Detecting Sleep Disorders Using Heart Activity

• US 20100204586A1
• Filed: 02/12/2009
• Published: 08/12/2010
• Est. Priority Date: 02/12/2009
• Status: Active Grant

• Current Assignee: Braemar Manufacturing LLC (Koninklijke Philips N.V.)
• Original Assignee: Cardionet Incorporated
• Inventors: Pu, Yachuan, Gropper, Charles, Lin, Dongping
• Granted Patent: US 8,515,529 B2
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 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 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00

Abstract

Systems, methods, apparatus, and computer program products for detecting the existence of a sleep disorder in an individual using heart activity. In one aspect, machine-implemented methods include the actions of analyzing a machine-readable heart rate series of a monitored individual in the time domain using one or more digital data processing devices, detecting a cyclic variation in heart rate in the heart rate series as a result of the analysis in the time domain, and outputting, over an output, a report characterizing a sleep disorder event based on the detection of the cyclic variation in heart rate in the heart rate series. The cyclic variation in heart rate is indicative of a sleep disorder.

Claims

1. An apparatus comprising one or more machine-readable data storage media storing instructions operable to cause one or more data processing machines to perform operations, the operations comprising:

receiving machine-readable heart rate information characterizing a heart rate of a monitored individual over a first interval;

detecting at least one of bradycardia and a cyclic variation in heart rate in the heart rate information during the first interval;

determining whether obstructive sleep apnea is indicated based at least on the detected at least one of bradycardia and cyclic variation in heart rate; and

selectively reporting an obstructive sleep apnea event in response to the determination that sleep apnea is indicated, wherein the obstructive sleep apnea event is a second interval when the information content of the received heart rate information is indicative of obstructive sleep apnea.

2. The apparatus of claim 1, wherein determining that obstructive sleep apnea is indicated comprises smoothing measures of the cyclic variation in heart rate over a detection window.

3. The apparatus of claim 2, wherein reporting the obstructive sleep apnea event comprises reporting information characterizing an aspect of the physiological condition of the monitored individual outside of the detection window.

4. The apparatus of claim 1, wherein reporting the obstructive sleep apnea event comprises transmitting information characterizing an aspect of the physiological condition of the monitored individual to a remote medical receiver.

5. The apparatus of claim 1, wherein reporting the obstructive sleep apnea event comprises reporting information characterizing aspects of the physiological condition of the monitored individual other than the heart.

6. A machine-implemented method comprising:

analyzing heart rate series of a monitored individual in the time domain using one or more digital data processing devices;

detecting a cyclic variation in heart rate in the heart rate series as a result of the analysis in the time domain, wherein the cyclic variation in heart rate is indicative of a sleep disorder; and

outputting, over an output, a report characterizing a sleep disorder event based on the detection of the cyclic variation in heart rate in the heart rate series.

7. The method of claim 6, further comprising:

establishing cyclic variation in heart rate indices from the heart rate series; and

establishing tachycardia indices from the heart rate series; and

establishing bradycardia indices from the heart rate series.

8. The method of claim 7, further comprising determining that periodic limb movement sleep disorders are indicated based on tachycardia indices indicating that tachycardia is present, bradycardia indices indicating that bradycardia is not present, and cyclic variation in heart rate indices indicating that cyclic variation in heart rate is present.

9. The method of claim 7, further comprising determining that obstructive sleep apneas are indicated based on tachycardia indices indicating that tachycardia is not present, bradycardia indices indicating that bradycardia is present, and cyclic variation in heart rate indices indicating that cyclic variation in heart rate is present.

10. The method of claim 7, further comprising determining that repeated central apneas or Cheyne-Stokes respiration are indicated based on tachycardia indices indicating that tachycardia is present, bradycardia indices indicating that bradycardia is present, and cyclic variation in heart rate indices indicating that cyclic variation in heart rate is present.

11. The method of claim 7, further comprising determining that repeated central apneas or Cheyne-Stokes respiration are indicated based on tachycardia indices indicating that tachycardia is not present, bradycardia indices indicating that bradycardia is not present, and cyclic variation in heart rate indices indicating that cyclic variation in heart rate is present.

12. The method of claim 7, further comprising progressively lowering thresholds for one or more of the cyclic variation in heart rate indices, the tachycardia indices, and the bradycardia indices until one of periodic limb movement sleep disorders, obstructive sleep apneas, or repeated central apneas or Cheyne-Stokes respiration is indicated.

13. The method of claim 6, wherein analyzing the heart rate series comprises autocorrelating the heart rate series.

14. The method of claim 9, wherein detecting the cyclic variation in heart rate comprises scoring autocorrelation measures to characterize a likelihood that the autocorrelation measures indicate a sleep disorder, wherein the measures include a number of zero crossings of an autocorrelated heart rate series, distances between adjacent zero crossings, and a standard deviation of the distances between adjacent zero crossings.

15. The method of claim 14, wherein the measures further include a measure f given by:

16. The method of claim 14, wherein detecting the cyclic variation in heart rate further comprises scoring measures of the heart rate series to characterize the likelihood that the heart rate series measures are indicative of a sleep disorder.

17. The method of claim 6, wherein reporting the sleep disorder event comprises reporting information characterizing aspects of the physiological condition of the monitored individual other than the heart,

18. The method of claim 6, wherein reporting the sleep disorder event comprises reporting information characterizing movement of a part of the body other than the heart.

19. The method of claim 6, wherein reporting the sleep disorder event comprises reporting an obstructive sleep apnea event based exclusively on the detection of the cyclic variation in heart rate in the heart rate series.

20. A system comprising:

an electrocardiograph configured to generate an electrocardiogram of a monitored individual;

a data processing device configured to detect a sleep disorder of the monitored individual based exclusively on the electrocardiogram; and

a transmitter configured to report information characterizing the physiological condition of the monitored individual during the detected sleep disorder.

21. The system of claim 20, wherein the data processing device is configured to detect the sleep disorder by analyzing a heart rate series derived from the electrocardiogram in the time domain.

22. The system of claim 21, wherein the data processing device comprises an autocorrelator configured to cross correlate the heart rate series with itself.

23. The system of claim 22, wherein the data processing device further comprises a scoring unit to score measures of the autocorrelation of the heart rate series to characterize the likelihood that the autocorrelation measures are indicative of the sleep disorder.

24. The system of claim 23, wherein the autocorrelation measures include a number of zero crossings of an autocorrelated heart rate series, distances between adjacent zero crossings, and a standard deviation of the distances between adjacent zero crossings.

25. The system of claim 23, wherein the autocorrelation measures include a measure f given by:

26. The system of claim 22, wherein the scoring unit also scores measures of the heart rate series to characterize the likelihood that the heart rate series measures are indicative of obstructive sleep apnea.

27. The system of claim 20, wherein the system further comprises one or more additional monitoring devices configured to generate information characterizing aspects of the physiological condition of a monitored individual other than activity of the heart.

28. The system of claim 27, wherein the transmitter is also configured to report the information generated by the additional monitoring devices.

29. The system of claim 20, wherein the system further comprises a beat detector.

30. The system of claim 20, wherein the electrocardiograph comprises a patient portable sensing unit.