SLEEP PARAMETERS

• US 20150112155A1
• Filed: 10/23/2014
• Published: 04/23/2015
• Est. Priority Date: 10/23/2013
• Status: Abandoned Application

• Current Assignee: Robert F. Dudley
• Original Assignee: Quanttus Inc
• Inventors: Bijjani, Richard Robehr, He, David Da
• Application Number:
  US14/521,822
• Publication Number:
  US 20150112155A1
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02007 Evaluating blood vessel condition, e.g. elasticity, compliance
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02427 Details of sensor
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0285 Measuring or recording phase velocity of blood waves
• CPC Class Codes: A61B 5/029 Measuring or recording blood output from the heart, e.g. minute volume A61B8/065 takes precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/117 : Identification of persons methods or arrangements for recognising patterns, e.g. fingerprints, G06F18/00, G06V40/00; identification of persons by analysing their voice or speech G10L17/00
• CPC Class Codes: A61B 5/14542 : for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/165 : Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/363 : Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4809 : Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4815 : Sleep quality
• CPC Class Codes: A61B 5/4818 : Sleep apnoea
• CPC Class Codes: A61B 5/4833 : Assessment of subject's compliance to treatment
• CPC Class Codes: A61B 5/486 : Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6898 : Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 3/0481 : based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G08B 21/0453 : worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/06 : indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

The technology described in this document is embodied in a method that includes processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject while the subject is sleeping, processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject while the subject is sleeping, and determining, based on the data, information about a characteristic of the subject'"'"'s sleep.

Claims

1. A method comprising:

processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject while the subject is sleeping;

processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject while the subject is sleeping; and

determining, based on the data, information about a characteristic of the subject'"'"'s sleep.

2. The method of claim 1, wherein the information about at least one pulse pressure wave propagating through blood in the subject comprises photoplethysmographic (PPG) data and the information about motion of the subject comprises one or both of motioncardiogram (MoCG) data and gross motion data.

3. The method of claim 1, wherein the data is acquired continuously.

4. The method of claim 1, wherein the data is acquired at a frequency of between 75 Hz and 85 Hz.

5. The method of claim 1, wherein the data is acquired at a single location of the subject.

6. The method of claim 5, wherein the data is acquired by a device worn by the subject.

7. The method of claim 6, wherein the device is mobile and does not reduce a mobility of the subject.

8. The method of claim 6, wherein the device processes the data.

9. The method of claim 5, wherein the single location is an arm of the subject.

10. The method of claim 9, wherein the single location is a wrist of the subject.

11. The method of claim 1, further comprising generating a reduced set of data by excluding data associated with non-sleep periods of the subject.

12. The method of claim 11, wherein a period of time is identified as a non-sleep period based on gross motion data of the subject.

13. The method of claim 12, wherein identifying the period of time as a non-sleep period comprises determining that the gross motion data during the period of time is above a threshold.

14. The method of claim 12, wherein identifying the period of time as a non-sleep period comprises determining that the gross motion data during the period of time is substantially irregular.

15. The method of claim 1, wherein a period of time is identified as a sleep period based on gross motion data of the subject.

16. The method of claim 15, wherein identifying the period of time as a sleep period comprises determining that the gross motion data during the period of time is below a threshold.

17. The method of claim 15, wherein identifying the period of time as a sleep period comprises determining that the gross motion data during the period of time is substantially flat.

18. The method of claim 15, further comprising determining a start and an end of the sleep period.

19. The method of claim 18, wherein determining the start of the sleep period comprises identifying a time when the gross motion data falls below a threshold, and determining the end of the sleep period comprises identifying a time when the gross motion data rises above a threshold.

20. The method of claim 1, further comprising calculating a property of the sleep of the subject based on the data.

21. The method of claim 20, wherein the property is associated with one or more of heart rate, heart rate variability, activity level, respiratory rate, and blood pressure of the subject.

22. The method of claim 21, wherein one or more of the heart rate, the heart rate variability, the activity level, the respiratory rate, and the blood pressure of the subject are determined based on the processed data.

23. The method of claim 22, wherein determining the heart rate of the subject comprises calculating a distance between two consecutive reference points in the first dataset, the distance representing a time that has elapsed between two consecutive heartbeats of the subject.

24. The method of claim 23, wherein the reference points are local maxima or local minima.

25. The method of claim 23, wherein the reference points are peaks or valleys.

26. The method of claim 22, wherein determining the heart rate variability of the subject comprises calculating distances between multiple pairs of consecutive reference points in the first dataset, each distance representing a time that has elapsed between two consecutive heartbeats of the subject.

27. The method of claim 22, wherein determining the blood pressure of the subject comprises:

identifying a first point in the first dataset, the first point representing an arrival time of the pulse pressure wave at a first body part of the subject;

identifying a second point in the second dataset, the second point representing an earlier time at which the pulse pressure wave traverses a second body part of the subject;

computing a pulse transit time (PTT) as a difference between the first and second points, the PTT representing a time taken by the pulse pressure wave to travel from the second body part to the first body part of the subject, wherein the PTT is related to an internal pressure of one or more blood vessels of the subject; and

determining the blood pressure of the subject based on the internal pressure of the one or more blood vessels.

28. The method of claim 27, wherein the first body part is the location of the subject at which the data in the first data set is acquired, and the second body part is the heart of the subject.

29. The method of claim 21, wherein the characteristic of the subject'"'"'s sleep is determined based on the property.

30. The method of claim 29, wherein the characteristic comprises sleep apnea.

31. The method of claim 30, wherein determining that the subject is experiencing sleep apnea comprises identifying a simple signal in a heart rate signal of the subject that is acquired during a sleep period of the subject.

32. The method of claim 31, wherein determining that the subject is experiencing sleep apnea comprises identifying recurring simple signals in the heart rate signal of the subject.

33. The method of claim 29, wherein the characteristic comprises a quality of the sleep, including one or more of a sleep duration, a sleep latency, a sleep staging, a number of disturbances, and a number of tosses and turns.

34. The method of claim 33, wherein determining information about a characteristic of the subject'"'"'s sleep comprises determining the sleep duration of the subject.

35. The method of claim 34, wherein determining the sleep duration of the subject comprises determining a total length of time during which the subject was asleep based on information related to one or more sleep rest periods of the subject.

36. The method of claim 35, wherein the information related to the one or more sleep rest periods comprises a time associated with a beginning of each sleep rest period, a time associated with an end of each sleep rest period, gross motion data of the subject during each sleep rest period, and heart rate data of the subject during each sleep rest period.

37. The method of claim 33, wherein determining information about a characteristic of the subject'"'"'s sleep comprises determining the sleep latency of the subject.

38. The method of claim 37, wherein determining the sleep latency of the subject comprises determining a length of time that it takes for the subject to transition from a state of wakefulness to the sleep state based on information related to one or more sleep rest periods of the subject and gross motion data of the subject before the subject fell asleep.

39. The method of claim 33, wherein determining information about a characteristic of the subject'"'"'s sleep comprises determining the sleep staging of the subject.

40. The method of claim 39, wherein determining the sleep staging of the subject comprises determining a deepness of the subject'"'"'s sleep during a portion of each of one or more sleep rest periods of the subject based on information related to the one or more sleep rest periods.

41. The method of claim 40, wherein the sleep staging of the subject is determined based on at least a heart rate and gross motion data of the subject during one or more of the portions of the sleep rest periods.

42. The method of claim 34, further comprising alerting the subject when the sleep duration exceeds a threshold while the subject is in a light sleep stage.

43. The method of claim 29, wherein the characteristic comprises a sleep disorder.

44. The method of claim 29, wherein the characteristic comprises a level of nocturnal dip of blood pressure.

45. The method of claim 1, further comprising processing data that represents information about an amount of ultraviolet light that the subject has been exposed to.

46. The method of claim 45, further comprising correlating a characteristic of the subject'"'"'s sleep to the amount of ultraviolet light that the subject has been exposed to.

47. The method of claim 45, further comprising correlating a quality of the subject'"'"'s sleep to the amount of ultraviolet light that the subject has been exposed to.

48. The method of claim 45, further comprising correlating a duration of the subject'"'"'s sleep to the amount of ultraviolet light that the subject has been exposed to.

49. A biofeedback device configured to be worn by a subject, the device comprising:

a light source configured to emit light toward the skin of the subject;

an optical sensor configured to;

receive the emitted light after the emitted light reflects off of the skin of the subject; and

provide data that corresponds to a characteristic of the received light, the data representing time-varying information about at least one pulse pressure wave propagating through blood in the subject acquired by the optical sensor at a location of the subject;

a motion sensor configured to provide data that represents time-varying information about motion of the subject acquired by the motion sensor at the location of the subject; and

a processor configured to;

receive data from one or more of the light-emitting element, the optical sensor, and the motion sensor; and

determine, based on the data, information about a characteristic of the subject'"'"'s sleep.

50. One or more machine-readable storage devices storing instructions that are executable by one or more processing devices to perform operations comprising:

processing data in a first dataset that represents time-varying information about at least one pulse pressure wave propagating through blood in a subject acquired at a location of the subject while the subject is sleeping;

processing data in a second dataset that represents time-varying information about motion of the subject acquired at the location of the subject while the subject is sleeping; and

determining, based on the data, information about a characteristic of the subject'"'"'s sleep.