RESPIRATION-BASED PROGNOSIS OF HEART DISEASE

• US 20070213622A1
• Filed: 05/17/2007
• Published: 09/13/2007
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: Widemed Technologies Ltd.
• Original Assignee: Widemed Ltd.
• Inventors: Reisfeld, Daniel
• Granted Patent: US 7,803,119 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 includes receiving a signal associated with respiration of a patient during sleep, and processing the signal to detect occurrences of a pattern corresponding to multiple cycles of periodic breathing. A cumulative duration of the occurrences is computed, and a prognosis of the patient is determined based on the cumulative duration.

Claims

1. A method for diagnosis, comprising:

receiving a signal associated with respiration of a patient during sleep;

processing the signal to detect occurrences of a pattern corresponding to multiple cycles of periodic breathing;

computing a cumulative duration of the occurrences; and

determining a prognosis of the patient based on the cumulative duration.

2. The method according to claim 1, wherein determining the prognosis comprises determining the prognosis of heart failure (HF) in the patient.

3. The method according to claim 2, wherein determining the prognosis comprises predicting a probability of survival of the patient.

4. The method according to claim 2, wherein determining the prognosis comprises predicting a level of at least one marker, selected from a group of markers consisting of Brain Natriuretic Peptide (BNP) and N-terminal prohormone Brain Natriuretic Peptide (NT proBNP).

5. The method according to claim 2, wherein determining the prognosis comprises differentiating compensated from decompensated heart failure.

6. The method according to claim 2, wherein determining the prognosis comprises predicting a hospitalization of the patient.

7. The method according to claim 2, wherein determining the prognosis comprises providing an indication of a need for a change in treatment of the patient.

8. The method according to claim 1, wherein the signal comprises a photoplethysmograph signal.

9. The method according to claim 8, and comprising processing the photoplethysmograph signal so as to identify a cardiac arrhythmia of the patient.

10. The method according to claim 8, wherein determining the prognosis comprises finding the prognosis responsively both to the occurrences of the pattern and to the cardiac arrhythmia.

11. The method according to claim 8, and comprising processing the photoplethysmograph signal so as to measure a vasomodulation in the body.

12. The method according to claim 8, and comprising processing the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

13. The method according to claim 8, wherein receiving the signal comprises receiving the photoplethysmograph signal from a sensor on a finger of the patient, and wherein processing the signal comprises digitizing and filtering the signal so as to generate data using processing circuitry in a control unit that is fastened to a forearm of the patient, and storing the data in a memory in the control unit.

14. The method according to claim 13, wherein processing the signal comprises connecting the control unit to an external processor and uploading the data from the memory to the external processor for analysis, and wherein storing the data comprises receiving and recording the data in the control unit while the control unit is disconnected from the external processor.

15. The method according to claim 8, wherein receiving the signal comprises receiving the photoplethysmograph signal from a sensor implanted in a body of the patient.

16. The method according to claim 1, and comprising treating the patient so as to reduce the cumulative duration.

17. The method according to claim 1, wherein determining the prognosis comprises classifying the patient in a high-mortality group if the cumulative duration exceeds a predetermined threshold.

18. The method according to claim 17, wherein the threshold is at least 45 minutes.

19. The method according to claim 1, wherein processing the signal comprises identifying a periodic breathing event when an event duration of the multiple cycles is no less than a predetermined minimum duration.

20. The method according to claim 1, wherein processing the signal comprises identifying a periodic breathing event when a number of the multiple cycles is no less than a predetermined minimum number.

21. The method according to claim 20, wherein the minimum number is three.

22. The method according to claim 1, wherein the signal is indicative of blood oxygen saturation.

23. The method according to claim 22, wherein processing the signal comprises identifying a periodic breathing event when a representative level of the blood oxygen saturation during the cycles is less than the blood oxygen saturation before the cycles by at least a predetermined amount.

24. The method according to claim 23, wherein the representative level of the blood oxygen saturation is one of a median saturation, a mean saturation, and a minimum saturation during the cycles.

25. The method according to claim 23, wherein the periodic breathing event is identified when the representative level of the blood oxygen saturation is less than the blood oxygen saturation before the cycles by at least 2%.

26. The method according to claim 1, wherein the periodic breathing comprises Cheyne-Stokes breathing.

27. The method according to claim 26, wherein processing the signal comprises distinguishing the Cheyne-Stokes breathing events from obstructive apnea events.

28. The method according to claim 27, wherein distinguishing the Cheyne-Stokes breathing events comprises determining a slope of the signal, and distinguishing the Cheyne-Stokes breathing events responsively to the slope.

29. The method according to claim 28, wherein determining the slope comprises finding a measure of the slope selected from a group of measures consisting of a median slope and a mean slope, and distinguishing the Cheyne-Stokes breathing events based on a maximal value of the measure of the slope in each of the cycles.

30. The method according to claim 29, wherein distinguishing the Cheyne-Stokes breathing events comprises classifying a breathing event as Cheyne-Stokes only when the maximal value of the measure is no greater than 0.7%/sec.

31. The method according to claim 1, wherein processing the signal comprises extracting from the signal a measure of respiratory energy, and filtering the measure so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient.

32. The method according to claim 1, wherein receiving the signal comprises monitoring the respiration using a non-contact sensor.

33. Apparatus for diagnosis, comprising:

a sensor, which is configured to be coupled to a body of a patient during sleep and to output a signal associated with respiration of the patient; and

a processor, which is coupled to process the signal to detect occurrences of a pattern corresponding to multiple cycles of periodic breathing, to compute a cumulative duration of the occurrences.

34. The apparatus according to claim 33, wherein the processor is configured to output an indication of a prognosis of heart failure (HF) in the patient based on the cumulative duration.

35. The apparatus according to claim 34, wherein the prognosis comprises a prediction of a probability of survival of the patient.

36. The apparatus according to claim 34, wherein the prognosis comprises a prediction of a level of at least one marker, selected from a group of markers consisting of Brain Natriuretic Peptide (BNP) and N-terminal prohormone Brain Natriuretic Peptide (NT proBNP).

37. The apparatus according to claim 34, wherein the prognosis differentiates compensated from decompensated heart failure.

38. The apparatus according to claim 34, wherein the prognosis comprises a prediction of a hospitalization of the patient.

39. The apparatus according to claim 34, wherein the prognosis comprises an indication of a need for a change in treatment of the patient.

40. The apparatus according to claim 33, wherein the signal comprises a photoplethysmograph signal.

41. The apparatus according to claim 40, wherein the processor is configured to process the photoplethysmograph signal so as to identify a cardiac arrhythmia of the patient.

42. The apparatus according to claim 40, wherein the processor is configured to output an indication of a prognosis of the patient responsively both to the occurrences of the pattern and to the cardiac arrhythmia.

43. The apparatus according to claim 40, wherein the processor is configured to process the photoplethysmograph signal so as to measure a vasomodulation in the body.

44. The apparatus according to claim 40, wherein the processor is configured to process the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

45. The apparatus according to claim 40, wherein the sensor is configured to be attached to a finger of the patient, and wherein the apparatus comprises a control unit, which contains the processor and a memory, and which is configured to be fastened to a forearm of the patient, and wherein the processor is configured to digitize and filter the signal so as to generate data and to store the data in the memory in the control unit.

46. The apparatus according to claim 45, wherein the control unit is configured to be connected to an external processor and to upload the data from the memory to the external processor for analysis, and wherein the processor is configured to receive and record the data in the memory while the control unit is disconnected from the external processor.

47. The apparatus according to claim 40, wherein the sensor is configured to be implanted in a body of the patient.

48. The apparatus according to claim 33, wherein the processor is configured to classify the patient in a high-mortality group if the cumulative duration exceeds a predetermined threshold.

49. The apparatus according to claim 48, wherein the threshold is at least 45 minutes.

50. The apparatus according to claim 33, wherein the processor is configured to identify a periodic breathing event when an event duration of the multiple cycles is no less than a predetermined minimum duration.

51. The apparatus according to claim 33, wherein the processor is configured to identify a periodic breathing event when a number of the multiple cycles is no less than a predetermined minimum number.

52. The apparatus according to claim 51, wherein the minimum number is three.

53. The apparatus according to claim 33, wherein the signal is indicative of blood oxygen saturation.

54. The apparatus according to claim 53, wherein the processor is configured to identify a periodic breathing event when a representative level of the blood oxygen saturation during the cycles is less than the blood oxygen saturation before the cycles by at least a predetermined amount.

55. The apparatus according to claim 54, wherein the representative level of the blood oxygen saturation is one of a median saturation, a mean saturation, and a minimum saturation during the cycles.

56. The apparatus according to claim 54, wherein the periodic breathing event is identified when the representative level of the blood oxygen saturation is less than the blood oxygen saturation before the cycles by at least 2%.

57. The apparatus according to claim 33, wherein the periodic breathing comprises Cheyne-Stokes breathing.

58. The apparatus according to claim 57, wherein the processor is configured to distinguish the Cheyne-Stokes breathing events from obstructive apnea events.

59. The apparatus according to claim 58, wherein the processor is configured to determine a slope of the signal, and to distinguish the Cheyne-Stokes breathing events responsively to the slope.

60. The apparatus according to claim 59, wherein the processor is configured to find a measure of the slope selected from a group of measures consisting of a median slope and a mean slope, and to distinguish the Cheyne-Stokes breathing events based on a maximal value of the measure of the slope in each of the cycles.

61. The apparatus according to claim 60, wherein the processor is configured to classify a breathing event as Cheyne-Stokes only when the maximal value of the measure is no greater than 0.7%/sec.

62. The apparatus according to claim 33, wherein the processor is configured to extract from the signal a measure of respiratory energy, and to filter the measure so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient.

63. The apparatus according to claim 33, wherein receiving the sensor comprises a non-contact sensor.

64. A computer software 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 signal associated with respiration of a patient during sleep, and to process the signal to detect occurrences of a pattern corresponding to multiple cycles of periodic breathing, to compute and output a cumulative duration of the occurrences.

65. The product according to claim 64, wherein the instructions cause the computer to output an indication of a prognosis of heart failure (HF) in the patient based on the cumulative duration.

66. The product according to claim 65, wherein the prognosis comprises a prediction of a probability of survival of the patient.

67. The product according to claim 65, wherein the prognosis comprises a prediction of a level of at least one marker, selected from a group of markers consisting of Brain Natriuretic Peptide (BNP) and N-terminal prohormone Brain Natriuretic Peptide (NT proBNP).

68. The product according to claim 65, wherein the prognosis differentiates compensated from decompensated heart failure.

69. The product according to claim 65, wherein the prognosis comprises a prediction of a hospitalization of the patient.

70. The product according to claim 65, wherein the prognosis comprises an indication of a need for a change in treatment of the patient.

71. The product according to claim 64, wherein the signal comprises a photoplethysmograph signal.

72. The product according to claim 71, wherein the instructions cause the computer to process the photoplethysmograph signal so as to identify a cardiac arrhythmia of the patient.

73. The product according to claim 71, wherein the instructions cause the computer to output an indication of a prognosis of the patient responsively both to the occurrences of the pattern and to the cardiac arrhythmia.

74. The product according to claim 71, wherein the instructions cause the computer to process the photoplethysmograph signal so as to measure a vasomodulation in the body.

75. The product according to claim 71, wherein the instructions cause the computer to process the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

76. The product according to claim 64, wherein the instructions cause the computer to classify the patient in a high-mortality group if the cumulative duration exceeds a predetermined threshold.

77. The product according to claim 76, wherein the threshold is at least 45 minutes.

78. The product according to claim 64, wherein the instructions cause the computer to identify a periodic breathing event when an event duration of the multiple cycles is no less than a predetermined minimum duration.

79. The product according to claim 64, wherein the instructions cause the computer to identify a periodic breathing event when a number of the multiple cycles is no less than a predetermined minimum number.

80. The product according to claim 79, wherein the minimum number is three.

81. The product according to claim 64, wherein the signal is indicative of blood oxygen saturation.

82. The product according to claim 81, wherein the instructions cause the computer to identify a periodic breathing event when a representative level of the blood oxygen saturation during the cycles is less than the blood oxygen saturation before the cycles by at least a predetermined amount.

83. The product according to claim 81, wherein the representative level of the blood oxygen saturation is one of a median saturation, a mean saturation, and a minimum saturation during the cycles.

84. The product according to claim 81, wherein the periodic breathing event is identified when the representative level of the blood oxygen saturation is less than the blood oxygen saturation before the cycles by at least 2%.

85. The product according to claim 64, wherein the periodic breathing comprises Cheyne-Stokes breathing.

86. The product according to claim 85, wherein the instructions cause the computer to distinguish the Cheyne-Stokes breathing events from obstructive apnea events.

87. The product according to claim 86, wherein the instructions cause the computer to determine a slope of the signal, and to distinguish the Cheyne-Stokes breathing events responsively to the slope.

88. The product according to claim 87, wherein the instructions cause the computer to find a measure of the slope selected from a group of measures consisting of a median slope and a mean slope, and to distinguish the Cheyne-Stokes breathing events based on a maximal value of the measure of the slope in each of the cycles.

89. The product according to claim 88, wherein the instructions cause the computer to classify a breathing event as Cheyne-Stokes only when the maximal value of the measure is no greater than 0.7%/sec.

90. The product according to claim 64, wherein the instructions cause the computer to extract from the signal a measure of respiratory energy, and to filter the measure so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient.