SLEEP APNEA DETECTION SYSTEM

• US 20130144190A1
• Filed: 05/27/2011
• Published: 06/06/2013
• Est. Priority Date: 05/28/2010
• Status: Abandoned Application

• Current Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Original Assignee: Mayo Foundation For Medical Education And Research (Mayo Clinic)
• Inventors: Bruce, Charles J., Friedman, Paul A., Bennet, Kevin E., Somers, Virend K.
• Application Number:
  US13/700,702
• Publication Number:
  US 20130144190A1
• Time in Patent Office: Days
• US Class Current: 600/586
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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 7/003 Detecting lung or respiration noise

Abstract

This document provides methods and materials (e.g., systems) related to assessing sleep conditions (e.g., sleep apnea).

Claims

1. A method for assessing sleep of a human in a normal sleep environment, wherein said method comprises:

(a) detecting audible sounds from said human in said normal sleep environment using a mobile electronic device having a sound sensor, and

(b) determining whether said audible sounds are indicative of normal sleep or a disorder present in said human.

9. The method of claim 1, wherein said audible sounds comprise snoring sounds of said human, and said determining step comprises determining that said audible sounds are indicative of normal sleep.

11. The method of claim 1, wherein said audible sounds comprise snoring sounds of said human, and said determining step comprises determining that said audible sounds are indicative of said disorder.

15. The method of claim 11, wherein said method comprises informing said human via said mobile device that said human has said disorder.

16. The method of claim 1, wherein said audible sounds comprise snoring sounds of said human, and said determining step comprises determining that said audible sounds are indicative of sleep apnea.

17. The method of claim 16, wherein said method comprises informing said human via said mobile device that said human has sleep apnea.

18. The method of claim 1, wherein said audible sounds comprise snores of said human, and said determining step comprises assessing the amplitude of said snores, the interval between said snores, the frequency composition of snores, or the duration of snores.

19. The method of claim 1, wherein said audible sounds comprise snores of said human, and said determining step comprises assessing the amplitude of said snores, the interval between said snores, the frequency composition of snores, and the duration of snores.

20. The method of claim 1, wherein said method comprises using waveform autocorrelation, frequency analysis for identifying an increased variation and power spectrum shift towards higher frequencies, an analysis of cesptral coefficients, or a hidden markov model.

21. The method of claim 1, wherein said method comprises recording said audible sounds.

23. The method of claim 1, wherein said method comprises transmitting said audible sounds with said mobile device to a computer.

24. The method of claim 23, wherein said method comprises recording said transmitted audible sounds on recordable medium.

25. The method of claim 1, wherein said determining step comprises obtaining a Fourier transform of at least a segment of said audible sounds.

26. The method of claim 1, wherein said method comprises detecting said audible sounds in stereo using said sound sensor and a second sound sensor.

30. The method of claim 1, wherein said method comprises detecting video signals from said human.

31. The method of claim 1, wherein said method comprises using one or more sensors to measure oxygen saturation, breathing, heart rate, electrocardiographic information, posture, body movements, electroencephalographic information, nasal air flow, oral air flow, CO₂ levels, body temperature, air temperature, or bioimpedance.

32. The method of claim 31, wherein said one or more sensors are attached to said human.

33. The method of claim 1, wherein said determining step is performed by an electronic computing device programmed to analyze data from a digital representation of said detected audible sounds.

34. The method of claim 33, wherein said electronic computing device is part of a unitary structure with said mobile electronic device.

2-8. -8. (canceled)

10. (canceled)

12-14. -14. (canceled)

22. (canceled)

27-29. -29. (canceled)

35. A method for assessing a human for a likelihood of obstructive sleep apnea in a normal sleep environment, wherein said method comprises:

(a) obtaining clinical information about said human,

(b) detecting audible sounds from said human in said normal sleep environment using a mobile electronic device having a sound sensor, and

(c) determining whether said human is likely to experience obstructive sleep apnea based on said clinical information and said audible sounds.