Sleep staging based on cardio-respiratory signals

• US 20060111635A1
• Filed: 11/22/2004
• Published: 05/25/2006
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: Widemed Technologies Ltd.
• Original Assignee: Widmed Ltd.
• Inventors: Reisfeld, Daniel, Todros, Koby, Geva, Amir
• Granted Patent: US 7,578,793 B2
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/082 Evaluation by breath analysis, e.g. determination of the chemical composition of exhaled breath A61B5/083, A61B5/091 take precedence
• CPC Class Codes: A61B 5/1135 by monitoring thoracic expansion
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6826 Finger
• CPC Class Codes: A61B 5/6838 Clamps or clips
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• 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
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method for diagnosis of a sleep-related condition of a patient having a thorax. The method includes receiving physiological signals from sensors coupled to the thorax of the patient, and analyzing the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

Claims

1. A method for diagnosis of a sleep-related condition of a patient having a thorax, the method comprising:

receiving physiological signals from sensors coupled to the thorax of the patient; and

analyzing the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

2. The method according to claim 1, wherein analyzing the physiological signals comprises detecting motion of the patient based on at least one of the physiological signals.

3. The method according to claim 2, wherein the at least one of the physiological signals comprises at least one of an electrocardiogram (ECG) signal and a respiration signal.

4. The method according to claim 2, wherein detecting the motion comprises:

measuring an energy of the at least one of the physiological signals in a selected frequency band as a function of time;

finding a respective characteristic of the energy in each of a plurality of time segments; and

determining the patient to have moved during one or more of the time segments responsively to the respective variance.

5. The method according to claim 4, wherein finding the respective characteristic comprises finding a respective variance of each of the time segments.

6. The method according to claim 4, wherein finding the respective characteristic comprises performing an adaptive segmentation in order to identify the time segments such that the energy of the at least one of the signals is quasi-stationary during each of the time segments.

7. The method according to claim 2, wherein analyzing the physiological signals comprises distinguishing, responsively to detecting the motion, between a waking stage and a REM sleep stage.

8. The method according to claim 1, wherein analyzing the physiological signals comprises:

performing an adaptive segmentation of at least one of the signals so as to identify time segments in which a characteristic of the at least one of the signals is quasi-stationary; and

based on the adaptive segmentation, identifying transient events during which the characteristic of the at least one of the signals is not quasi-stationary.

9. The method according to claim 8, wherein analyzing the physiological signals comprises determining at least one of the sleep stages to have been disturbed by occurrence of the transient events during the at least one of the sleep stages.

10. The method according to claim 1, wherein receiving the physiological signals comprises receiving an electrocardiogram (ECG) signal.

11. The method according to claim 10, wherein analyzing the physiological signals comprises measuring a variability of a heart rate of the patient responsively to the ECG signal, and identifying at least one of the sleep stages based on the variability.

12. The method according to claim 11, wherein identifying the at least one of the sleep stages comprises computing a variance associated with the variability of the heart rate, and finding, responsively to the variance, a period during which the heart rate was decoupled from a respiratory function of the patient.

13. The method according to claim 12, wherein identifying the period comprises classifying the period as a REM sleep period.

14. The method according to claim 11, wherein identifying the at least one of the sleep stages comprises measuring first and second energies respectively contained in first and second frequency bands of the variability of the heart rate during a selected epoch, and classifying the sleep stages responsively to a function of the first and second energies.

15. The method according to claim 14, wherein the function comprises a ratio of the first and second energies.

16. The method according to claim 14, wherein the first and second frequency bands respectively comprise low and high frequency bands, and wherein classifying the sleep stages comprises distinguishing between light and deep sleep stages based on the function.

17. The method according to claim 10, wherein receiving the physiological signals comprises receiving a respiration signal, and wherein analyzing the physiological signals comprises analyzing the respiration signal together with the ECG signal in order to identify the sleep stages.

18. The method according to claim 10, and comprising receiving a respiration signal from an airway of the patient, wherein analyzing the physiological signals comprises analyzing the respiration signal together with the ECG signal in order to identify the sleep stages.

19. The method according to claim 1, wherein receiving the physiological signals comprises receiving a respiration signal.

20. The method according to claim 19, wherein analyzing the physiological signals comprises evaluating a complexity of the respiration signal during a selected epoch, and identifying at least one of the sleep stages responsively to the complexity.

21. The method according to claim 20, wherein evaluating the complexity comprises quantizing and compressing the respiration signal, and measuring the complexity based on an efficiency of compression of the quantized respiration signal.

22. The method according to claim 20, wherein identifying the at least one of the sleep states comprises determining the patient to be in NREM sleep if the complexity is below a predetermined threshold.

23. The method according to claim 1, wherein receiving the physiological signals comprises collecting the physiological signals at a bedside of the patient, and wherein analyzing the physiological signals comprises transmitting the physiological signals over a communication network for processing by a diagnostic processor remote from the bedside.

24. The method according to claim 1, wherein receiving the physiological signals comprises collecting the physiological signals from a Holter monitor coupled to the patient.

25. The method according to claim 1, wherein receiving the physiological signals comprises collecting the physiological signals from a device implanted in the thorax of the patient.

26. A method for diagnosis of a sleep-related condition of a patient, the method comprising:

receiving at least one of an electrocardiogram (ECG) signal and a respiration signal from a sensor coupled to the patient during sleep;

measuring an energy of the at least one of the ECG and respiration signals in a selected frequency band as a function of time;

finding a respective characteristic of the energy in each of a plurality of time segments; and

determining the patient to have moved during one or more of the time segments responsively to the respective variance.

27. The method according to claim 26, wherein finding the respective characteristic comprises finding a respective variance of the energy.

28. The method according to claim 26, and comprising identifying a sleep stage of the patient during the one or more of the time segments responsively to determining the patient to have moved.

29. The method according to claim 28, wherein identifying the sleep stage comprises distinguishing a REM sleep stage from a waking stage.

30. The method according to claim 28, and comprising receiving an electroencephalogram (EEG) signal from the patient, wherein identifying the sleep stage comprises processing the EEG signal together with the at least one of the ECG and respiration signals.

31. A method for diagnosis of a sleep-related condition of a patient, the method comprising:

receiving an electrocardiogram (ECG) signal from a sensor coupled to the patient during sleep;

measuring a variability of a heart rate of the patient responsively to the ECG signal;

computing a characteristic of the variability of the heart rate; and

finding, responsively to the variance, a period during which the heart rate was decoupled from a respiratory function of the patient.

32. The method according to claim 31, wherein computing the characteristic comprises determining a variance associated with the variability of the heart rate.

33. The method according to claim 31, and comprising identifying a sleep stage of the patient during the period based on decoupling of the heart rate from the respiratory function.

34. The method according to claim 33, wherein identifying the sleep stage comprises classifying the period as a REM sleep period.

35. The method according to claim 31, and comprising receiving an electroencephalogram (EEG) signal from the patient, wherein identifying the sleep stage comprises processing the EEG signal together with the ECG signal.

36. A method for diagnosis of a sleep-related condition of a patient, the method comprising:

receiving a respiration signal from a sensor coupled to the patient during sleep;

evaluating a complexity of the respiration signal during a selected epoch; and

identifying a sleep stage of the patient responsively to the complexity.

37. The method according to claim 36, wherein evaluating the complexity comprises quantizing and compressing the respiration signal, and measuring the complexity based on an efficiency of compression of the quantized respiration signal.

38. The method according to claim 37, wherein identifying the sleep stage comprises determining the patient to be in NREM sleep if the complexity is below a predetermined threshold.

39. The method according to claim 37, and comprising receiving an electroencephalogram (EEG) signal from the patient, wherein identifying the sleep stage comprises processing the EEG signal together with the respiration signal.

40. Apparatus for diagnosis of a sleep-related condition of a patient having a thorax, the apparatus comprising:

one or more sensors, coupled to the thorax of the patient, which are adapted to receive physiological signals; and

a diagnostic processor, which is coupled to receive and process the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

41. The apparatus according to claim 40, wherein the diagnostic processor is adapted to detect motion of the patient based on at least one of the physiological signals.

42. The apparatus according to claim 41, wherein the at least one of the physiological signals comprises at least one of an electrocardiogram (ECG) signal and a respiration signal.

43. The apparatus according to claim 41, wherein the diagnostic processor is adapted to measure an energy of the at least one of the physiological signals in a selected frequency band as a function of time, to find a respective characteristic of the energy in each of a plurality of time segments, and to determine the patient to have moved during one or more of the time segments responsively to the respective variance.

44. The apparatus according to claim 43, wherein the respective characteristic comprises a respective variance of each of the time segments.

45. The apparatus according to claim 43, wherein the diagnostic processor is operative to perform an adaptive segmentation in order to identify the time segments such that the energy of the at least one of the signals is quasi-stationary during each of the time segments.

46. The apparatus according to claim 41, wherein the diagnostic processor is adapted to distinguish, responsively to detecting the motion, between a waking stage and a REM sleep stage.

47. The apparatus according to claim 40, wherein the diagnostic processor is adapted to perform an adaptive segmentation of at least one of the signals so as to identify time segments in which a characteristic of the at least one of the signals is quasi-stationary, and based on the adaptive segmentation, to identify transient events during which the characteristic of the at least one of the signals is not quasi-stationary.

48. The apparatus according to claim 47, wherein the diagnostic processor is adapted to determine at least one of the sleep stages to have been disturbed by occurrence of the transient events during the at least one of the sleep stages.

49. The apparatus according to claim 40, wherein the physiological signals comprise an electrocardiogram (ECG) signal.

50. The apparatus according to claim 49, wherein the diagnostic processor is adapted to measure a variability of a heart rate of the patient responsively to the ECG signal, and to identify at least one of the sleep stages based on the variability.

51. The apparatus according to claim 50, wherein the diagnostic processor is adapted to compute a variance associated with the variability of the heart rate, and to find, responsively to the variance, a period during which the heart rate was decoupled from a respiratory function of the patient.

52. The apparatus according to claim 51, wherein the diagnostic processor is adapted to classify the period as a REM sleep period responsively to decoupling of the heart rate from the respiratory function.

53. The apparatus according to claim 50, wherein the diagnostic processor is adapted to measure first and second energies respectively contained in first and second frequency bands of the variability of the heart rate during a selected epoch, and to classify the sleep stages responsively to a function of the first and second energies.

54. The apparatus according to claim 53, wherein the function comprises a ratio of the first and second energies.

55. The apparatus according to claim 53, wherein the first and second frequency bands respectively comprise low and high frequency bands, and wherein the diagnostic processor is adapted to distinguish between light and deep sleep stages based on the function.

56. The apparatus according to claim 49, wherein the physiological signals comprise a respiration signal, and wherein the diagnostic processor is adapted to analyze the respiration signal together with the ECG signal in order to identify the sleep stages.

57. The apparatus according to claim 49, wherein the diagnostic processor is coupled to receive a respiration signal from an airway sensor in an airway of the patient, and wherein the diagnostic processor is adapted to analyze the respiration signal together with the ECG signal in order to identify the sleep stages.

58. The apparatus according to claim 40, wherein the physiological signals comprise a respiration signal.

59. The apparatus according to claim 58, wherein the diagnostic processor is adapted to evaluate a complexity of the respiration signal during a selected epoch, and to identify at least one of the sleep stages responsively to the complexity.

60. The apparatus according to claim 59, wherein the diagnostic processor is adapted to quantize and compress the respiration signal, and to measure the complexity based on an efficiency of compression of the quantized respiration signal.

61. The apparatus according to claim 59, wherein the diagnostic processor is adapted to determine the patient to be in NREM sleep if the complexity is below a predetermined threshold.

62. The apparatus according to claim 40, and comprising a console, which is coupled to collect the physiological signals at a bedside of the patient, and to transmit the physiological signals over a communication network for processing by the diagnostic processor at a location remote from the bedside.

63. The apparatus according to claim 40, wherein the diagnostic processor is coupled to receive the physiological signals from a Holter monitor coupled to the patient.

64. The apparatus according to claim 40, wherein the diagnostic processor is coupled to receive the physiological signals from a device implanted in the thorax of the patient.

65. Apparatus for diagnosis of a sleep-related condition of a patient, the apparatus comprising:

a sensor, which is adapted to be coupled to the patient during sleep so as to receive from the patient at least one of an electrocardiogram (ECG) signal and a respiration signal; and

a diagnostic processor, which is coupled to measure an energy of the at least one of the ECG and respiration signals in a selected frequency band as a function of time, to find a respective characteristic of the energy in each of a plurality of time segments, and to determine the patient to have moved during one or more of the time segments responsively to the respective variance.

66. The apparatus according to claim 65, wherein the characteristic comprises a respective variance of the energy in each of the plurality of time segments.

67. The apparatus according to claim 65, wherein the diagnostic processor is adapted to identify a sleep stage of the patient during the one or more of the time segments responsively to determining the patient to have moved.

68. The apparatus according to claim 67, wherein the diagnostic processor is adapted to distinguish a REM sleep stage from a waking stage.

69. The apparatus according to claim 67, wherein the diagnostic processor is coupled to receive an electroencephalogram (EEG) signal from the patient, and to process the EEG signal together with the at least one of the ECG and respiration signals.

70. Apparatus for diagnosis of a sleep-related condition of a patient, the apparatus comprising:

one or more electrodes, which are adapted to receive an electrocardiogram (ECG) signal from the patient during sleep; and

a diagnostic processor, which is coupled to measure a variability of a heart rate of the patient responsively to the ECG signal, to compute a characteristic of the variability of the heart rate, and to find, responsively to the variance, a period during which the heart rate was decoupled from a respiratory function of the patient.

71. The apparatus according to claim 70, wherein the characteristic comprises a respective variance associated with the variability of the heart rate.

72. The apparatus according to claim 70, wherein the diagnostic processor is adapted to identify a sleep stage of the patient during the period based on decoupling of the heart rate from the respiratory function.

73. The apparatus according to claim 72, wherein the processor is adapted to classify the period as a REM sleep period based on the decoupling.

74. The apparatus according to claim 70, wherein the diagnostic processor is coupled to receive an electroencephalogram (EEG) signal from the patient, and to process the EEG signal together with the ECG signal.

75. Apparatus for diagnosis of a sleep-related condition of a patient, the apparatus comprising:

a respiration sensor, which is adapted to receive a respiration signal from the patient during sleep; and

a diagnostic processor, which is coupled to evaluate a complexity of the respiration signal during a selected epoch, and to identify a sleep stage of the patient responsively to the complexity.

76. The apparatus according to claim 75, wherein the diagnostic processor is adapted to quantize and compress the respiration signal, and to measure the complexity based on an efficiency of compression of the quantized respiration signal.

77. The apparatus according to claim 76, wherein the diagnostic processor is adapted to determine the patient to be in NREM sleep if the complexity is below a predetermined threshold.

78. The apparatus according to claim 75, wherein the diagnostic processor is coupled to receive an electroencephalogram (EEG) signal from the patient, and to process the EEG signal together with the respiration signal.

79. A computer software product for diagnosis of a sleep-related condition of a patient having a thorax, the product comprising a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive physiological signals from one or more sensors coupled to the thorax of the patient during sleep, and to process the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

80. A computer software product for diagnosis of a sleep-related condition of a patient, the product comprising a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive from the patient at least one of an electrocardiogram (ECG) signal and a respiration signal during sleep, and to measure an energy of the at least one of the ECG and respiration signals in a selected frequency band as a function of time, to find a respective variance of the energy in each of a plurality of time segments, and to determine the patient to have moved during one or more of the time segments responsively to the respective variance.

81. A computer software product for diagnosis of a sleep-related condition of a patient, the product comprising a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive an electrocardiogram (ECG) signal from the patient during sleep, and to measure a variability of a heart rate of the patient responsively to the ECG signal, to compute a variance associated with the variability of the heart rate, and to find, responsively to the variance, a period during which the heart rate was decoupled from a respiratory function of the patient.

82. A computer software product for diagnosis of a sleep-related condition of a patient, the product comprising a computer-readable medium, in which program instructions are stored, which instructions, when read by a computer, cause the computer to receive a respiration signal from the patient during sleep, and to evaluate a complexity of the respiration signal during a selected epoch, and to identify a sleep stage of the patient responsively to the complexity.