Detection of heart failure using a photoplethysmograph

• US 7,803,118 B2
• Filed: 05/17/2007
• Issued: 09/28/2010
• Est. Priority Date: 11/22/2004
• Status: Active Grant

• Current Assignee: Widemed Technologies Ltd.
• Original Assignee: Widemed Ltd.
• Inventors: Sberro, Hila, Reisfeld, Daniel
• Primary Examiner(s): Marmor, II; Charles A.
• Assistant Examiner(s): Natnithithadha; Navin
• Application Number:
  US11/750,222
• Publication Number:
  US 20070213621A1
• Time in Patent Office: 1,230 Days
• Field of Search: 600529-543, 600473-481, 600/483, 600/484, 600500-504, 600/323, 600/324, 600/326
• US Class Current: 600/483
• 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 blood oxygen saturation of a patient during sleep. The signal is filtered so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient. The filtered signal is processed to detect a pattern corresponding to multiple cycles of periodic breathing.

Claims

1. A method for diagnosis, comprising:

receiving a signal associated with blood oxygen saturation of a patient during sleep;

filtering the signal so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient; and

operating a computer for processing the filtered signal to determine a slope of the filtered signal, to distinguish Cheyne-Stokes breathing events from obstructive apnea events responsively to the slope, and to detect a pattern corresponding to multiple cycles of periodic breathing based on the distinguished Cheyne-Stokes breathing events.

2. The method according to claim 1, wherein processing the filtered signal comprises detecting the signal components that have a period greater than a minimum period of at least 30 sec.

3. The method according to claim 2, wherein the minimum period is at least 55 sec.

4. The method according to claim 1, and comprising determining a prognosis of heart failure (HF) in the patient based on the occurrences of the pattern of Cheyne-Stokes breathing.

5. The method according to claim 4, wherein determining the prognosis comprises predicting a probability of survival of the patient.

6. The method according to claim 4, 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).

7. The method according to claim 4, wherein determining the prognosis comprises differentiating compensated from decompensated heart failure.

8. The method according to claim 4, wherein determining the prognosis comprises predicting a hospitalization of the patient.

9. The method according to claim 4, wherein determining the prognosis comprises providing an indication of a need for a change in treatment of the patient.

10. The method according to claim 4, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor coupled to a body of a patient, wherein the method comprises processing the photoplethysmograph signal so as to identify a cardiac arrhythmia of the patient.

11. The method according to claim 10, wherein determining the prognosis comprises finding the prognosis responsively both to the occurrences of the pattern of Cheyne-Stokes breathing and to the cardiac arrhythmia.

12. The method according to claim 4, wherein determining the prognosis comprises assessing a duration of the occurrences of a pattern of Cheyne-Stokes breathing.

13. The method according to claim 12, and comprising treating the patient so as to reduce the duration of the occurrences of the pattern of Cheyne-Stokes breathing.

14. The method according to claim 12, wherein assessing the duration comprises finding a cumulative duration of the occurrences of the pattern of Cheyne-Stokes breathing.

15. The method according to claim 14, wherein finding the cumulative duration comprises classifying the patient in a high-mortality group if the cumulative duration exceeds a predetermined threshold.

16. The method according to claim 15, wherein the threshold is at least 45 minutes.

17. The method according to claim 1, wherein detecting the occurrences comprises identifying a Cheyne-Stokes breathing event when a duration of the multiple cycles is no less than a predetermined minimum duration.

18. The method according to claim 1, wherein detecting the occurrences comprises identifying a Cheyne-Stokes breathing event when a number of the multiple cycles is no less than a predetermined minimum number.

19. The method according to claim 18, wherein the minimum number is three.

20. The method according to claim 1, 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.

21. The method according to claim 20, wherein the Cheyne-Stokes 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%.

22. The method according to claim 1, 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.

23. The method according to claim 22, 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.

24. The method according to claim 1, and comprising screening the patient for heart failure based on the occurrences of the pattern of Cheyne-Stokes breathing.

25. The method according to claim 1, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor coupled to a body of a patient, wherein the method comprises processing the photoplethysmograph signal so as to measure a heart rate of the patient.

26. The method according to claim 25, wherein processing the photoplethysmograph signal comprises identifying at least one premature ventricular contraction (PVC) in a heart rhythm of the patient.

27. The method according to claim 1, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor coupled to a body of the patient, wherein the method comprises processing the photoplethysmograph signal so as to measure a vasomodulation in the body.

28. The method according to claim 1, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor coupled to a body of the patient, wherein the method comprises processing the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

29. The method according to claim 1, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor on a finger of the patient, and wherein filtering 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.

30. The method according to claim 28, wherein processing the filtered 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.

31. The method according to claim 1, wherein receiving the signal comprises receiving a photoplethysmograph signal from a sensor implanted in a body of the patient.

32. 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 blood oxygen saturation in the body; and

a processor, which is coupled to filter the signal so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient, and to process the filtered signal to determine a slope of the filtered signal, to distinguish Cheyne-Stokes breathing events from obstructive apnea events responsively to the slope, and to detect a pattern corresponding to multiple cycles of periodic breathing based on the distinguished Cheyne-Stokes breathing events.

33. The apparatus according to claim 32, wherein the processor is configured to detect the pattern by detecting the signal components that have a period greater than a minimum period of at least 30 sec.

34. The apparatus according to claim 33, wherein the minimum period is at least 55 sec.

35. The apparatus according to claim 32, wherein the processor is configured to determine a prognosis of heart failure (HF) in the patient based on the occurrences of the pattern of Cheyne-Stokes breathing.

36. The apparatus according to claim 35, wherein the prognosis is predictive of a probability of survival of the patient.

37. The apparatus according to claim 35, wherein the processor is configured to predict, based on the occurrences of the pattern of Cheyne-Stokes breathing, 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).

38. The apparatus according to claim 35, wherein the processor is configured to differentiate compensated from decompensated heart failure based on the occurrences of the pattern of Cheyne-Stokes breathing.

39. The apparatus according to claim 35, wherein the processor is configured to predict a hospitalization of the patient based on the occurrences of the pattern of Cheyne-Stokes breathing.

40. The apparatus according to claim 35, wherein the processor is configured to provide an indication of a need for a change in treatment of the patient based on the occurrences of the pattern of Cheyne-Stokes breathing.

41. The apparatus according to claim 35, wherein the signal comprises a photoplethysmograph signal, and 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 41, wherein the processor is configured to determine the prognosis responsively both to the occurrences of the pattern of Cheyne-Stokes breathing and to the cardiac arrhythmia.

43. The apparatus according to claim 35, wherein the processor is configured to determine the prognosis by assessing a duration of the occurrences of a pattern of Cheyne-Stokes breathing.

44. The apparatus according to claim 43, wherein the processor is configured to find a cumulative duration of the occurrences of the pattern of Cheyne-Stokes breathing.

45. The apparatus according to claim 44, wherein the processor is configured to classify the patient in a high-mortality group if the cumulative duration exceeds a predetermined threshold.

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

47. The apparatus according to claim 32, wherein the processor is configured to identify a Cheyne-Stokes breathing event when a duration of the multiple cycles is no less than a predetermined minimum duration.

48. The apparatus according to claim 32, wherein the processor is configured to identify a Cheyne-Stokes breathing event when a number of the multiple cycles is no less than a predetermined minimum number.

49. The apparatus according to claim 48, wherein the minimum number is three.

50. The apparatus according to claim 32, wherein the processor is configured to identify a Cheyne-Stokes 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.

51. The apparatus according to claim 50, 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.

52. The apparatus according to claim 51, wherein the Cheyne-Stokes 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%.

53. The apparatus according to claim 32, 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.

54. The apparatus according to claim 53, 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.

55. The apparatus according to claim 32, wherein the processor is configured to screen the patient for heart failure based on the occurrences of the pattern of Cheyne-Stokes breathing.

56. The apparatus according to claim 32, wherein the signal comprises a photoplethysmograph signal, and wherein the processor is configured to process the photoplethysmograph signal so as to measure a heart rate of the patient.

57. The apparatus according to claim 56, wherein the processor is configured to identify at least one premature ventricular contraction (PVC) in a heart rhythm of the patient.

58. The apparatus according to claim 32, wherein the signal comprises a photoplethysmograph signal, and wherein the processor is configured to process the photoplethysmograph signal so as to measure a vasomodulation in the body.

59. The apparatus according to claim 32, wherein the signal comprises a photoplethysmograph signal, and wherein the processor is configured to process the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

60. The apparatus according to claim 32, wherein the sensor is configured to fit over a finger of the patient, and wherein the processor comprises a control unit, which is configured to be fastened to a forearm of the patient, and which is coupled to digitize and filter the signal so as to generate data indicative of the pattern, and which comprises a memory for storing the data.

61. The apparatus according to claim 60, wherein the control unit comprises an interface for connection to an external processor so as to upload the data from the memory to the external processor for analysis, and wherein the control unit is configured to receive and record the data while the control unit is disconnected from the external processor.

62. 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 blood oxygen saturation in a body or a patient, and to filter the signal so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient, and to process the filtered signal to determine a slope of the filtered signal, to distinguish Cheyne-Stokes breathing events from obstructive apnea events responsively to the slope, and to detect a pattern corresponding to multiple cycles of periodic breathing based on the distinguished Cheyne-Stokes breathing events.

63. The product according to claim 62, wherein the instructions cause the computer to determine a prognosis of heart failure (HF) in the patient based on the occurrences of the pattern of Cheyne-Stokes breathing.

64. The product according to claim 63, wherein the prognosis is predictive of a probability of survival of the patient.

65. The product according to claim 63, wherein the signal comprises a photoplethysmograph signal, and wherein the instructions cause the computer to process the photoplethysmograph signal so as to identify a cardiac arrhythmia of the patient.

66. The product according to claim 65, wherein the instructions cause the computer to determine the prognosis responsively both to the occurrences of the pattern of Cheyne-Stokes breathing and to the cardiac arrhythmia.

67. The product according to claim 62, wherein the instructions cause the computer to identify a Cheyne-Stokes breathing event when a duration of the multiple cycles is no less than a predetermined minimum duration.

68. The product according to claim 62, wherein the instructions cause the computer to identify a Cheyne-Stokes 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.

69. The product according to claim 62, wherein the signal comprises a photoplethysmograph signal, and wherein the instructions cause the computer to process the photoplethysmograph signal so as to measure a heart rate of the patient.

70. The product according to claim 69, wherein the instructions cause the computer to identify at least one premature ventricular contraction (PVC) in a heart rhythm of the patient.

71. The product according to claim 62, wherein the signal comprises a photoplethysmograph signal, and wherein the instructions cause the computer to process the photoplethysmograph signal so as to measure a vasomodulation in the body.

72. The product according to claim 62, wherein the signal comprises a photoplethysmograph signal, and wherein the instructions cause the computer to process the photoplethysmograph signal so as to measure a modulation of respiration by the patient.

73. A method for diagnosis, comprising:

receiving a signal associated with blood oxygen saturation of a patient during sleep;

filtering the signal so as to eliminate signal components at frequencies equal to and greater than a respiratory frequency of the patient;

operating a computer for processing the filtered signal to distinguish Cheyne-Stokes breathing events from obstructive apnea events and to detect a pattern corresponding to multiple cycles of periodic breathing based on the distinguished Cheyne-Stokes breathing events.

74. The method of claim 73, further comprising finding a cumulative duration of the occurrences of the pattern of Cheyne-Stokes breathing, thereby assessing a duration of said occurrences of said pattern of Cheyne-Stokes breathing.