. In some instances, machine learning parameters 3230 can be implemented to evaluate and modify adjustments to the probabilistic determination of sleep-wake state, including but not limited to adjustments to any amplitude thresholds, duration thresholds, etc. associated with the probabilistic determination of sleep-wake state. In some instances, employing such probabilistic determinations can allow for more refined control over individual patient signals (used in combination to determine sleep-wake state), which in turn can enable the ability to balance simple control with complex control and sensor flexibility when needed.

[0254] In some instances, at least through machine learning parameters 3230, the care engine 2500 may include and / or access neural network resources (e.g., deep learning, convolutional neural networks, etc.) to identify patterns indicative of sleep from a single sensor of a plurality of sensors. In some instances, expert resources based on decision trees may also be used to combine sensor or neural network output with other signals such as date and time or remote input / use. For example, an example embodiment of machine learning through parameters 3230 is later combined with at least Figures 33 to 36 Further Description. At least some other example embodiments are described throughout this disclosure.

[0255] In some examples, via the probability portion 3200 , the care engine 2500 can assign and apply weights (parameters 3240 ) associated with each signal in order to increase (or decrease) the relative importance of particular sensor signals in determining sleep-wake state.

[0256] In some examples, different thresholds may be selected for different times of the 24-hour day cycle via the time emphasis parameter 3250. For example, during a first period (e.g., during the day, such as noon), some parameters may be de-emphasized and / or other parameters may be emphasized, while during a second period (e.g., during the night, such as 10 p.m.), some parameters may be emphasized in determining sleep-wake state while other parameters may be de-emphasized. Alternatively, during the first period, the sensitivity of most or all parameters (used in determining sleep-wake state) may be reduced, and during the second period, the sensitivity of some or all parameters (used in determining sleep-wake state) may be increased.

[0257] In some such instances, this adjustability of the time emphasis parameter 3250 can enhance sleep-wake determination for patients with non-standard sleep cycles, such as garbage yard shift workers (e.g., working hours from 11 PM to 7 AM), because their expected sleep cycle (e.g., 8 AM to 3 PM) conflicts with their regular sleep cycle (e.g., 10 PM to 6 AM).

[0258] 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 motion at the chest, neck, and / or head (or motion of the chest, neck, and / or head). In some such examples, accelerometers and / or other sensors (e.g., impedance, EMG, etc.) may be used to sense motion at the chest, neck, and / or head (or motion of the chest, neck, and / or head). In some such examples, based on a differentiation parameter 3260, when sensing is performed with a sensor (e.g., an accelerometer) having multiple signal components (e.g., a multi-axis accelerometer), or when sensing captures a signal from which multiple different signals can be derived (e.g., an ECG), an example method may include separating a signal associated with the sensed physiological information into multiple different signals, each corresponding signal representing a different sleep-wake determination parameter. In other words, the multiple components within the signal are differentiated into different and separate signals, each of which may be indicative of a sleep-wake state. The probability of the sleep-wake state is then determined based on evaluating the corresponding different signals associated with the corresponding different sleep-wake determination parameters. As described above, in some examples, each respective distinct signal may comprise one axis of a multi-axis accelerometer (e.g., where each axis is orthogonal to the other axes), or (when multiple single-axis accelerometers are employed) may comprise a single-axis accelerometer. 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).

[0259] Later in combination Figures 33 to 36 At least one example implementation of dividing a signal in the manner described above is provided in the described example method / apparatus 7000 .

[0260] like Figure 27A As shown in , in some examples, the care engine 2500 can include an activation portion 3000 that can generally control activation of an implantable medical device (e.g., an IPG). In some such examples, neural stimulation delivery via the implantable medical device can be automatically activated and terminated (3010), where such activation and termination are based on sleep-wake state. In some such examples, the sleep-wake state is automatically determined by the care engine 2500.

[0261] Thus, in some instances, via automatic parameter 3010, at least some example methods and / or apparatus for determining sleep-wake states may be used to automatically initiate a therapy cycle (e.g., upon automatic detection of sleep) and automatically terminate a therapy cycle (e.g., upon automatic detection of wakefulness).

[0262] In some instances where automatic determination of sleep-wake states is unavailable or disabled by the patient (or clinician or caregiver), then based on remote parameters 3012, a therapy cycle may include a time period starting from when the therapy device is turned on using the remote control and ending when the patient turns the device off via the remote control. In some instances, based on remote parameters 3012, a therapy cycle may be initiated and / or terminated based on the level of ambient light sensed by the remote control, the level or type of motion sensed by the remote control, and / or the aforementioned therapy activation (e.g., on, off) performed by the remote control. In some instances, the remote control may include Figure 30 It will be appreciated that in some instances, the detection of the degree of ambient light and / or the degree or type of movement of the remote control may be used as part of other features described herein to perform automatic determination of sleep-wake state, which in turn may determine automatic initiation, termination, pause, adjustment, etc. of a treatment cycle in which neurostimulation therapy is applied. In some instances, the remote control parameters 4340 may be combined with Figure 20I Method 788 is implemented in.

[0263] In some instances where automatic determination of sleep-wake states is unavailable or disabled by the patient (or clinician or caregiver), a therapy cycle may include a time period starting when the patient turns on the therapy device using the app and ending when the patient turns the device off via the app, based on the app parameters 3013. In some instances, a therapy cycle may be initiated and / or terminated based on the level of ambient light sensed by the app, the level or type of motion sensed by the mobile device, and / or activation of the therapy described above (e.g., on, off) by the app on the mobile device, based on the app parameters 3013. In some instances, the app may include Figure 30 , which can be accessed through a mobile device 4320 ( Figure 30 ) is implemented in a manner such that the mobile device is, for example, a mobile smartphone, a tablet computer, a phablet, a smartwatch, etc. The mobile device may include a control portion that operates a user interface (e.g., a display) of the application, and the mobile device may include sensors for sensing the aforementioned characteristics (e.g., motion, ambient light, sound, etc.) in a manner that enables the application to perform sleep-wake determinations based, at least in part, on use (or non-use) of the mobile device.

[0264] In some examples, the sensors of the remote control and / or mobile device may include accelerometers, gyroscopes, and / or other motion detectors.

[0265] However, in some instances where automatic determination of sleep-wake state is not available (or disabled), via time parameter 3014, a therapy cycle may automatically begin at a selectable predetermined start time (e.g., 10:00 PM) and may terminate at a selectable predetermined stop time (e.g., 6:00 AM).

[0266] In one aspect, a treatment cycle corresponds to a time period during which the patient is asleep such that stimulation of the upper airway patency-related nerves and / or central sleep apnea-related nerves is generally not felt by the patient, and such that stimulation coincides with patient behavior (e.g., falling asleep) during which sleep-disordered breathing behavior (e.g., central or obstructive sleep apnea) is expected to occur. Thus, to avoid enabling stimulation before the patient falls asleep, in some instances, stimulation may be enabled during a treatment cycle after a timer started upon automatic sleep detection expires. To avoid continuing stimulation after the patient wakes up, stimulation may be disabled upon automatic detection of awakening. Thus, in at least some instances, these periods may be considered outside of the treatment cycle, or may be considered the beginning and end of a treatment cycle, respectively.

[0267] In some examples, via boundary parameters 3016, an optional predetermined first time marker (e.g., 10:00 PM) can be used as a limit or boundary to prevent automatic initiation of a therapy cycle before the first time marker (based on automatic detection of sleep), and an optional predetermined second time marker (e.g., 6:00 AM) can be used as a limit or boundary to ensure automatic termination of the therapy cycle to prevent continuation of the therapy cycle after the second time marker. With such an example arrangement, a therapy cycle can be automatically initiated by automatic sleep detection and / or automatically terminated by automatic wake detection, while assuring the patient that a therapy cycle is not initiated during a normal wake period or does not extend beyond their normal sleep period.

[0268] In some examples, determining a sleep-wake state in conjunction with boundary parameters 3016 may include and / or combine at least the aforementioned Figure 20A Method 780 in conjunction with the previously described temperature parameter 2038 ( Figure 24 ) describes the characteristics and properties of the

[0269] However, in some cases, through physical parameters 3018, the user can take physical steps to activate (or deactivate) the treatment cycle of the implantable medical device. For example, through activation portion 3000 and physical parameters 3018, the care engine 2500 can receive physical input, for example, patting the chest (or neck or head) or patting on the implant to activate or deactivate the device. Alternatively, the user can use the patient remote control function 3012 to activate or deactivate the implantable medical device, which in turn can activate or deactivate the delivery of neural stimulation. In some such instances, the activation or deactivation of the treatment cycle (during which neural stimulation is applied) can be implemented by physical movement of a remote control or mobile device (e.g., a hosted application). In some cases, through a clinician programmer or remote control, this physical feature (3018) can be activated or deactivated at the discretion of the clinician or user.

[0270] like Figure 27A As also shown in FIG, in some examples, the care engine 2500 includes a stimulation portion 2900 to control stimulation of target tissues (e.g., but not limited to, upper airway patency nerves) 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 responsive to and / or based on sensed patient physiological information.

[0271] In some examples, closed-loop parameters 2910 can be implemented to use the sensed information to control the specific timing of stimulation based on respiratory information, where stimulation pulses are triggered by or synchronized with a specific portion of the patient's respiratory cycle (e.g., the inspiratory phase). In some such examples and as previously described, this respiratory information can be obtained by sensing portion 2000 ( Figure 24 ) and sensing portion 2510 ( Figure 27A ) is determined by a single type of sensing or multiple types of sensing.

[0272] In some instances where the sensed physiological information is capable of determining (at least) 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 specific sleep stages).

[0273] like Figure 27AAs also shown in FIG, in some examples, stimulation portion 2900 includes open-loop parameters 2925 by which 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 cycle without (e.g., independently of) sensed information about 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 cycle without (i.e., independently of) specific knowledge of the patient's respiratory cycle information.

[0274] However, in some such instances, some sensory feedback may be utilized to roughly determine whether the patient should receive stimulation based on the severity of the sleep apnea behavior.

[0275] like Figure 27A As also shown, in some instances, the stimulation portion 2900 includes an automatic titration parameter 2920 by which the intensity of the stimulation therapy can be automatically titrated (i.e., adjusted) to be more intense (e.g., higher amplitude, greater frequency, and / or greater pulse width) or less intense (e.g., lower amplitude, lower frequency, and / or lower pulse width) during a treatment cycle.

[0276] In some such examples and as previously described, such automatic titration can be implemented based on sleep quality and / or sleep state information, which in some examples can be obtained through sensed physiological information. It should be understood that such examples can be used with stimulation synchronized to sensed respiratory information (i.e., closed-loop stimulation) or without synchronizing stimulation to sensed respiratory information (i.e., open-loop stimulation).

[0277] In some instances, at least some aspects of the automatic titration parameters 2920 may include and / or may be implemented by at least some substantially the same features and attributes as described in Christopherson et al., “SYSTEM FORTREATING SLEEP DISORDERED BREATHING,” issued as US Pat. No. 8,938,299 on Jan. 20, 2015, which is incorporated herein by reference in its entirety.

[0278] With respect to various examples of the present disclosure, in some instances, delivering stimulation to an upper airway patency nerve will cause contraction of muscles associated with upper airway patency. In some such instances, the contraction comprises suprathreshold stimulation, as contrasted with subthreshold stimulation of such muscles (e.g., tone alone). In one aspect, the suprathreshold intensity level corresponds to a stimulation energy greater than the nerve excitation threshold, such that the suprathreshold stimulation can provide maximum upper airway clearance (i.e., patency) and obstructive sleep apnea treatment efficacy.

[0279] In some instances, at least some example 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.

[0280] In some instances, when sleep is determined based on a minimum predetermined confidence level, the amplitude (e.g., intensity) of the stimulation signal may start at a lower value and then increase in a ramped manner to a higher value. In some such instances, the increase in amplitude (up to a desired / target value) may be based on an additional or further predetermined confidence level. However, if it is later determined that sleep did not occur, but rather that the patient was in a quiet, stationary wake state, the stimulation may be terminated or reduced while still in the ramping phase before the target stimulation amplitude is reached. In other applications, the example method may be beneficial for patients with cardiac or respiratory diseases, at least because the cardiac morphology and / or respiratory morphology (from which sleep can be detected) may be complex, making accurate detection of actual sleep in such patients more challenging.

[0281] As noted above in connection with the boundary parameters 3016 of the activation portion 3000, a clock or timing element within the implantable medical device (e.g., IPG 2133) can be used to implement boundaries or limits on when stimulation therapy (within a treatment cycle) can be automatically initiated or terminated by automatic sleep detection (or wake detection) based on the determination of sleep-wake state. In some instances, the time-based boundaries can be based on patient behavior and / or direct clinician programming. In some instances, such tracked patient behavior can be used as an input to a probabilistic model for determining sleep-wake state. In some instances, the time-based boundaries can also be based, at least in part, on a history of patient activity.

[0282] In some examples, the time-based boundary can account for daylight saving time and travel (e.g., different time zones) and can be adjusted via a patient remote control or a physical tap on the chest. In some such examples, the time-based boundary parameter can include one of multiple inputs for determining sleep-wake state, which can improve the reliability of determining sleep-wake state in a variety of environments (rather than a single time-place environment such as just the patient's bedroom).

[0283] In some instances, Figure 27A Boundary parameters 3016 for the activation portion 3000 in the example embodiment may include criteria that are not strictly based on time (e.g., date and time). For example, in some instances, boundary parameters 3016 may be implemented based on the number, type, and / or duration of various sleep stages associated with a single treatment cycle (e.g., a night's sleep). For example, the example method may determine boundaries or end limits for a treatment cycle based on observing a specific number of REM sleep cycles (e.g., 4 or 5), stage 4 sleep cycles, or stage 3 sleep cycles, etc. In some such instances, the number of specific sleep stage cycles may be selectable. In some instances, the boundaries may be based on a selectable percentage of the patient's time spent in one or more specific sleep stages.

[0284] In some examples, upon detecting a sleep state (based on sleep-wake state), a neurostimulation signal can be applied to the phrenic nerve to treat central sleep apnea. In some examples, determining sleep-wake state can be used to control the initiation and / or termination of stimulation of upper airway patency nerves (e.g., the hypoglossal nerve) and diaphragm control nerves (in a coordinated manner relative to each other) to treat sleep-disordered breathing.

[0285] In some examples, the stimulation portion 2900 can be coupled to at least the breathing portion 2580 and / or the sensing portion 2510 of the care engine 2500 (e.g., Figure 24 The sensing portion 2000 in the respiratory system operates in cooperation to determine the efficacy of the stimulation and / or whether there is flow restriction by evaluating the flow response within a single respiratory cycle. This type of evaluation is in contrast to performing such evaluation on a cycle-by-cycle basis, for example, 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.

[0286] For example, in the example method, if the stimulation portion 2900 causes a change in stimulation intensity level during the inspiratory phase (e.g., an increase or decrease), a feature of the care engine 2500 may include determining whether a substantial change in the flow response occurs (e.g., 10%, 15%, 20% or more).

[0287] In some instances, this feature can also be implemented as Figure 27D At least a portion of the method shown at 3600 in FIG. 3600 may be performed together with or as part of other example methods described herein. Figure 27DAs shown in , method 3600 may include determining whether a change in flow response occurs during the inspiratory phase of a single respiratory cycle when a change in stimulation intensity level is caused during the inspiratory phase of a single respiratory cycle. For example, if such a substantial change is determined, then confirmation of flow restriction in the upper airway, such as obstructive sleep apnea, may be provided. Upon such confirmation, the method and / or device may also determine whether the current stimulation intensity level is valid. For example, if no substantial change in flow response occurs when a change in stimulation intensity is made during the inspiratory phase of a single respiratory cycle, the method and / or device may determine that the stimulation intensity is warranted to have not changed, or that the stimulation intensity may be slightly reduced without reducing therapeutic efficacy. However, if a substantial change in flow response occurs when a change in stimulation intensity is made during the inspiratory phase of a single respiratory cycle, the method and / or device may determine that a change in stimulation intensity (e.g., an increase) is warranted. Such determination and / or implementation of the stimulation intensity change may be implemented by at least the stimulation portion 2900 of the care engine 2500. Some such sensing of the flow response (e.g., aspects of the sensed respiratory waveform) may be implemented by the sensing portion 2000 ( Figure 24 ) through the sensing portion 2510 of the care engine 2500 ( Figure 27A ), and / or through the respiratory portion 2580 of the care engine 2500 ( Figure 27A ) to implement.

[0288] In some such examples of the stimulation portion 2900 of evaluating whether a stimulation therapy is effective (based on the flow response relative to the inspiratory phase from cycle to cycle within a single respiratory cycle), some example methods may include determining, during the inspiratory phase of a single respiratory cycle, whether a change (e.g., a substantial change) in the flow response occurs when stimulation is fully terminated or when stimulation is initiated (e.g., when no stimulation has previously occurred), e.g., Figure 27E As shown in 3610.

[0289] In combination Figure 27D and / or Figure 27E In some examples, based on the determined flow response (e.g., whether there is flow restriction), the stimulation portion 2900 may be configured to stimulate the flow of fluid by at least Figure 27A Changes in stimulation intensity are implemented using closed-loop parameters 2910 and / or automatic titration parameters 2920 in the .

[0290] In some instances, Figure 27AThe care engine 2500 in may include an initial use functionality 3100 that may automatically enhance the determination of sleep-wake states in certain instances. In some such instances, through the initial use functionality 3100, the method and / or device for SDB care may omit a manual training cycle and, in effect, automatically "normalize" the use of the method and / or device for a particular patient. For example, in some instances, the determination of sleep-wake states may begin with default parameters, or may begin with parameters collected when the SDB care device is implanted in the patient. In some instances, the determination of sleep-wake states may initially be performed without default parameters. In some such instances, when wakefulness is detected, the sensing portion 2000 ( Figure 24 ) and / or Care Engine 2500( Figure 27A ) can collect respiratory information, movement information, and / or posture information related to arousal, which in turn can allow for more sensitive sleep detection when determining sleep-wake states. In some examples, detecting arousal can include detecting whole-body movements, such as, but not limited to, walking, swallowing, trunk movements, etc. In some examples, a gravity vector is established when the SDB care device is implanted.

[0291] With this in mind, such automatic normalization may include omitting the use of absolute thresholds and instead performing sleep-wake state determination (e.g., detection of the onset of sleep) based on percentage changes in sensed values, according to the initial use functionality 3100. Furthermore, in some examples, sensing of various physiological phenomena (e.g., respiratory, cardiac, etc.) may be used to determine the highest or lowest values ​​for such physiological phenomena, with the thresholds then adjusted accordingly using such end-of-range values.

[0292] It should be understood that Figure 27A The various parameters, functions, parts, etc. shown and described are not limited to Figure 27A The specific groupings, relationships, etc. shown in Figure 27A In addition, it should be understood that Figure 27A The care engine 2500 (or portion thereof) in Figure 27A Some (ie, not all) of the parts, elements, parameters, etc. shown in FIG.

[0293] Reference at least Figure 27A With reference to the care engine 2500 and example methods and / or apparatus described in the present disclosure, it should be understood that such engines, methods and / or apparatus (and components, portions, etc. thereof) for determining sleep-wake states may also be used to quantify activity levels and assess related health parameters.

[0294] In some instances, such as Figure 27BAs shown in FIG, a cardiac morphology diagram 3500 schematically represents various cardiac morphology features, at least some of which may be used to determine sleep-wake states according to at least some of the example methods and / or example devices of the present disclosure, such as, but not limited to, cardiac morphology features previously described in at least Figures 10 to 13 、 Figure 24 (e.g., 2020, 2036) and Figure 27A (e.g., those described in 2520, 2600). For example, Figure 3500 provides Figure 27A A graphical representation of at least some of the various parameters and characteristics (e.g., onset, peak, contraction, relaxation, valve closure, etc.) of at least the heart portion 2600 of the care engine 2500 in FIG. Figure 27B As shown in , the cardiac morphology diagram 3500 includes an aortic pressure signal 3502, an atrial pressure signal 3504, a ventricular pressure signal 3506, a ventricular volume signal 3510, an ECG signal 3520, and a phonocardiogram (e.g., heart sound) signal 3530. It should be understood that the cardiac morphology diagram 3500 can also include a ballistocardiogram or shock cardiograph. These signals are plotted relative to time to show various morphological features, movements, etc. of the cardiac cycle with each beat of the heart. For example, the diagram 3500 depicts time points along the atrial pressure signal 3504 and the ventricular pressure signal 3506 at which the opening and closing of different valves can be identified, such as AV valve closed 3550, AV valve open 3552, semilunar valve open 3554, semilunar valve closed 3556. As shown in combination with at least the heart portion 2600 ( Figure 27A ), these valve openings and closings can be used as a benchmark or parameter for tracking heart rate (and heart rate variability), and sleep-wake state determination can be performed from the benchmark or parameter. Graph 3500 also depicts time points along the atrial pressure signal 3502 and the ventricular pressure signal 3504, at which various parts of the cardiac cycle, such as ventricular contraction 3560 and ventricular relaxation 3562, can be identified, and sleep-wake state determination can be performed from the time points when tracking such contractions as reliable identifiers of heart rate (and / or heart rate variability). Similarly, graph 3500 depicts heart sounds S1, S2, S3, etc., whose characteristics can correspond to ventricular contraction and relaxation ( Figure 27A 3560, 3562), valve opening and closing ( Figure 27A 3550, 3552, 2554, 3556) etc., as shown in FIG3500.

[0295] Figure 28Ais a block diagram schematically illustrating an example control portion 4000. In some examples, the control portion 4000 provides an 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, such as in conjunction with Figures 1 to 27B and Figures 28B to 36 Described in the examples of this disclosure.

[0296] In some instances, the control portion 4000 includes a controller 4002 and a memory 4010. Generally speaking, the controller 4002 of the control portion 4000 includes at least one processor 4004 and associated memory. The controller 4002 can be electrically coupled to the memory 4010 and communicate with the memory 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 in the examples of the present disclosure. In some instances, these generated control signals include, but are not limited to, using instructions 4011 and / or information 4012 stored in the memory 4010 to at least determine the patient's sleep-wake state, including a specific sleep stage. Such sleep-wake determination may include a portion of guiding and managing treatment for sleep-disordered breathing (e.g., obstructive sleep apnea, hypopnea, and / or central sleep apnea), wherein such sleep-wake determination also includes sensing physiological information (including but not limited to brain electrical activity, respiratory information, heart rate) and / or monitoring sleep-disordered breathing, etc., as in the implementation of the present disclosure in conjunction with Figures 1 to 27E and Figures 28B to 36 described. In some cases, the controller 4002 or control portion 4000 may sometimes be referred to as being programmed to perform the above-described actions, functions, etc., such that the controller 4002, control portion 4000, and any associated processor may sometimes be referred to as a special-purpose computer, control portion, controller, or processor. In some instances, at least some of the stored instructions 4011 are implemented as or may be referred to as a care engine, sensing engine, monitoring engine, and / or treatment engine. In some instances, at least some of the stored instructions 4011 and / or information 4012 may form at least a portion of a care engine, sensing engine, monitoring engine, and / or treatment engine, and / or may be referred to as a care engine, sensing engine, monitoring engine, and / or treatment engine.

[0297] In response to or based on a user interface (e.g., Figure 29 4040 in the user interface 4040) and / or commands received via machine-readable instructions, the controller 4002 generates control signals as described above in accordance with at least some examples of the present disclosure. In some examples, the controller 4002 is included in a general-purpose computing device, while in some examples, the controller 4002 incorporates or is 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, as described in examples of the present disclosure.

[0298] For the purposes of this application, with respect to controller 4002, the term "processor" shall mean a processor (or processing resource) currently developed or developed in the future that executes machine-readable instructions contained in memory. In some examples, executing machine-readable instructions (e.g., those provided by memory 4010 of control portion 4000) causes the processor to perform the actions described above, such as operating controller 4002 to implement sensing, monitoring, determination, treatment, etc., as generally described in at least some examples of the present disclosure (or consistent with at least some examples of the present disclosure). The machine-readable instructions can be loaded into random access memory (RAM) for execution by the processor from their storage location in read-only memory (ROM), mass storage, or some other persistent storage device (e.g., non-transitory tangible media or non-volatile tangible media), as represented by memory 4010. In some examples, the machine-readable instructions may include a series of instructions, a processor-executable machine learning model, etc. In some examples, memory 4010 includes a computer-readable tangible medium that provides non-volatile storage of machine-readable instructions that can be executed by the processes of controller 4002. In some examples, computer-readable tangible media may sometimes be referred to as and / or include at least a portion of a computer program product. In other examples, hard-wired circuitry can be used in place of or in combination with machine-readable instructions to implement the described functionality. For example, controller 4002 can be included as part of at least one application-specific integrated circuit (ASIC), at least one field-programmable gate array (FPGA), and the like. In at least some examples, controller 4002 is not limited to any specific combination of hardware circuitry and machine-readable instructions, nor is it limited to any specific source of machine-readable instructions executed by controller 3002.

[0299] In some examples, control portion 4000 can be implemented entirely within or by a standalone device.

[0300] In some examples, the control portion 4000 may be partially implemented in one of a sensing device, a monitoring device, a stimulation device, an apnea treatment device (or a portion thereof), etc., and partially implemented in a computing resource that is separate and independent from the apnea treatment device (or a portion thereof) but in communication with the apnea treatment device (or a portion thereof). For example, in some examples, the control portion 4000 may be implemented by a server accessible via a cloud and / or other network path. In some examples, the control portion 4000 may be distributed or shared among multiple devices or resources, such as a server, an apnea treatment device (or a portion thereof), and / or a user interface.

[0301] In some examples, the control portion 4000 includes Figure 29 4040 shown in and / or communicates with the user interface.

[0302] Figure 28B is a schematic diagram showing a control portion 4000 ( Figure 28A ). ) is a diagram of at least some example implementations of a control portion 4020 of a device. In some instances, the control portion 4020 is entirely within or implemented by an IPG component 4025, which has at least some substantially the same features and properties as the pulse generators (e.g., power / control elements, micro stimulators) as previously described in this disclosure. In some instances, the control portion 4020 is entirely within or implemented by a remote control 4030 (e.g., a programmer) that is external to the patient's body, such as a patient control 4032 and / or a physician control 4034. In some instances, the control portion 4000 is partially implemented in the IPG component 4025 and partially implemented in the remote control 4030 (at least one of the patient control 4032 and the physician control 4034).

[0303] Figure 29 4040 is a block diagram schematically illustrating 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, and the treatment system can be at least partially controlled and / or monitored by the device. The external device hosting the user interface 4040 can be a patient remote device (e.g., Figure 28B 4032 in), doctor remote device (e.g., Figure 28B In some instances, user interface 4040 includes a user interface or other display that provides access to the patient information in conjunction with the patient information. Figures 1 to 364040. The user interface 4040 may be a graphical user interface (GUI) that provides a graphical user interface (GUI) and may include a display 4044 and an input 4042.

[0304] Figure 30 is a block diagram 4300 schematically illustrating some example embodiments in which an implantable device (IMD) 4310 (e.g., an implantable pulse generator and / or an implantable sensor monitor) may wirelessly communicate with an external device external to a patient. Figure 30 , in some examples, IMD 4310 can communicate with at least one of a patient application 4330 on a mobile device 4320, a patient remote control 4340, a clinician programmer 4350, and a patient management tool 4336. Patient management tool 4336 can be implemented via a cloud-based portal 4362, patient application 4330, and / or patient remote control 4340. These communication arrangements enable IMD 4310 to communicate, display, manage, etc., sleep / wake data for patient management, among other types of data, and to allow for adjustment of detection algorithms if / when desired.

[0305] It should be understood that at least some of the various devices / elements 4320 , 4340 , 4350 , patient management tool 4360 may also be in communication with each other, with or without communication with implantable device 4310 .

[0306] Figure 31A is a diagram schematically illustrating an example user interface 5000, which may include Figure 29 In just one example embodiment of many example embodiments of the user interface 4040 in FIG. 5 , the user interface 5000 may be displayed in whole or in part on the Figure 30 43. However, from the combination of at least Figure 29 It will be understood from the description that the user interface 4040 may include or display in addition to (or in addition to) Figure 31A Many other parameters, functions, relationships, sensed information, etc. (from examples of the present disclosure) of the information shown in user interface 5000 in.

[0307] In some instances, a user interface (e.g., Figure 294040) can display only a portion or portions of the user interface 5000 on a single display or a series of displays. In some examples, the user interface (e.g., 4040) can display Figure 31A All parts of user interface 5000 shown in .

[0308] In some examples, the user interface 5000 can include a nighttime utilization portion 5010. In some examples, the nighttime utilization portion 5010 includes an awakening parameter 5050, a selective start parameter 5060, a selective stop parameter 5062, an automatic start parameter 5070, an automatic stop parameter 5072, an on parameter 5075, a pause (or non-use) parameter 5080, and / or a not applicable parameter 5090. In some examples, each of the various parameters 5050-5090 is associated with a display element having a unique shape, color, pattern, and / or size, etc., to enable differentiation between the different parameters. It should be understood that the example methods and / or apparatus relating to the user interface 5000 are not limited to the examples provided for illustrative purposes. Figure 31A The specific shape, color, size, etc. of the display elements shown in FIG. For example, the awakening parameter 5050 is represented by a grid pattern in a columnar shape, such as Figure 31A , extends over a portion of the 24-hour daily cycle corresponding to the period during which the patient is awake. Figure 31A As shown in , the wakefulness parameter 5050 represents the number of hours in a 24-hour cycle that the patient is awake. Similarly, the other parameters 5060, 5062, 5070, 5072, 5075, 5080, 5090 are represented by their own unique shapes, colors and / or patterns, etc.

[0309] The automatic start parameter 5070 (striped triangle) indicates the point in time at which a treatment cycle is automatically initiated, which in turn may be driven in some instances by automatically determining sleep-wake state (e.g., detecting sleep). Following such automatic start, the on parameter 5075 (solid colored elongated rectangle) indicates the number of hours (within a 24-hour period) that the treatment cycle is extended and stimulation may be delivered. The pause parameter 5080 (white bar) indicates the time period during which the treatment cycle is paused or interrupted, such as when the patient temporarily wakes up to use the restroom, eat a snack, etc. In some instances, such as Figure 31A As shown in FIG, after such a pause (5080), the treatment cycle resumes in the on mode (5075). The treatment cycle may be resumed by automatic initiation upon detection of the patient falling asleep, or may be resumed by the patient re-initiating the treatment cycle via a remote control or other means (e.g., physical control).

[0310] In some examples, pause parameter 5080 may be graphically represented by one type of indicator (e.g., color, pattern, shape, size) for automatic pause and another different type of indicator (e.g., color, pattern, shape, size, etc.) for optional pause.

[0311] In some instances, the end of a treatment cycle may be indicated by an auto-stop parameter 5072 (short black bar).

[0312] In some instances, instead of automatically initiating (e.g., starting) a treatment cycle, in some cases, a patient can selectively initiate a treatment cycle, e.g., by Figure 31A The selective start parameters 5060 (speckled bars) are shown in FIG.

[0313] In some instances, instead of automatic termination (e.g., discontinuation), in some cases, a patient may elect to terminate a treatment cycle, e.g., by Figure 31A The selective stopping parameters 5062 (speckled isosceles triangles) are shown in FIG.

[0314] In some instances, such as Figure 31A As shown in , the SDB care device may also be unable to enter a treatment cycle at the discretion of the patient, clinician, etc. In such cases, the not applicable parameter 5090 can fill the entire 24 hour period.

[0315] In some instances, one or more parameters may be presented in a changing shape, pattern, etc. Figure 31A In the example nighttime utilization portion 5010, changes in the number, value, or other aspects of the sleep quality parameters are indicated. In one non-limiting example, for example, when the SDB care device is active in a short treatment cycle A at night, but before the patient falls asleep, the patient enters the wake state B (e.g., 19:00 to midnight (00:00)) without activating the treatment cycle as shown at C (white bar from midnight to 4:00 a.m.). This lack of treatment can be attributed to manual control or other reasons. As shown on Wednesday, November 30, as represented by the narrow column (grid pattern) D representing the wake state, the patient exhibited one or more sleep quality parameters that indicated poor sleep quality during the previous nighttime period (Tuesday, November 29), where no treatment cycle was used from midnight to 4:00 a.m., and in addition, the patient did not sleep for a long time. In some such instances, at least some of the sleep quality parameters exhibited by the patient during his or her wake state (e.g., Wednesday, November 30) can be detected by the sensing portion 2000 ( Figure 24 ) sensed and / or by the care engine 2500 ( Figure 27A ) is tracked and can be displayed according to the sleep quality from wakefulness portion 5100 of the user interface 5000.

[0316] like Figure 31A As shown in , in some instances, the sleep quality due to awakenings portion 5100 may include a date parameter 5102 (e.g., day of the month), an average parameter 5104 (e.g., average number of hours of sleep), a trend parameter 5106, a heart rate (HR) parameter 5108, a heart rate variability (HRV) parameter 5110, a body movement parameter 5112, a blood pressure parameter 5114, and / or other parameters 5116 (e.g., posture, position, etc.).

[0317] In some examples, the user interface 5000 may include a sleep quality from sleep section 5200 that may track and display substantially the same parameters as the sleep quality from wakefulness section (e.g., 5202-5216), except that the parameters are sensed during sleep periods rather than wake periods. Additionally, the sleep quality from sleep section 5200 includes an AHI parameter 5218 for tracking the hypopnea-apnea index (AHI).

[0318] Where a patient consistently receives therapeutic treatment, at least some of the parameters of the wakefulness-derived sleep quality parameters portion 5100 can be used to better characterize wake states, and at least some of the parameters of the wakefulness-derived sleep quality parameters portion 5100 can be used to better characterize sleep states. Thus, comparison of wakefulness-derived sleep quality parameters with sleep-derived sleep quality parameters (according to portion 5100) can be used to enhance sleep-wake state determination (e.g., detecting sleep, detecting wakefulness) by understanding how a particular patient behaves physiologically during intrinsic wake states and during sleep states across a range of conditions, days, etc.

[0319] like Figure 31AAs also shown in FIG, user interface 5000 may include a user metrics portion 5300 that, in at least some instances, may display and / or otherwise communicate information to a user regarding automatic and / or selective treatments. For example, in some instances, user metrics portion 5300 may include an automatic element 5310 to track and / or report instances where treatments are automatically started (5312), automatically stopped (5314), and automatically paused (5316). In some instances, these automatic implementations (e.g., starts, stops, pauses) are triggered or caused by automatic determination of sleep-wake states as described herein. For example, in some instances, user metrics portion 5300 may include a selective element 5320 to track and / or report instances where treatments are selectively started (5322), selectively stopped (5324), and selectively paused (5326). In some instances, these selective implementations (e.g., starts, stops, pauses) are caused by a user operating a remote control, an application on a mobile device, or the like. Each corresponding automatic and selective function (e.g., start, stop, pause) can be tracked and / or reported based on observable patterns (5330), averages (5340), and trends (5350).

[0320] By tracking and / or reporting the absolute and / or relative amounts of automatic and selective implementation via the usage metrics portion 5300 and / or other displayable / reportable formats, a clinician or patient can determine the relative effectiveness of automatic determination of sleep-wake states according to examples of the present disclosure. Figure 31A 5342 in ), which in turn can be used to determine the relative effectiveness of SDB care (e.g., electrical stimulation for obstructive sleep apnea, etc.).

[0321] It should be understood that Figure 31A The various parameters, functions, parts, etc. shown and described are not limited to Figure 31A The specific groupings, relationships, etc. shown in Figure 31A In addition, it should be understood that Figure 31A The user interface (or part of it) in Figure 31A Only some (ie, not all) of the parts, elements, parameters, etc. shown in the drawings are implemented.

[0322] like Figure 31B As shown in , in some examples, Figure 31A The user interface 5000 may also display and / or report the use of ramp-on, ramp-to-and-off pause therapy, and / or ramp-to-end stimulation within a given therapy cycle. Figure 31B As shown in the schematic representation in FIG, the daily display portion 5400 may include a display similar to Figure 31A, such as the various graphical identifiers shown in , such as the wake-up period 5050, the auto-start (eg, auto-start) instance 5070, the on-period 5075, etc. It should be understood that Figure 31B The display portion 5400 may include at least some Figure 31A 5030 shown in FIG, and / or at least some of the same features and attributes as the everyday display portion 5030 ( Figure 31B ) Essentially the same features and properties can be found in Figure 31A is implemented in or as at least one of the daily display parts 5030.

[0323] In some example methods, at least some of the starting, stopping, and pausing of stimulation within a treatment cycle may be implemented in a ramped manner, wherein Figure 31B These implementations are schematically represented by the display portion 5400 in FIG. For example, the automatic start of stimulation may include, as shown at 5070 (also shown at FIG. Figure 31A 5406 ). It will be appreciated that the representation of a ramped increase (from zero) in the stimulation intensity of the target is represented by a triangle ramp symbol (shown in FIG. 5410 ). This representation immediately indicates to the observer the ramping manner in which the stimulation intensity is to be implemented. The ramping increase can occur at the beginning of a treatment cycle (e.g., 5405 ). Similarly, the triangle ramp symbol 5410 represents a ramping decrease in the 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 to be paused (e.g., at 5080 ). It will be appreciated that the representation of a ramped increase or decrease in stimulation intensity can be implemented by shapes other than triangles.

[0324] Gradual ramping of stimulation therapy on or off can enhance patient comfort by avoiding abrupt initiation, pause, or cessation of stimulation therapy.Among other features, ramping can increase the likelihood of patient compliance and appreciation for SDB care.

[0325] In some instances, with at least Figures 31A to 31B At least some of the features and properties of the methods and / or apparatuses shown may be combined with the following Figures 31C to 31H In some instances, the method may be implemented in conjunction with at least some of the features and attributes of the example methods described. Figures 31C to 31H The method described can be performed by removing at least Figures 31A to 31B The present invention may be implemented with devices and elements other than those shown in FIG.

[0326] It should be understood that Figure 31A 、 Figure 31B Schematically illustrates at least some aspects of a patient's experience with treating a patient's sleep apnea, the operation of an apparatus for treating a patient's sleep apnea, and / or a method for treating a patient's sleep apnea. Figures 31A to 31BAt least some aspects of the invention are schematically represented by way of example, example methods, such as Figure 31C As shown at 5500 in FIG, the example method includes automatically taking action when the probability of sleep determined from the sleep-wake state exceeds a sleep detection threshold or the probability of wakefulness determined from the sleep-wake state exceeds a wakefulness detection threshold. In some examples, such as in Figure 31D As shown at 5510 in FIG, automatically taking action includes at least one of automatically starting a stimulation therapy cycle and automatically stopping a stimulation therapy cycle. In some such instances, the term "non-sleep" may correspond to a probability of sleep remaining below a sleep detection threshold, and in some such instances, the term "non-wakefulness" may correspond to a probability of wakefulness remaining below a wakefulness detection threshold.

[0327] In some of these instances ( Figure 31D 5510 in ), the example method may also include, as in Figure 31E As shown at 5520 in , receiving input for selectively starting a treatment cycle and / or selectively stopping a treatment cycle; and when the input for selective start is received, the suspension is automatically started, and when the input for selective stop is received, the suspension is automatically terminated.

[0328] In some instances, such as Figure 31F As shown at 5530 in Figures 31C to 31E The method may also include tracking information of at least one of a pattern, a trend, and an average of at least one of automatic start, automatic stop, selective start, and selective stop for a plurality of nighttime utilization periods. Figures 31A to 31B Other (or additional) nighttime utilization parameters described may be based on Figure 31F Method 5530 in is used for tracking.

[0329] In some instances, such as Figure 31G As shown at 5540 in , the method includes determining a quantitative effectiveness of the automatic sleep-wake determination by determining a first ratio of automatic starts to selective starts and / or a second ratio of automatic stops to selective stops based on at least the tracked information.

[0330] In some instances, such as Figure 31H As shown at 5550 in the method, the method includes displaying, via a graphical user interface, a plurality of separate nighttime utilization cycles, each nighttime utilization cycle symbolically illustrating at least one of the following with respect to a treatment cycle within each nighttime utilization cycle: automatic start; automatic stop; selective start; and selective stop. Figures 31A to 31B It is obvious that Figure 31H At least some features of the method at 5550 may be achieved by combining Figures 31A to 31BA graphical user interface is shown and described for implementation.

[0331] Figure 32 is a diagram schematically illustrating a timeline 6010 of sleep-wake related events according to an example method 6000 for sleep-wake determination, such as may occur during sleep-disordered breathing (SDB) care (e.g., monitoring, diagnosis, treatment, etc.). In some instances, example SDB care may include at least some Figures 1 to 31H and Figures 33 to 36 The described example SDB care methods and / or devices (including sleep-wake detection) have substantially the same features and attributes.

[0332] like Figure 32 As shown in , timeline 6010 includes a series of wake and sleep cycles, where a wake cycle 6020 occurs just before a first sleep stage cycle 6040 (e.g., stage 1). Figure 32 The wake cycle 6020 in may represent the end portion of a wake cycle extending from the end of the previous night's sleep, or may represent another wake cycle.

[0333] As also indicated by indicator 6035, a true physiological transition occurs between the wake period 6020 and the first sleep stage 6040, and indicator 6043 indicates sleep detection according to an example of the present disclosure. Figure 32 As shown in , the detection of sleep (6043) can be performed just after the physiological transition 6035.

[0334] In some examples, detection of sleep 6043 can trigger a delay period 6045 before therapy (e.g., electrical stimulation) begins. In some such examples, the duration of the delay typically corresponds to an amount of time sufficient for the patient to experience sufficient deep sleep so that the patient is not awakened by the start of stimulation. Furthermore, in some examples, once stimulation begins, it can be implemented in a ramping manner (6046) with an initial lower stimulation intensity that gradually increases until a target stimulation intensity (6047) is reached to therapeutically provide electrical stimulation to tissues associated with upper airway patency.

[0335] As previously noted in this disclosure, Figure 32 At least some example implementations of method 6000 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.

[0336] like Figure 32 As also shown in , once the target stimulation intensity is reached, it can be maintained throughout the treatment cycle.

[0337] In some instances, the target stimulation intensity can be automatically adjusted (e.g., auto-titrated) during the treatment cycle. In some such instances, the target stimulation intensity can be automatically adjusted (e.g., auto-titrated) during the treatment cycle. Figure 27A Automatic adjustment of the target stimulation intensity may be implemented using substantially the same features and / or properties as described in automatic titration parameters 2920 in FIG.

[0338] like Figure 32 As also shown in FIG, after a certain period of time (which may vary from night to night), the patient may sometimes experience a wake period 6060 during a treatment cycle that interrupts a sleep stage (e.g., in this example, the second sleep stage (S2) 6050). The example method 6000 detects an awakening (6062) before the patient resumes sleeping (e.g., represented by a sleep stage 6070 and a corresponding transition 6065 between the awakening period 6060 and the sleep stage 6070), which may last for a certain period of time (W1).

[0339] In some instances, the method 600 may completely suspend stimulation during the wake period 6060, or alternatively, in some instances, the method 6000 may implement reduced therapy 6064 during the wake period 6060 in anticipation of the patient continuing to sleep and resuming full stimulation therapy. In some such instances, the reduced therapy at 6064 may include providing stimulation at a functional threshold (FT), corresponding to a minimum amplitude at which stimulation will cause the tongue to at least partially protrude beyond the lower teeth and a therapeutic outcome (e.g., a reduction in apnea) may be achieved. However, in some such instances, the reduced therapy at 6064 may include providing stimulation at a sensory threshold (ST), which relates to a stimulation intensity that is lower than the stimulation intensity at which the functional threshold (FT) is achieved. The sensory threshold (ST) may correspond to a minimum amplitude at which the patient can feel the stimulation.

[0340] As in Figure 32 As shown at 6072 in FIG, treatment may be automatically resumed. It should be understood that in at least some instances, the resumption of treatment 6072 may include a change in the treatment schedule previously discussed. Figure 32 Indicators 6043, 6046, 6047 in the initiation of therapy described have substantially the same features and attributes, including detection 6043 of sleep to determine sleep-wake state according to at least some examples of the present disclosure.

[0341] In some examples, generally speaking, the start of the first sleep stage 6040 generally corresponds to the start of a treatment period during which the patient may receive treatment for sleep-disordered breathing and / or the method (and / or device) may monitor or diagnose sleep-disordered breathing.

[0342] Figure 33is a block diagram schematically illustrating an example arrangement 7000 including an example method (and / or example apparatus) for determining sleep-wake states. In some instances, the method 7000 may be combined with at least one of the previously described methods. Figure 27A The probability function 3200 of the care engine 2500 (including the machine learning parameters 3230), Figure 26 Processing section 2600 and / or Figure 21 In some examples, at least some aspects of the example arrangement 7000 may be implemented more generally by controlling portion 4000 ( Figure 28A ) implementation, the control portion is, for example but not limited to, an example implementation of at least some of the stored executable instructions 4011 and / or 4012 ( Figure 28A ).

[0343] Further references Figure 33 , an example single sensor 2010 (or type of single sensor) may include an accelerometer that can generate a signal of sensed physiological information, from which multiple physiological phenomena can be extracted, such as but not limited to heart, breathing, movement, activity, etc.

[0344] like Figure 33 As shown in , method 7000 includes sensing a physiological phenomenon via a sensor 7010 (at 7005) to generate a signal 7012, which is processed (7020) to separate the signal 7012 into different / individual components, for example, at 7062A-7062N.

[0345] In some instances, each individual component 7062A-7062N may sometimes be referred to as a sleep-wake determination parameter, such as, but not limited to, the previously combined Figure 21 It should be understood that throughout Figure 33 , the identifier "N" represents an "nth" signal component, e.g., an unspecified total number of signal components, rather than a specific limitation on the number of components into which the signal 7012 may be segmented. As previously noted, at least some example signal components may include cardiac, respiratory, motion, activity, posture, etc.

[0346] like Figure 337062A-N, 7072A-N, 7082A-N, can sometimes be referred to as a processing path or processing portion 7050. The segmented signals 7062A-7062N can be referred to as a signal component group 7060 of the signal 7012, the individual feature enhancements 7072A-7072N can be collectively referred to as a feature enhancement group 7070, and / or the individual sleep-wake determinations 7072A-7072N can be referred to as a sleep-wake determination group 7070.

[0347] like Figure 33 As also shown in FIG, each respective signal component 7062A-7062N (e.g., each sleep-wake determination parameter) is further processed to produce a respective feature enhancement 7072A-7072N (group 7070), which in turn can be used and / or further processed to produce a respective sleep-wake determination 7082A-7082N (group 7080). In some instances, any one of the various sleep-wake determinations 7082A-7082N may be sufficient to effectively determine a sleep-wake state. However, in some instances, at least two sleep-wake determinations (e.g., 7802A, 7802C) may be considered together as part of a combined sleep-wake determination at 7300.

[0348] As also shown at 7350, in some instances, the patient and / or clinician can provide information (e.g., input) regarding the patient's experience with sleep and wakefulness, and the transitions therebetween, which can be used to enhance or support the combined sleep-wake determination. In some instances, this input can be provided by providing input to the various sleep-wake determination parameters described above (e.g., Figure 33 7062A-7062N, Figure 20A ) apply different weights (e.g., Figure 27A 3240) to implement or express.

[0349] In some instances, as Figure 33 Alternatively or in addition to the processing path 7050 shown in FIG, method 7000 may include further processing at 7200 by pattern matching. In some such instances, pattern matching includes accessing stored signal patterns of sleep behavior (e.g., sleep behavior patterns), stored signal patterns of wake behavior (e.g., wake patterns), and / or stored signal sleep-related behavior. In some instances, the stored signal patterns, etc., may be stored in memory 4010 ( Figure 28A ) and / or in other accessible databases external to the patient.

[0350] like Figure 33As also shown in FIG, the output of the stored patterns from pattern matching (7200) can be submitted to and used by deep learning component 7250 to analyze sensed physiological phenomena (e.g., respiratory signals, cardiac signals, etc.) to determine patterns indicative of sleep states (e.g., onset, offset, various sleep stages) and / or patterns indicative of wakefulness (e.g., onset, offset, various sleep stages). In some examples, at least a portion of this analysis can include comparing stored signal patterns with current or recent signal patterns.

[0351] In some examples, the deep learning component 7250 may include a convolutional neural network, a deep neural network, a deep neural learning, etc. It should be understood that in some examples, the deep learning component 7250 may be implemented by other forms of artificial intelligence tools. The deep learning component 7250 may be implemented as Figure 27A as part of the machine learning parameters 3230 in or implemented in a manner complementary to the machine learning parameters.

[0352] The output of deep learning element 7250 is provided to or as a comprehensive sleep-wake determination at 7300. It should be understood that in some instances, the output of deep learning element 7250 may be the sole basis for implementing comprehensive sleep-wake determination 7300. However, in some instances, the output of deep learning element 7250 may comprise only one input in comprehensive sleep-wake determination 7300. In some such instances, the other inputs may include one of sleep-wake determination results 7082A-7082N and / or may include patient / clinician input 7350.

[0353] In some instances, such as Figure 33 The deep learning element 7250 represented in FIG may include a trained machine learning model (e.g., a trained deep learning model) that may be trained (i.e., constructed) prior to operation of the example arrangement 7000. Figure 34 As also shown in the example arrangements (e.g., example methods or apparatuses) in some examples, training can be performed at least in part by an external resource 7410 external to the patient's body and external to the implantable medical device 7420. The implantable medical device 7420 may include an implantable sensor (e.g., Figure 33 7010) and control part 4000 ( Figure 28A ), as well as other components, features, etc. After such training, the trained deep learning model can be imported into the implantable medical device 7420 for use, for example, by using Figure 33 The deep learning element 7250 in the example arrangement 7000 in FIG. 7A determines sleep-wake states.

[0354] In some instances, external resource 7410 ( Figure 34) may include computing resources 7414 sized and scaled to perform deep learning. In some examples, external resources 7410 may include data storage 7412, such as, but not limited to, a large dataset storing sleep information for many patients, which may include acceleration signal component information related to various non-physiological parameters and physiological parameters (such as, but not limited to, cardiac information, respiratory information, motion / activity information, posture information, etc.). In some examples, the stored sleep-related data may be patient-specific, and the trained deep learning model may be imported into the patient, such as into or as element 7250 of example arrangement 7000 within an implantable medical device.

[0355] With this in mind, in some instances, Figure 34 The example arrangement (e.g., method and / or apparatus) 7500 in FIG. 7 trains (i.e., constructs) a deep learning element 7250 through an external resource 7410. Figure 34 As shown in , by at least an implanted accelerometer (e.g., Figure 33 7010) and known outputs 7540 are provided to a trainable machine learning model 7530. In some examples, known outputs 7540 may include externally determined sleep-wake states, which may include any number of externally measurable physiological parameters for determining sleep-wake states, such as, but not limited to, any (or combination) of EEG, EOG, EMG, ECG, cardiac information, respiratory information, motion / activity, posture, etc.

[0356] like Figure 34 , in some examples, at least some of the known inputs (obtained via an implanted accelerometer or other implantable sensor) may include cardiac information 7512, respiratory information 7514, motion / activity information 7516, posture information 7518, and / or other information. It should be understood that these inputs are merely examples, and that the known inputs (from implanted accelerometer signals or other implantable sensors) may include any sensed physiological information relevant to determining sleep-wake state.

[0357] By providing such known inputs (7510) and known outputs (7540) to the trainable machine learning model 7530, a trained machine learning model 7631 ( Figure 35 ). In some instances, only one or some of the known inputs 7510 may be used, while in some instances all of the known inputs 7510 may be used. As mentioned elsewhere, the trainable / trained machine learning models (7530, 7631) may include deep learning models.

[0358] Figure 35is a diagram schematically illustrating an example method 7600 (and / or example apparatus) for determining sleep-wake states using a trained machine learning model 7631, for example, using internal measurements from an implanted accelerometer in some instances. Figure 35 As shown in FIG, the current sensed input 7611 is fed into a trained machine learning model 7631, which then produces a determinable output 7641, such as a current sleep-wake state determination 7643, based on the current input 7611. In some examples, the current input 7611 corresponds to the same type and / or number of known inputs 7510 ( Figure 34 ). In some instances, only one or some of the current inputs 7611 may be used, while in some instances, all of the current inputs 7611 may be used.

[0359] As previously noted, once the trained machine learning model 7631 is obtained, it is imported into the control portion 4000 (and / or Figure 27A Care Engine 2500 in the ) and / or otherwise forming a part thereof, so that the trained machine learning model 7631 can be used as Figure 33 Deep learning element 7250 in exemplary arrangement 7000 of .

[0360] Further references Figure 33 In some examples, method / apparatus 7000 can include other input element 7260 through which other information sensed by an implantable sensor (e.g., within or connected to an implantable pulse generator) as internal input 7264 or otherwise received from an external sensor as external input 7262 can be used as part of the integrated sleep-wake determination 7300. In some examples, a first output 7266 of other input element 7260 can be combined with output 7090 from processing path 7050 as part of the integrated sleep-wake determination 3000. In some examples, a second output 7267 of other input element 7260 can be fed into deep learning element 7250 prior to or as part of performing the integrated sleep-wake determination 3000.

[0361] In some examples, external input 7262 may include, for example, input from a remote control 4340, an application 4330 on a mobile consumer device 4320, or the like (e.g., Figure 30 and Figure 28B ) associated with and / or with Figure 27AThe external sensor inputs associated with the remote parameters 3012, application parameters 3013, and physical parameters 3018 in FIG. The external sensor / inputs may include ambient light, movement / operation of a remote control, or movement / operation of an application / mobile consumer device, etc. Other inputs may include the aforementioned combination of at least Figures 20E to 20F The date and time, time zone, geographical latitude, etc. described in connection with the time parameters 3014 (input) for at least partially determining the sleep-wake state based on the detected sleep probability and / or wake probability Figure 27A ), boundary parameters 3016( Figure 27A )etc.

[0362] In some instances, Figure 33 Internal sensors / inputs 7264 in may include any other signals or inputs (e.g., stored information, commands, etc.) available through an implantable device (e.g., an IPG) that may supplement the information being processed (e.g., in pathways 7005 / 7012 / 7020 / 7200 / 7250 or pathways 7005 / 7012 / 7020 / 7050).

[0363] Although specific examples have been illustrated and described herein, various alternative and / or equivalent embodiments 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.

[0364] The present disclosure can adopt the following configurations.

[0365] Paragraph 1. A method comprising:

[0366] Sensing physiological information through implantable sensors; and

[0367] The sleep-wake state is determined by the sensed physiological information.

[0368] Paragraph 2. The method of paragraph 1, wherein determining the sleep-wake state comprises detecting the onset of sleep.

[0369] Paragraph 3. The method of paragraph 1, wherein sensing physiological information comprises:

[0370] Movement at at least one of the chest and the neck is sensed by the implantable sensor.

[0371] Paragraph 4. The method of Paragraph 3, wherein sensing motion comprises

[0372] Sense at least one of the following:

[0373] Respiratory information;

[0374] Cardiac information, including heart rate; and

[0375] Full body exercise.

[0376] Paragraph 5. The method of paragraph 4, wherein the respiratory information is based on sensing movement of the chest wall.

[0377] Paragraph 6. The method of paragraph 5, comprising:

[0378] Based on the magnitude of the sensed chest wall motion, active breathing, indicative of a wakeful state, is distinguished from passive breathing, indicative of a sleeping state.

[0379] Paragraph 7. The method of paragraph 4, comprising:

[0380] In addition to motion sensing, electrocardiogram sensing is also performed to further determine the cardiac information.

[0381] Paragraph 8. The method of paragraph 4, wherein sensing the motion comprises:

[0382] The sensing is performed by an implantable sensor implanted in the neck region.

[0383] Paragraph 9. The method of paragraph 8, wherein implanting the implantable sensor comprises:

[0384] A micro stimulator including the implantable sensor is implanted in the neck region.

[0385] Paragraph 10. The method of Paragraph 9, wherein performing the sensing comprises implementing the sensing via an accelerometer of the micro stimulator as at least one implantable sensor.

[0386] Paragraph 11. The method of paragraph 9, comprising:

[0387] In addition to motion sensing, additional cardiac information is obtained by performing electrocardiogram sensing via at least one first electrode on the outer housing of the micro stimulator and a second electrode spaced apart from the first electrode.

[0388] Paragraph 12. The method of paragraph 11, comprising:

[0389] The second electrode is disposed on the outer housing of the micro stimulator but spaced apart from the first electrode.

[0390] Paragraph 13. The method of paragraph 11, comprising:

[0391] Leads are arranged to extend from the housing of the micro stimulator and to support a second electrode spaced apart from the first electrode.

[0392] Paragraph 14. The method of Paragraph 4, wherein performing the sensing comprises implementing the sensing via an accelerometer as the at least one implantable sensor.

[0393] Paragraph 15. The method of paragraph 14, comprising:

[0394] The accelerometer is arranged as part of an implantable pulse generator.

[0395] Paragraph 16. The method of paragraph 4, comprising:

[0396] In addition to motion sensing, additional cardiac information is obtained by performing electrocardiographic sensing via at least one first electrode on the outer housing of the pulse generator and a second electrode spaced apart from the first electrode.

[0397] Paragraph 17. The method of paragraph 16, comprising:

[0398] The second electrode is disposed on the outer housing of the pulse generator but is spaced apart from the first electrode.

[0399] Paragraph 18. The method of paragraph 12, comprising:

[0400] Leads are arranged extending from the housing of the IPG and supporting a second electrode spaced apart from the first electrode.

[0401] Paragraph 19. The method of paragraph 4, wherein sensing the cardiac information comprises obtaining at least some ballistocardiograph information via the implantable sensor; and

[0402] The sleep-wake state is determined based on determining at least one of heart rate variability (HRV) and heart rate from the ballistocardiograph information.

[0403] Paragraph 20. The method of paragraph 19, comprising:

[0404] positioning the implantable sensor within an artery of the patient; and

[0405] The ballistocardiograph is derived from sensing the motion of at least the arterial wall caused by cardiac activity.

[0406] Paragraph 21. The method of paragraph 1, wherein sensing physiological information comprises sensing body movement, and wherein determining the sleep-wake state comprises:

[0407] Detect sleep when:

[0408] Date and time; and

[0409] Lack of sensed body movement for a predetermined period of time.

[0410] Paragraph 22. The method of paragraph 21, comprising:

[0411] When sleep is detected, delivering stimulation; and

[0412] Stimulation is maintained until at least one of the following occurs:

[0413] sensing physiological information indicative of arousal; and

[0414] Terminate stimulation manually.

[0415] Paragraph 23. The method of paragraph 22, wherein administering the stimulation comprises increasing the stimulation from a lower intensity level to a target intensity level.

[0416] Paragraph 24. The method of paragraph 22, wherein the sensed physiological information includes at least one of respiratory information and cardiac information, and the method further comprises:

[0417] Sleep is detected based on at least one of the sensed respiratory information and the sensed cardiac information.

[0418] Paragraph 25. The method of paragraph 24, wherein the sensed physiological information includes posture information, and the method further comprises:

[0419] Sleep is detected using at least one of the sensed posture information, the sensed breathing information, and the sensed heart information.

[0420] Paragraph 26. The method of paragraph 22, comprising:

[0421] Detection of sleep is performed without using posture information.

[0422] Paragraph 27. The method of paragraph 1, wherein sensing the physiological information comprises sensing variability in at least one of respiratory information including respiratory rate, cardiac information including heart rate, posture, and body movement; and

[0423] The determination of the sleep-wake state is performed by variability of at least one of corresponding sensed breathing rate, sensed cardiac information including heart rate, sensed posture, and sensed body movement.

[0424] Paragraph 28. The method of paragraph 27, wherein sensing the respiratory information comprises sensing at least one of a start of inspiration, an end of an expiratory pause, and a start of exhalation; and

[0425] The determination of the sleep-wake state is performed by at least one of a sensed start of inspiration, a sensed end of an expiratory pause, and a sensed start of exhalation.

[0426] Paragraph 29. The method of paragraph 27, wherein sensing the physiological information comprises sensing at least one of an exhalation cutoff and an end of an exhalation pause; and

[0427] The determination of the sleep-wake state is performed by at least one of a sensed exhalation cut-off or a sensed end of an exhalation pause.

[0428] Paragraph 30. The method of paragraph 27, wherein sensing the physiological information comprises sensing at least one of an inhalation to exhalation transition and an exhalation to inhalation transition; and

[0429] The determination of the sleep-wake state is performed by at least one of a sensed inhalation to exhalation transition and a sensed exhalation to inhalation transition.

[0430] Paragraph 31. The method of paragraph 27, wherein sensing the physiological information comprises sensing at least one of an inspiratory peak and an expiratory peak; and

[0431] The determination of the sleep-wake state is performed by at least one of a sensed inhalation peak and a sensed expiratory peak.

[0432] Paragraph 32. The method of paragraph 27, wherein sensing the physiological information as cardiac information comprises sensing at least one of atrial contraction or ventricular contraction; and

[0433] The determination of the sleep-wake state is performed by at least one of the sensed atrial contraction and the sensed ventricular contraction.

[0434] Paragraph 33. The method of paragraph 32, wherein sensing the physiological information comprises sensing at least one of a peak of the atrial contraction or a peak of the ventricular contraction; and

[0435] The determination of the sleep-wake state is performed by at least one of a peak value of a sensed atrial contraction and a peak value of a sensed ventricular contraction.

[0436] Paragraph 34. The method of paragraph 27, wherein sensing the physiological information as cardiac information comprises sensing both atrial contraction and ventricular contraction; and

[0437] The determination of the sleep-wake state is performed by both sensed atrial and ventricular contractions.

[0438] Paragraph 35. The method of Paragraph 27, wherein sensing the physiological information as cardiac information comprises sensing cardiac valve closure; and

[0439] The determination of the sleep-wake state is performed by means of the sensed closure of the heart valves.

[0440] Paragraph 36. The method according to paragraph 27,

[0441] The determination of the sleep-wake state is performed by both the sensed respiratory information and the sensed cardiac information as cardiac motion.

[0442] Paragraph 37. The method of paragraph 27, comprising:

[0443] Processes the sensed physiological information to distinguish between:

[0444] a first variability in respiratory information and cardiac information that is characteristic of sleep-disordered breathing; and

[0445] A second variability of the respiratory information and the cardiac information that is characteristic of sleep-disordered breathing, different from the first variability.

[0446] Paragraph 38. The method of paragraph 37, comprising:

[0447] A sleep-wake state is determined based on the respiratory information and a second variability of the cardiac information.

[0448] Paragraph 39. The method of paragraph 38, comprising:

[0449] The first variability in the respiratory information and the cardiac information is excluded from the determination of the sleep-wake state.

[0450] Paragraph 40. The method of paragraph 38, comprising:

[0451] Upon identifying the presence of the first variability, the sleep-wake determination is confirmed as a sleep state.

[0452] Paragraph 41. The method of paragraph 40, comprising:

[0453] Upon identifying an absence of the first variability, the sleep-wake determination is confirmed as a wake state.

[0454] Paragraph 42. The method of paragraph 1, wherein determining the sleep-wake state comprises identifying a wake state by identifying a variability in the sensed physiological information exceeding a selectable threshold of at least one of:

[0455] Respiratory rate;

[0456] Heart rate;

[0457] the inspiratory and / or expiratory portion of the respiratory cycle;

[0458] duration of the inspiratory portion;

[0459] the amplitude of the peak of the inhalation portion;

[0460] Duration of the peak of the inspiratory portion

[0461] duration of the exhalation portion;

[0462] physical exercise;

[0463] Posture; and

[0464] The amplitude of the peak of the exhaled portion.

[0465] Paragraph 43. The method of paragraph 42, wherein the body movement comprises neck movement.

[0466] Paragraph 44. The method of paragraph 42, wherein the body movement comprises chest movement.

[0467] Paragraph 45. The method of paragraph 1, wherein determining the sleep-wake state comprises identifying a sleep state by identifying that a variability of the sensed physiological information remains below a selectable threshold, the selectable threshold comprising a variability of at least one of:

[0468] Respiratory rate;

[0469] Heart rate;

[0470] the inspiratory and / or expiratory portion of the respiratory cycle;

[0471] duration of the inspiratory portion;

[0472] the amplitude of the peak of the inhalation portion;

[0473] Duration of the peak of the inspiratory portion

[0474] duration of the exhalation portion;

[0475] body movements, including at least one of chest movement and neck movement;

[0476] Posture; and

[0477] The amplitude of the peak of the exhaled portion.

[0478] Paragraph 46. The method of paragraph 1, wherein sensing physiological information comprises sensing motion at at least one of a chest and a neck with the implantable sensor, and wherein sensing motion comprises sensing at least one of:

[0479] Respiratory information;

[0480] Cardiac information, including heart rate; and

[0481] Full body exercise,

[0482] The method comprises:

[0483] The subsequent second motion information is compared with the first motion information.

[0484] Paragraph 47. The method of Paragraph 46, wherein each of the respective first motion information and second motion information includes at least one of sensed respiratory information, sensed cardiac information, and sensed whole-body motion.

[0485] Paragraph 48. The method of paragraph 46, comprising:

[0486] The sleep-wake state is determined when it is determined, according to 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.

[0487] Paragraph 49. The method of paragraph 46, wherein the subsequent second information comprises information obtained in a sensed second respiratory cycle, and the first information comprises information obtained in an immediately previous sensed first respiratory cycle.

[0488] Paragraph 50. The method of Paragraph 49, wherein each respective first and second breathing cycles comprises at least about 30 seconds.

[0489] Paragraph 51. The method according to Paragraph 49 includes determining the subsequent second motion information based on the second average value of the motion information in the breathing cycle of the sensed second breathing cycle, and determining the first motion information based on the first average value of the motion information in the breathing cycle of the first breathing cycle.

[0490] Paragraph 52. The method of paragraph 51, wherein the second average value of motion information corresponds to an average value of at least one of:

[0491] the sensed amplitude of the second respiratory cycle;

[0492] the sensed respiratory rate of the second respiratory cycle; and

[0493] A ratio of the sensed inspiratory period to the expiratory period of the second respiratory cycle.

[0494] Paragraph 53. The method of paragraph 1, wherein sensing physiological information comprises sensing motion at at least one of the chest and the neck via the implantable sensor, and

[0495] Wherein performing the determination of the sleep-wake state comprises tracking at least one second parameter in addition to movement of the chest or neck, wherein the second parameter comprises at least one of:

[0496] Date and time;

[0497] daily activity patterns; and

[0498] Non-apneic breathing pattern.

[0499] Paragraph 54. The method of paragraph 1, wherein sensing physiological information comprises sensing motion at at least one of the chest and the neck via the implantable sensor, and

[0500] Wherein performing the determination of the sleep-wake state comprises sensing at least one second parameter in addition to movement at the chest or neck, wherein the second parameter comprises a physiological parameter, and using the at least one second parameter in the sleep-wake determination.

[0501] Paragraph 55. The method of Paragraph 54, wherein the second parameter comprises a temperature within the patient sensed by the implantable sensor.

[0502] Paragraph 56. The method of paragraph 55, comprising:

[0503] The sensing of the temperature is performed by an implantable pulse generator housing the implantable sensor.

[0504] Paragraph 57. The method of paragraph 55, wherein sensing the temperature comprises sensing at least one of:

[0505] a profile of the sensed temperature over a 24-hour daily period; and

[0506] A change in sensed temperature exceeding a selectable threshold value occurring within a selectable time window of a 24-hour daily cycle.

[0507] Paragraph 58. The method of paragraph 57, wherein the selectable time window comprises a time window on the order of hours.

[0508] Paragraph 59. The method of paragraph 55, comprising:

[0509] Sleep-disordered breathing is identified when temperature changes are sensed during a therapy cycle.

[0510] Paragraph 60. The method of Paragraph 1, wherein the implantable sensor comprises a temperature sensor, and sensing the physiological information comprises sensing temperature information.

[0511] Paragraph 61. The method of Paragraph 1, wherein the implantable sensor comprises at least one subcutaneous electrode implantable in the head and neck region.

[0512] Paragraph 62. The method of paragraph 61, wherein the sensed physiological information includes at least one of respiratory rate or heart rate.

[0513] Paragraph 63. The method of paragraph 61, comprising:

[0514] The at least one subcutaneous electrode is arranged as a plurality of subcutaneous electrodes integrated into a single device.

[0515] Paragraph 64. The method of Paragraph 61, wherein sensing physiological information comprises sensing cardiac activity via the at least one subcutaneous electrode.

[0516] Paragraph 65. The method of paragraph 64, wherein sensing cardiac activity comprises at least one of:

[0517] sensing benchmarks related to atrial and ventricular behavior;

[0518] sensing an electrical signal; and

[0519] At least one of ballistocardiograph information, shockcardiograph information, and tachycardiograph information is sensed.

[0520] Paragraph 66. The method of Paragraph 61, wherein sensing physiological information comprises sensing electrical brain activity via the at least one subcutaneous electrode.

[0521] Paragraph 67. The method of paragraph 1, wherein determining the sleep-wake state comprises:

[0522] At least one of a sleep probability and a wakefulness probability is assessed based on sensing the physiological information.

[0523] Paragraph 68. The method of paragraph 67, comprising:

[0524] Action is taken when the sleep probability or wake probability exceeds a threshold.

[0525] Paragraph 69. The method of paragraph 68, comprising:

[0526] When the sleep probability or wake probability exceeds the threshold by a selectable predetermined percentage for a selectable predetermined duration, an action is taken.

[0527] Paragraph 70. The method of Paragraph 68, wherein taking action comprises at least one of initiating a stimulation therapy cycle and terminating the stimulation therapy cycle.

[0528] Paragraph 71. The method of paragraph 70, comprising:

[0529] Apply boundaries to the corresponding starts and stops.

[0530] Paragraph 72. The method of paragraph 71, wherein applying the boundary comprises:

[0531] setting a start boundary before which the initiation is not performed; and

[0532] Sets the stopping boundary up to which the termination will be performed.

[0533] Paragraph 73. The method of paragraph 72, comprising:

[0534] Implement appropriate start and stop boundaries based on datetime.

[0535] Paragraph 74. The method of paragraph 73, comprising:

[0536] The date and time are implemented based on at least one of the following:

[0537] Time zone;

[0538] Ambient light via external sensing;

[0539] Daylight Saving Time;

[0540] Geographic latitude; and

[0541] Seasonal calendar.

[0542] Paragraph 75. The method of paragraph 72, comprising:

[0543] The stopping boundary is implemented based on at least one of the number, type, and duration of sleep stages.

[0544] Paragraph 76. The method of paragraph 72, comprising:

[0545] At least one of a start boundary parameter and a stop boundary parameter is implemented based on sensing temperature by the implantable sensor.

[0546] Paragraph 77. The method of Paragraph 70, comprising implementing at least one of initiating the stimulation therapy cycle and terminating the stimulation therapy cycle based on sensing body temperature via the implantable sensor.

[0547] Paragraph 78. The method of paragraph 77, comprising:

[0548] The implantable sensor is disposed within the implantable pulse generator and includes a temperature sensor.

[0549] Paragraph 79. The method of paragraph 67, wherein determining the sleep-wake state further comprises:

[0550] Receiving input from at least one of a remote control and an application on a mobile consumer device regarding at least one of:

[0551] Ambient lighting levels;

[0552] the extent or type of movement of the remote control or mobile consumer device; and

[0553] The frequency, type, or extent of usage of the remote control or mobile consumer device.

[0554] Paragraph 80. The method of paragraph 1, wherein sensing physiological information comprises sensing movement of the chest and / or neck via the implantable sensor,

[0555] Wherein determining the sleep-wake state comprises assessing at least one of a sleep probability and a wake probability based on sensing movement of the chest and / or neck.

[0556] Paragraph 81. The method of Paragraph 1, wherein sensing physiological information comprises obtaining and identifying arousal information, and comprising performing the sleep-wake state determination via the arousal information.

[0557] Paragraph 82. The method of paragraph 81, comprising:

[0558] The identification of arousal is performed by sensing at least one of whole body motion and movement.

[0559] Paragraph 83. The method of paragraph 1, wherein sensing the physiological information includes detecting snoring, and determining the sleep-wake state at least in part upon detecting at least one of the presence and absence of snoring.

[0560] Paragraph 84. The method of Paragraph 1, wherein sensing the physiological information comprises sensing and distinguishing between SDB-induced neural arousal and non-SDB-induced arousal.

[0561] Paragraph 85. The method of paragraph 84, comprising:

[0562] maintaining the stimulation therapy while the SDB-induced neural arousal is sensed; and

[0563] The stimulation therapy is suspended or terminated upon sensing of the non-SDB-induced arousal.

[0564] Paragraph 86. The method of paragraph 85, comprising:

[0565] The determination of the sleep-wake state is performed by sensed physiological information regarding arousal.

[0566] Paragraph 87. A method comprising:

[0567] sensing physiological signals via implantable sensors; and

[0568] The sleep-wake state is determined based on the sensed physiological signals.

[0569] Paragraph 88. The method of paragraph 87, wherein the implantable sensor comprises an accelerometer.

[0570] Paragraph 89. The method of paragraph 87, wherein sensing the physiological signal comprises sensing at least one of:

[0571] posture;

[0572] Respiratory information;

[0573] Heart information;

[0574] activities; and

[0575] Physical exercise.

[0576] Paragraph 90. The method of paragraph 87, comprising:

[0577] segmenting the sensed physiological signal into a plurality of sensed signal components, wherein at least some of the respective sensed signal components include a reference indicative of a sleep-wake determination;

[0578] amplifying a reference within each of at least some of the corresponding sensed signal components; and

[0579] The sleep-wake state is determined based on at least one of the amplified references indicative of a sleep-wake determination from the corresponding sensed signal components.

[0580] Paragraph 91. The method of paragraph 90, wherein determining the sleep-wake state comprises:

[0581] By aggregating the amplified references from the corresponding sensed signal components, an overall sleep-wake determination is performed.

[0582] Paragraph 92. The method of paragraph 91, comprising:

[0583] At least one of clinician input and patient input is received regarding at least some of the sleep-wake determination parameters.

[0584] Paragraph 93. The method of paragraph 87, wherein determining the sleep-wake state comprises:

[0585] The determination is performed by a constructed machine learning model within an implantable medical device comprising the implantable sensor.

[0586] Paragraph 94. The method of paragraph 93, comprising:

[0587] constructing the machine learning model at a location external to the patient; and

[0588] The constructed machine learning model is imported into the implantable medical device.

[0589] Paragraph 95. The method of paragraph 94, comprising:

[0590] The construction is implemented with a known input sensed via the implantable sensor and a known output corresponding to an externally measurable sleep-wake state.

[0591] Paragraph 96. The method of paragraph 95, wherein the known input comprises information sensed via an implantable sensor, the information comprising at least one of:

[0592] Posture information;

[0593] Heart information;

[0594] body movement information;

[0595] Event information; and

[0596] Breathing information.

[0597] Paragraph 97. The method of paragraph 95, comprising:

[0598] The known input and the known output are provided based on a database of stored patient sleep information for a plurality of patients.

[0599] Paragraph 98. The method of paragraph 93, comprising:

[0600] The machine learning model is implemented as at least one of a convolutional neural network and a deep learning network.

[0601] Paragraph 99. The method of paragraph 87, comprising:

[0602] splitting the sensed physiological signal into a plurality of different signal components, wherein each respective signal component represents a different sleep-wake determination parameter; and

[0603] The probability of the sleep-wake state is determined based on evaluating respective different signal components associated with respective different sleep-wake determination parameters.

[0604] Paragraph 100. The method of paragraph 99, comprising:

[0605] A different weight value is applied to each corresponding sleep-wake determination parameter.

[0606] Paragraph 101. The method of paragraph 87, comprising:

[0607] Automatically take action when at least one of the following is true:

[0608] Determined based on the sleep-wake state, the sleep probability exceeds a sleep detection threshold; or

[0609] A wakefulness probability is determined to exceed a wakefulness detection threshold based on the sleep-wakefulness state.

[0610] Paragraph 102. The method of paragraph 101, wherein automatically taking action includes at least one of automatically starting a stimulation therapy cycle and automatically stopping the stimulation therapy cycle.

[0611] Paragraph 103. The method of paragraph 102, comprising:

[0612] receiving input for selectively starting a treatment cycle and / or selectively stopping a treatment cycle; and

[0613] Upon receipt of a selective start input, the automatic start is suspended, and upon receipt of a selective stop input, the automatic termination is suspended.

[0614] Paragraph 104. The method of paragraph 103, comprising:

[0615] For a plurality of nighttime utilization periods, information of at least one of a pattern, a trend, and an average of at least one of the following is tracked.

[0616] Automatic start;

[0617] Automatic stop;

[0618] Selective start; and

[0619] Selective stop.

[0620] Paragraph 105. The method according to paragraph 104,

[0621] At least some of the tracked information is displayed via a graphical user interface.

[0622] Paragraph 106. The method of paragraph 104, comprising:

[0623] The quantitative effectiveness of the automatic sleep-wake determination is determined by determining a first ratio of automatic starts to selective starts and / or a second ratio of automatic stops to selective stops based on at least the tracked information.

[0624] Paragraph 107. The method of paragraph 103, comprising:

[0625] A plurality of separate nighttime utilization cycles are displayed via a graphical user interface, each nighttime utilization cycle symbolically illustrating at least one of the following with respect to a treatment cycle within each nighttime utilization cycle:

[0626] Automatic start;

[0627] Automatic stop;

[0628] Selective start; and

[0629] Selective stop.

[0630] Paragraph 108. An apparatus comprising:

[0631] A first sensor may be implanted to sense physiological information to determine a sleep-wake state through the sensed physiological information.

[0632] Paragraph 109. The apparatus of paragraph 108, wherein the determination of the sleep-wake state comprises detecting the onset of sleep.

[0633] Paragraph 110. The device of paragraph 108, wherein the implantable first sensor is configured to sense physiological information as motion at at least one of the chest and the neck.

[0634] Paragraph 111. The device of paragraph 110, wherein the sensed motion comprises at least one of:

[0635] Sensed respiratory information;

[0636] sensed cardiac information, including heart rate; and

[0637] Sensed whole-body motion.

[0638] Paragraph 112. The apparatus of paragraph 111, wherein the sensed respiratory information is based on sensing movement of the chest wall.

[0639] Paragraph 113. The apparatus of Paragraph 112, wherein the implantable first sensor is configured to distinguish active breathing indicative of a wakeful state from passive breathing indicative of a sleep state based on the magnitude of the sensed chest wall movement.

[0640] Paragraph 114. The apparatus of paragraph 111, comprising:

[0641] A second sensor, in addition to the motion sensing by the first sensor, the second sensor also performs electrocardiogram sensing to further determine the heart information.

[0642] Paragraph 115. The apparatus of paragraph 111, wherein sensing the motion comprises:

[0643] The sensing is performed by the implantable first sensor implanted in the neck region.

[0644] Paragraph 116. The apparatus of paragraph 115, comprising:

[0645] An implantable micro stimulator for implantation in the head and neck region, the implantable micro stimulator comprising the implantable first sensor.

[0646] Paragraph 117. The device of Paragraph 116, wherein the implantable first sensor comprises an accelerometer.

[0647] Paragraph 118. A device according to paragraph 116, wherein the microstimulator includes an outer shell, at least one first electrode is mounted on the outer shell, and the device includes an implantable second electrode spaced apart from the first electrode, wherein in addition to motion sensing by the first sensor, the corresponding first electrode and second electrode are configured to obtain the cardiac information also by electrocardiogram sensing.

[0648] Paragraph 119. The device of Paragraph 118, wherein the second electrode is located on the outer housing of the micro stimulator while being spaced apart from the first electrode.

[0649] Paragraph 120. The apparatus of paragraph 118, comprising:

[0650] A lead extends from the housing of the micro stimulator and includes the second electrode, wherein the lead has a length that keeps the second electrode spaced apart from the first electrode.

[0651] Paragraph 121. The device of paragraph 111, wherein the implantable first sensor comprises an accelerometer.

[0652] Paragraph 122. The apparatus of paragraph 121, comprising:

[0653] An implantable pulse generator comprising the accelerometer.

[0654] Paragraph 123. The apparatus of paragraph 111, comprising:

[0655] An implantable pulse generator comprising an outer shell, at least one first electrode being located on the outer shell, and the device comprising an implantable second electrode spaced apart from the first electrode, wherein, in addition to motion sensing by the first sensor, the respective first and second electrodes are configured to obtain the cardiac information by electrocardiogram sensing.

[0656] Paragraph 124. The device of paragraph 123, wherein the second electrode is located on the outer housing of the implantable pulse generator and is spaced apart from the first electrode.

[0657] Paragraph 125. The apparatus of paragraph 123, comprising:

[0658] A lead extends from the housing of the implantable pulse generator and includes the second electrode, wherein the lead has a length that keeps the second electrode spaced apart from the first electrode.

[0659] Paragraph 126. The apparatus of paragraph 111, wherein the implantable first sensor comprises an accelerometer and is configured to sense cardiac information as ballistocardiograph information, and wherein the implantable first sensor determines the sleep-wake state based on at least one of:

[0660] said ballistocardiograph information;

[0661] Heart rate variability (HRV); and

[0662] Heart rate.

[0663] Paragraph 127. The apparatus of Paragraph 126, wherein the implantable sensor is sized and configured to be implanted within an artery of the patient and wherein the ballistocardiograph information is obtained from sensing movement of at least the arterial wall caused by cardiac activity.

[0664] Paragraph 128. An apparatus according to paragraph 108, wherein the implantable first sensor senses the physiological information as sensed body movement to determine the sleep-wake state by detecting sleep at a date and time and in the absence of sensed body movement within a predetermined time period.

[0665] Paragraph 129. The apparatus of paragraph 128, comprising:

[0666] A stimulation element that stimulates when sleep is detected, wherein the stimulation is maintained until at least one of:

[0667] The first sensor senses physiological information indicating wakefulness; and

[0668] Terminate stimulation manually.

[0669] Paragraph 130. The device of paragraph 129, wherein the stimulation element achieves stimulation by increasing the stimulation from a lower intensity level to a target intensity level.

[0670] Paragraph 131. A device according to paragraph 129, wherein the implantable first sensor senses physiological information including at least one of sensed respiratory information and sensed cardiac information, wherein the device determines the sleep-wake state through at least one of the sensed respiratory information and the sensed cardiac information.

[0671] Paragraph 132. A device according to paragraph 131, wherein the implantable first sensor senses physiological information including posture information, and wherein the device determines the sleep-wake state by at least one of the sensed posture information, the sensed respiratory information, and the sensed cardiac information.

[0672] Paragraph 133. The device of paragraph 129, wherein the device detects sleep without utilizing posture information as part of determining the sleep-wake state.

[0673] Paragraph 134. The apparatus of paragraph 108, wherein the implantable first sensor senses the physiological information by sensing at least respiratory information including respiratory rate, cardiac information including heart rate, variability in at least one of posture and body movement; and

[0674] The apparatus determines the sleep-wake state by determining variability of at least one of corresponding sensed breathing rate, sensed cardiac information including heart rate, sensed posture, and sensed body movement.

[0675] Paragraph 135. A device according to paragraph 134, wherein the implantable first sensor senses the respiratory information by sensing at least one of the start of inspiration, the end of expiratory pause, and the start of exhalation, and wherein the device determines the sleep-wake state by at least one of the sensed start of inspiration, the sensed end of expiratory pause, and the sensed start of exhalation.

[0676] Paragraph 136. A device according to paragraph 134, wherein the implantable first sensor sensing the physiological information includes sensing at least one of an exhalation cutoff and an end of an exhalation pause, and wherein the device determines the sleep-wake state by at least one of the sensed exhalation cutoff or the sensed end of an exhalation pause.

[0677] Paragraph 137. A device according to paragraph 134, wherein the implantable first sensor senses physiological information including at least one of an inhalation to exhalation transition and an exhalation to inhalation transition, and wherein the device determines the sleep-wake state by at least one of the sensed inhalation to exhalation transition and the sensed exhalation to inhalation transition.

[0678] Paragraph 138. A device according to paragraph 134, wherein the implantable first sensor sensing the physiological information includes sensing at least one of an inspiratory peak and an expiratory peak, and wherein the device determines the sleep-wake state by at least one of the sensed inspiratory peak and the sensed expiratory peak.

[0679] Paragraph 139. A device according to paragraph 134, wherein the implantable first sensor senses physiological information including cardiac information by sensing at least one of atrial contraction or ventricular contraction, and wherein the device determines the sleep-wake state by at least one of the sensed atrial contraction and the sensed ventricular contraction.

[0680] Paragraph 140. A device according to paragraph 139, wherein the implantable first sensor sensing the physiological information includes sensing at least one of the peak value of the atrial contraction or the peak value of the ventricular contraction, and wherein the device determines the sleep-wake state by at least one of the sensed peak value of the atrial contraction and the sensed peak value of the ventricular contraction.

[0681] Paragraph 141. A device according to paragraph 134, wherein the implantable first sensor sensing the physiological information includes sensing cardiac information as both atrial contraction and ventricular contraction, and wherein the device determines the sleep-wake state by both the sensed atrial contraction and ventricular contraction.

[0682] Paragraph 142. The device of Paragraph 134, wherein the implantable first sensor senses physiological information including cardiac information such as closure of a heart valve, and wherein the device determines the sleep-wake state by the sensed closure of the heart valve.

[0683] Paragraph 143. A device according to paragraph 134, wherein the device determines the sleep-wake state by both sensed respiratory information and sensed cardiac information as cardiac motion.

[0684] Paragraph 144. The device of paragraph 134, wherein the device processes the sensed physiological information to distinguish:

[0685] a first variability in respiratory information and cardiac information that is characteristic of sleep-disordered breathing; and

[0686] A second variability of the respiratory information and the cardiac information that is characteristic of sleep-disordered breathing, different from the first variability.

[0687] Paragraph 145. An apparatus according to paragraph 108, wherein the apparatus determines the sleep-wake state by identifying a wake state where the variability of the sensed physiological information exceeds a selectable threshold of at least one of:

[0688] Respiratory rate;

[0689] Heart rate;

[0690] the inspiratory and / or expiratory portion of the respiratory cycle;

[0691] duration of the inspiratory portion;

[0692] the amplitude of the peak of the inhalation portion;

[0693] Duration of the peak of the inspiratory portion

[0694] duration of the exhalation portion;

[0695] body movements, including at least one of neck movement and chest movement;

[0696] Posture parameters; and

[0697] The amplitude of the peak of the exhaled portion.

[0698] Paragraph 146. An apparatus according to paragraph 108, wherein the apparatus determines the sleep-wake state by identifying a sleep state where variability of the sensed physiological information remains below a selectable threshold, the selectable threshold comprising variability of at least one of:

[0699] Respiratory rate;

[0700] Heart rate;

[0701] the inspiratory and / or expiratory portion of the respiratory cycle;

[0702] duration of the inspiratory portion;

[0703] the amplitude of the peak of the inhalation portion;

[0704] Duration of the peak of the inspiratory portion

[0705] duration of the exhalation portion;

[0706] body movements, including at least one of chest movement and neck movement;

[0707] Posture parameters; and

[0708] The amplitude of the peak of the exhaled portion.

[0709] Paragraph 147. An apparatus according to paragraph 146, wherein the apparatus compares subsequent second motion information with the first motion information.

[0710] Paragraph 148. An apparatus according to paragraph 147, wherein each of the respective first motion information and second motion information includes at least one of sensed respiratory information, sensed cardiac information, and sensed whole-body motion.

[0711] Paragraph 149. An apparatus according to paragraph 147, wherein the apparatus determines the sleep-wake state when it is determined 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.

[0712] Paragraph 150. An apparatus as described in paragraph 147, wherein the subsequent second information includes information obtained in a sensed second respiratory cycle, and the first information includes information obtained in an immediately previous sensed first respiratory cycle.

[0713] Paragraph 151. The apparatus of paragraph 108, wherein the implantable first sensor sensing the physiological information comprises sensing motion at at least one of a chest and a neck via the implantable sensor, and wherein the apparatus determining the sleep-wake state comprises tracking at least one second parameter in addition to movement of the chest or neck, wherein the second parameter comprises at least one of:

[0714] Date and time;

[0715] daily activity patterns; and

[0716] Non-apneic breathing pattern.

[0717] Paragraph 152. The device of paragraph 108, wherein the implantable first sensor sensing the physiological information comprises sensing, via the implantable sensor, motion at at least one of a chest and a neck.

[0718] Paragraph 153. An apparatus according to paragraph 152, wherein the apparatus determines the sleep-wake state by sensing at least one second parameter in addition to movement at the chest or neck, wherein the second parameter comprises a physiological parameter, and utilizes at least one second parameter in the sleep-wake determination.

[0719] Paragraph 154. The device of paragraph 153, wherein the second parameter comprises a temperature within the patient sensed by the implantable sensor.

[0720] Paragraph 155. The device of paragraph 108, wherein the implantable sensor comprises a temperature sensor, and sensing the physiological information comprises sensing temperature information.

[0721] Paragraph 156. The device of paragraph 108, wherein the implantable sensor comprises at least one subcutaneous electrode implantable in the head and neck region.

[0722] Paragraph 157. An apparatus according to paragraph 156, wherein the sensed physiological information includes at least one of respiratory rate or heart rate.

[0723] Paragraph 158. The device of Paragraph 156, wherein the at least one subcutaneous electrode comprises a plurality of subcutaneous electrodes integrated into a single device.

[0724] Paragraph 159. The device of Paragraph 156, wherein the implantable first sensor senses physiological information including cardiac activity via the at least one subcutaneous electrode.

[0725] Paragraph 160. The device of Paragraph 156, wherein the implantable first sensor senses physiological information including electrical brain activity via the at least one subcutaneous electrode.

[0726] Paragraph 161. A device according to paragraph 108, wherein the device determines the sleep-wake state as an assessment based on at least one of sensed physiological information, a sleep probability, and a wake probability.

[0727] Paragraph 162. The apparatus of paragraph 161, comprising:

[0728] Action is taken when the sleep probability or wake probability exceeds a threshold.

[0729] Paragraph 163. The apparatus of paragraph 162, comprising:

[0730] When the sleep probability or wake probability exceeds the threshold by a selectable predetermined percentage for a selectable predetermined duration, an action is taken.

[0731] Paragraph 164. The device of paragraph 162, wherein taking action comprises at least one of initiating a stimulation therapy cycle and terminating the stimulation therapy cycle.

[0732] Paragraph 165. The device according to paragraph 164, wherein the device

[0733] Apply boundaries to the corresponding starts and stops.

[0734] Paragraph 166. The apparatus of paragraph 165, wherein applying the boundary comprises:

[0735] setting a start boundary before which the initiation is not performed; and

[0736] Sets the stopping boundary up to which the termination will be performed.

[0737] Paragraph 167. The apparatus of paragraph 166, wherein the apparatus implements corresponding start and stop boundaries based on date and time.

[0738] Paragraph 168. The device of Paragraph 164, wherein the device implements at least one of initiating the stimulation therapy cycle and terminating the stimulation therapy cycle based on sensing body temperature via the implantable sensor.

[0739] Paragraph 169. The device of paragraph 161, wherein the device determines the sleep-wake state by receiving input from at least one of a remote control and an application on a mobile consumer device regarding at least one of:

[0740] Ambient lighting levels;

[0741] the extent or type of movement of the remote control or mobile consumer device; and

[0742] The frequency, type, or extent of usage of the remote control or mobile consumer device.

[0743] Paragraph 170. An apparatus according to paragraph 108, wherein the implantable first sensor sensing the physiological information includes sensing movement of the chest and / or neck via the implantable sensor, and wherein the apparatus determining the sleep-wake state includes evaluating at least one of a sleep probability and a wake probability based on sensing movement of the chest and / or neck.

[0744] Paragraph 171. An apparatus according to paragraph 108, wherein the sensing of the physiological information by the implantable first sensor includes obtaining and identifying arousal information, and determining the sleep-wake state through the arousal information, wherein, optionally, the identification of arousal is performed by sensing at least one of whole body movement and movement.

[0745] Paragraph 172. The apparatus of paragraph 108, wherein the implantable first sensor sensing the physiological information includes detecting snoring, and the sleep-wake state is determined at least in part upon detecting at least one of the presence and absence of snoring.

[0746] Paragraph 173. The apparatus of Paragraph 108, wherein the implantable first sensor sensing the physiological information comprises sensing and distinguishing neural arousal induced by SDB from arousal induced by non-SDB.

[0747] Paragraph 174. The device of paragraph 173, wherein the device:

[0748] maintaining the stimulation therapy while the SDB-induced neural arousal is sensed; and

[0749] The stimulation therapy is suspended or terminated upon sensing of the non-SDB-induced arousal.

[0750] Paragraph 175. An apparatus according to paragraph 174, wherein the apparatus determines the sleep-wake state by sensed physiological information about arousal.

[0751] Paragraph 176. The method of paragraph 108, wherein the implantable first sensor senses a physiological signal comprising at least one of:

[0752] posture;

[0753] Respiratory information;

[0754] Heart information;

[0755] activities; and

[0756] Physical exercise.

[0757] Paragraph 177. The device of paragraph 176, wherein the implantable first sensor comprises an accelerometer.

[0758] Paragraph 178. The device of paragraph 108, wherein the device:

[0759] segmenting the sensed physiological signal into a plurality of sensed signal components, wherein at least some of the respective sensed signal components include a reference indicative of a sleep-wake determination;

[0760] amplifying a reference within each of at least some of the corresponding sensed signal components; and

[0761] The sleep-wake state is determined based on at least one of the amplified references indicative of a sleep-wake determination from the corresponding sensed signal components.

[0762] Paragraph 179. An apparatus according to paragraph 178, wherein the apparatus determines the sleep-wake state from an overall sleep-wake determination by aggregating amplified references from corresponding sensed signal components.

[0763] Paragraph 180. The device of Paragraph 179, wherein the device receives at least one of clinician input and patient input regarding at least some of the sleep-wake determination parameters.

[0764] Paragraph 181. An apparatus according to paragraph 108, wherein the determination of the sleep-wake state comprises making the determination via a machine learning model constructed within an implantable medical device comprising the implantable sensor.

[0765] Paragraph 182. The device of Paragraph 181, wherein the device constructs the machine learning model at a location external to the patient and imports the constructed machine learning model into the implantable medical device.

[0766] Paragraph 183. The device of paragraph 182, wherein the device implements the constructing via a known input sensed via the implantable sensor and a known output corresponding to an externally measurable sleep-wake state.

[0767] Paragraph 184. The device of paragraph 183, wherein the known input comprises information sensed by an implantable sensor, the information comprising at least one of:

[0768] Posture information;

[0769] Heart information;

[0770] body movement information;

[0771] Event information; and

[0772] Breathing information.

[0773] Paragraph 185. The apparatus of Paragraph 183, wherein the apparatus provides the known input and the known output based on a stored database of patient sleep information for a plurality of patients.

[0774] Paragraph 186. A device according to paragraph 181, wherein the device implements the machine learning model as at least one of a convolutional neural network and a deep learning network.

[0775] Paragraph 187. The method of paragraph 108, wherein the device:

[0776] splitting the sensed physiological signal into a plurality of different signal components, wherein each respective signal component represents a different sleep-wake determination parameter; and

[0777] The probability of the sleep-wake state is determined based on evaluating respective different signal components associated with respective different sleep-wake determination parameters.

[0778] Paragraph 188. An apparatus according to paragraph 187, wherein the apparatus applies a different weight value to each corresponding sleep-wake determination parameter.

[0779] Paragraph 189. The device of paragraph 108, wherein the device automatically takes action when at least one of the following is true:

[0780] Determined based on the sleep-wake state, the sleep probability exceeds a sleep detection threshold; or

[0781] A wakefulness probability is determined to exceed a wakefulness detection threshold based on the sleep-wakefulness state.

[0782] Paragraph 190. The device of Paragraph 189, wherein automatically taking action includes at least one of automatically starting a stimulation therapy cycle and automatically stopping the stimulation therapy cycle.

[0783] Paragraph 191. The device of paragraph 190, wherein the device:

[0784] receiving input for selectively starting a treatment cycle and / or selectively stopping a treatment cycle; and

[0785] Upon receipt of a selective start input, the automatic start is suspended, and upon receipt of a selective stop input, the automatic termination is suspended.

[0786] Paragraph 192. The apparatus of paragraph 191, wherein the apparatus tracks information of at least one of a pattern, a trend, and an average of at least one of the following for a plurality of nighttime utilization periods:

[0787] Automatic start;

[0788] Automatic stop;

[0789] Selective start; and

[0790] Selective stop.

[0791] Paragraph 193. The apparatus of paragraph 192, comprising a graphical user interface for displaying at least some of the tracked information.

[0792] Paragraph 194. The apparatus of paragraph 191, comprising:

[0793] A graphical user interface for displaying a plurality of separate nighttime utilization periods, each nighttime utilization period symbolically illustrating at least one of the following with respect to a treatment cycle within each nighttime utilization period:

[0794] Automatic start;

[0795] Automatic stop;

[0796] Selective start; and

[0797] Selective stop.

Claims

1. An implantable medical device comprising an implantable sensor and a control portion, wherein the control portion comprises a controller and a memory, wherein the controller comprises at least one processor, wherein the processor is configured to perform the following operations: Sensing physiological information through implantable sensors; and Determine sleep-wake state through sensed physiological information, in, The implantable medical device automatically takes action when at least one of the following is true: Determining, based on the sleep-wake state, that a sleep probability exceeds a sleep detection threshold; and Determined based on the sleep-wake state, the wakefulness probability exceeds a wakefulness detection threshold, Wherein, automatically taking action includes at least one of automatically starting a stimulation therapy cycle and automatically stopping the stimulation therapy cycle, Wherein, the implantable medical device: receiving input for selectively starting a treatment cycle and / or selectively stopping a treatment cycle; and The suspension automatically begins upon receipt of a selective start input and automatically ends upon receipt of a selective stop input.

2. The implantable medical device of claim 1, wherein determining the sleep-wake state comprises detecting the onset of sleep.

3. The implantable medical device of claim 1 , wherein sensing physiological information comprises: Movement at at least one of the chest and the neck is sensed by the implantable sensor.

4. The implantable medical device of claim 3 , wherein sensing motion comprises sensing at least one of: Respiratory information; Cardiac information, including heart rate; and Full body exercise.

5. The implantable medical device of claim 4, wherein the respiratory information is based on sensing movement of the chest wall.

6. The implantable medical device of claim 5, wherein the processor is further configured to execute: Based on the magnitude of the sensed chest wall motion, active breathing, indicative of a wakeful state, is distinguished from passive breathing, indicative of a sleeping state.

7. The implantable medical device of claim 4, wherein the processor is further configured to execute: In addition to motion sensing, electrocardiogram sensing is also performed to further determine the cardiac information.

8. The implantable medical device of claim 4, wherein sensing the motion comprises: The sensing is performed by an implantable sensor implanted in the neck region.

9. The implantable medical device of claim 8, wherein implanting the implantable sensor comprises: implanting a micro stimulator including the implantable sensor into the neck region, Wherein performing the sensing comprises implementing the sensing via an accelerometer of the micro stimulator as at least one implantable sensor.

10. The implantable medical device of claim 4, wherein performing the sensing comprises implementing the sensing via an accelerometer as the at least one implantable sensor.

11. The implantable medical device of claim 10, wherein the processor is further configured to execute: The accelerometer is arranged as part of an implantable pulse generator.

12. The implantable medical device of claim 4, wherein the processor is further configured to execute: In addition to motion sensing, additional cardiac information is obtained by performing electrocardiographic sensing via at least one first electrode on the outer housing of the pulse generator and a second electrode spaced apart from the first electrode.

13. The implantable medical device of claim 1 , wherein sensing physiological information comprises sensing body movement, and wherein determining the sleep-wake state comprises: Detect sleep when: Date and time; as well as Lack of sensed body movement for a predetermined period of time.

14. The implantable medical device of claim 13, wherein the processor is further configured to execute: When sleep is detected, delivering stimulation; and Stimulation is maintained until at least one of the following occurs: sensing physiological information indicative of arousal; and Terminate stimulation manually.

15. The implantable medical device of claim 14, wherein administering stimulation comprises increasing stimulation from a lower intensity level to a target intensity level.

16. The implantable medical device of claim 14 , wherein the sensed physiological information comprises at least one of respiratory information and cardiac information, and the processor is further configured to: Sleep is detected based on at least one of the sensed respiratory information and the sensed cardiac information.

17. The implantable medical device of claim 16, wherein the sensed physiological information includes posture information, and the processor is further configured to: Sleep is detected using at least one of the sensed posture information, the sensed breathing information, and the sensed heart information.

18. The implantable medical device of claim 14, wherein the processor is further configured to execute: Detection of sleep is performed without using posture information.

19. The implantable medical device of claim 1 , wherein sensing the physiological information comprises sensing at least variability in at least one of respiratory information including respiratory rate, cardiac information including heart rate, posture, and body movement; and The determination of the sleep-wake state is performed by variability of at least one of corresponding sensed breathing rate, sensed cardiac information including heart rate, sensed posture, and sensed body movement.

20. The implantable medical device of claim 19, wherein the processor is further configured to execute: Processes the sensed physiological information to distinguish between: a first variability in respiratory information and cardiac information that is characteristic of sleep-disordered breathing; and A second variability of the respiratory information and the cardiac information that is characteristic of sleep-disordered breathing, different from the first variability.

Images