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CN-115334959-B - Sleep state detection for apnea-hypopnea index calculation

CN115334959BCN 115334959 BCN115334959 BCN 115334959BCN-115334959-B

Abstract

Apparatus, systems, and methods are disclosed. The apparatus, system, and method detect one or more parameters related to a user's movement, a user's heart activity, audio associated with the user, or a combination thereof during a sleep period of the user, process the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or sleep stage, and calculate an apnea-hypopnea index of the user during the sleep period based at least in part on the sleep state.

Inventors

  • NEIL FOX
  • Anna Rice
  • Si Difen McMahon
  • Graham Lyon
  • Redmond schuddes
  • STEPHEN DODD

Assignees

  • 瑞思迈传感器技术有限公司

Dates

Publication Date
20260512
Application Date
20210131
Priority Date
20200131

Claims (20)

  1. 1.A method, comprising: Detecting one or more parameters relating to movement of a user during a sleep period of the user; Processing the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or sleep stage; Determining an occurrence of a sleep disordered breathing event during the sleep period; Ignoring one or more sleep disordered breathing events in response to the sleep state being determined to be awake during the one or more sleep disordered breathing events, and An apnea-hypopnea index of the user during the sleep period is calculated based at least in part on remaining sleep disordered breathing events occurring during the sleep period.
  2. 2. The method of claim 1, wherein the sleep period comprises a period of applying pressurized air to the airway of the user.
  3. 3. The method of claim 1 or 2, wherein the sleep stage comprises an indication of non-rapid eye movement sleep, N1 sleep, N2 sleep, N3 sleep, or rapid eye movement sleep.
  4. 4. The method of claim 1, wherein the one or more events are one or more apneas, one or more hypopneas, or a combination thereof.
  5. 5. The method of claim 1 or 2, wherein the one or more parameters relate to duration, period, rate, frequency, intensity, type of movement of the user, or a combination thereof.
  6. 6. The method of claim 1 or 2, wherein the one or more parameters are measured based on one or more sensors placed on the user, placed in proximity to the user, or a combination thereof.
  7. 7. The method of claim 6, wherein pressurized air is applied to the airway of the user through a tube connected to a respiratory device and a mask, and at least one of the one or more sensors is located on or within the tube, on or within the mask, or a combination thereof.
  8. 8. The method of claim 7, wherein the at least one sensor comprises a motion sensor on or within the tube, on or within the mask, or a combination thereof.
  9. 9. The method of claim 6, wherein at least one of the one or more sensors comprises a motion sensor within a smart device.
  10. 10. The method of claim 9, wherein the smart device is one or more of (1) a smart watch, a smart phone, a smart mask, a smart garment, a smart mattress, a smart pillow, a smart sheet, or a smart ring, each in contact with the user, (2) a radar-based sensor, a sonar-based sensor, a lidar-based sensor, or other non-contact motion sensor, each in proximity to the user, (3) or a combination thereof.
  11. 11. The method of claim 1 or 2, wherein processing the one or more parameters comprises processing a signal representative of a change in at least one of the one or more parameters over time.
  12. 12. A method, comprising: Detecting one or more parameters related to heart activity of a user during a sleep period of the user; processing the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or sleep stage, and An apnea-hypopnea index of the user during the sleep period is calculated based at least in part on the sleep state, wherein the one or more events are ignored in response to the sleep state being determined to be awake during the one or more events affecting the calculation of the apnea-hypopnea index of the user.
  13. 13. The method of claim 12, wherein the one or more events are one or more apneas, one or more hypopneas, or a combination thereof.
  14. 14. The method of claim 12 or 13, wherein the one or more parameters relate to a heart rate, heart rate variability, cardiac output, or a combination thereof, of the user.
  15. 15. The method of claim 14, wherein the heart rate variability is calculated over a period of one minute, five minutes, ten minutes, half an hour, one hour, two hours, three hours, or four hours.
  16. 16. The method of claim 12 or 13, wherein pressurized air is applied to the airway of the user through a tube connected to a respiratory device and a mask, and at least one of the one or more sensors is located on or within the tube, on or within the mask, or a combination thereof.
  17. 17. The method of claim 12 or 13, wherein the sleep period comprises a period of applying pressurized air to the airway of the user.
  18. 18. The method of claim 12 or 13, wherein detecting the one or more parameters is based on cepstral analysis, spectral analysis, fast fourier transform, or a combination thereof of one or more stream signals, one or more audio signals, or a combination thereof.
  19. 19. A method, comprising: Detecting one or more parameters related to audio associated with a user during a sleep period of the user; Processing the one or more parameters to determine a sleep state of the user, the sleep state being at least one of awake, asleep, or sleep stage; Determining an occurrence of a sleep disordered breathing event during the sleep period; Ignoring one or more sleep disordered breathing events in response to the sleep state being determined to be awake during the one or more sleep disordered breathing events, and An apnea-hypopnea index of the user during the sleep period is calculated based at least in part on remaining sleep disordered breathing events occurring during the sleep period.
  20. 20. The method of claim 19, wherein the one or more events are one or more apneas, one or more hypopneas, or a combination thereof.

Description

Sleep state detection for apnea-hypopnea index calculation Cross Reference to Related Applications The present application claims the benefit and priority of U.S. provisional patent application No.63/002,585 entitled "sleep state detection for apnea-hypopnea index calculation" filed on 3/31/2020, and U.S. provisional patent application No.62/968,775 entitled "sleep state detection for apnea-hypopnea index calculation" filed on 31/2020, the contents of which are incorporated herein by reference in their entirety. Technical Field The present technology relates to devices, systems, and methods for sleep state detection and determination of an apnea-hypopnea index (AHI) that accounts for sleep states. Background Whether the user is asleep or awake may be considered a sleep state. After falling asleep, sleep may be characterized by four distinct sleep stages that may vary throughout the night. Users, particularly healthy users, typically move between sleep stages in sequence multiple times during sleep. Sleep stages include N1, N2, and N3, collectively referred to as non-rapid eye movement stages and rapid eye movement. Stage N1 is the shallowest sleep stage characterized by the presence of some low amplitude waves of multiple frequencies interspersed with alpha waves for more than 50% of the epoch. There may also be sharp peak waves, some slow eye movements on the electro-oculogram (EOG) signal, and/or an overall decrease in the frequency of the electroencephalogram (EEG) signal. Stage N2 is a slightly deeper sleep stage and is marked by the sleep axis and the occurrence of K-complexes on the background of the mixed signal. Sleep spindles are bursts of higher frequency activity (e.g., greater than 12 Hz). The K-complex is a distinct isolated dipole wave lasting about 1-2 seconds. Stage N3 is the deepest sleep stage characterized by slow waves (e.g., 1-2Hz frequency) occurring at least 20% of the time. Stage REM is rapid eye movement sleep and becomes apparent by the presence of significant activity in the EOG signal. The recorded EEG signals are typically very similar to phase N1 or even wakefulness. The term Sleep Disordered Breathing (SDB) may refer to conditions in which there is an apnea (e.g., airflow ceases for ten seconds or more) and hypopnea (e.g., airflow decreases by at least 30% for 10 seconds or more, with associated oxygen desaturation or arousal) during sleep. Respiratory instability is an indication of wakefulness or REM sleep, and respiratory stability is an indication of non-REM (e.g., N1, N2, N3) sleep. However, respiratory instability alone is not sufficient to accurately infer sleep stages. For example, respiratory instability is an indication of arousal or REM sleep, which may also occur as a result of frequent respiratory events, such as apneas, hypopneas, and Respiratory Effort Related Arousals (RERAs) that occur during sleep. Therefore, it is helpful to distinguish between periods of respiratory instability driven primarily by respiratory events and periods of actual arousal. While positive airway pressure breathing apparatuses may be configured to detect Sleep Disordered Breathing (SDB) events, such as apneas and hypopneas, in real time, they often miss detecting SDB events based on the user not falling asleep or the user being in an incorrect sleep stage. For example, analysis of the flow may result in determining whether the user is asleep, and even what sleep stages. However, there are limitations to such flow-based sleep stages. It can be difficult to accurately distinguish between wake and sleep states with a stream-based signal. The stream-based signal may be segmented, losing information at the beginning, middle (when going to a restroom or middle of night) and end. It is of interest to know when the user sleeps, when the user wakes up, and what sleep stages the user has passed at the same time. The complete representation of the various sleep stages passed by the user during a sleep session is referred to as a sleep map. One example application of sleep patterns is the calculation of an SDB severity index, known as the apnea-hypopnea index (AHI). AHI, which is typically calculated as the total number of apneas and hypopneas divided by the length of sleep time, is a widely used SDB screening, diagnostic and monitoring tool. However, such calculations tend to underestimate the AHI because the user may not fall asleep for a significant period of time during the session. As a result, users tend to obtain overly optimistic images of the user's treatment efficacy if traditional AHI calculations are employed. In particular, with respect to flow-based sleep stages, it is biased towards sleep, so SDB events that occur during wakefulness are incorrectly counted into the AHI. Conversely, if an SDB event is erroneously detected while the user is awake and moving, the AHI may be overestimated. Overestimation and/or underestimation may mean that, for example, an automatic setting algorithm o