SLEEP QUALITY INDICATORS

• US 20090292215A1
• Filed: 07/21/2005
• Published: 11/26/2009
• Est. Priority Date: 05/15/2003
• Status: Abandoned Application

• Current Assignee: Widemed Technologies Ltd.
• Original Assignee: Widemed Ltd.
• Inventors: Geva, Amir B., Levy, Baruch, Todros, Koby, Novodvorets, Alex
• Application Number:
  US11/572,481
• Publication Number:
  US 20090292215A1
• Time in Patent Office: Days
• US Class Current: 600/484
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A method for diagnosis includes acquiring a physiological signal from a patient (22) during a period of sleep, and segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum. Respective levels of membership of the segments in a plurality of frequency states are computed responsively to the respective frequency spectrum. Based on the respective levels of membership, a sleep quality indicator is determined and displayed, responsively to a statistical characteristic of the segments.

Claims

1. A method for diagnosis, comprising:

acquiring a physiological signal from a patient during a period of sleep;

segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum;

computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum; and

displaying a plot indicative of the levels of membership of the segments in the sequence over time.

2. The method according to claim 1, wherein computing the respective levels comprises applying fuzzy clustering to the segments so as to define the states.

3. The method according to claim 1, wherein displaying the plot comprises displaying a density plot, in which the levels of membership are represented by color variations.

4. The method according to claim 1, wherein displaying the plot comprises displaying an accumulation plot, showing cumulative levels of membership of the segments in the plurality of frequency states over the sequence.

5. The method according to claim 1, wherein displaying the plot comprises displaying an accumulation plot showing cumulative durations of the segments in each of the plurality of frequency states.

6. The method according to claim 5, and comprising determining and comparing respective accumulation rates of the cumulative durations in at least two of the frequency states.

7. The method according to claim 1, wherein displaying the plot comprises assigning each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum, and displaying an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

8. The method according to claim 1, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

9. A method for diagnosis, comprising:

acquiring a physiological signal from a patient during a period of sleep;

segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum;

computing a fundamental frequency of each segment in the time sequence responsively to a moment of the respective frequency spectrum of the segment; and

displaying a plot showing the fundamental frequency of the segments in the sequence over time.

10. The method according to claim 9, wherein displaying the plot comprises showing at least one of a trend and a variance of the fundamental frequency.

11. The method according to claim 9, wherein computing the respective levels comprises applying fuzzy clustering to the segments so as to define the states.

12. The method according to claim 9, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

13. A method for diagnosis, comprising:

acquiring a physiological signal from a patient during a period of sleep;

segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum;

computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum;

based on the respective levels of membership, determining a sleep quality indicator responsively to a statistical characteristic of the segments; and

displaying the sleep quality indicator.

14. The method according to claim 13, wherein computing the respective levels comprises applying fuzzy clustering to the segments so as to define the states.

15. The method according to claim 13, wherein the statistical characteristic comprises at least one duration measure selected from a group of duration measures consisting of:

a cumulative duration of the segments associated with each of the frequency clusters;

a relative duration of the segments associated with each of the frequency clusters;

a mean duration of the segments associated with each of the frequency clusters;

a variance of a duration of the segments associated with each of the frequency clusters;

a total number of the segments associated with each of the frequency clusters; and

a relative duration of the segments associated with each of the frequency clusters.

16. The method according to claim 13, and comprising assigning the segments to predefined sleep stages responsively to the frequency spectrum, wherein determining the sleep quality indicator comprises computing the statistical characteristic with respect to each of the sleep stages.

17. The method according to claim 13, wherein displaying the sleep quality indicator comprises displaying a plot indicative of the levels of membership of the segments in the sequence over time.

18. The method according to claim 13, wherein displaying the sleep quality indicator comprises displaying a plot showing a fundamental frequency of the segments in the sequence over time.

19. The method according to claim 13, wherein computing the respective levels of membership comprises assigning the segments in the time sequence to respective frequency states, and wherein determining the sleep quality indicator comprises computing probabilities of transition among the frequency states.

20. The method according to claim 13, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

21. The method according to claim 20, and comprising identifying transient phenomena in the EEG signal, and computing an index quantifying a frequency of occurrence of the transient phenomena.

22. The method according to claim 21, wherein the transient phenomena comprise one or more of K-complexes and spindles.

23. The method according to claim 13, wherein the physiological signal comprises a respiration signal.

24. The method according to claim 23, and comprising identifying respiratory events occurring during the period of sleep, and computing statistical characteristics of the respiratory events.

25. The method according to claim 24, wherein computing the statistical characteristics comprises computing and displaying a respiratory event histogram.

26. The method according to claim 24, and comprising measuring a heart rate of the patient, wherein computing the statistical characteristics comprises computing a relative heart rate index indicative of changes in the heart rate associated with the respiratory events.

27. The method according to claim 24, wherein computing the statistical characteristics comprises assigning respective confidence levels to the respiratory events, and displaying the confidence levels as a function of respiration state.

28. A method for diagnosis, comprising:

acquiring a physiological signal from a patient during a period of sleep;

segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum;

computing respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum;

responsively to the respective levels of membership, assigning each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum; and

displaying an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

29. The method according to claim 28, wherein computing the respective levels comprises applying fuzzy clustering to the segments so as to define the states.

30. The method according to claim 28, and comprising determining and comparing respective accumulation rates of the waking and sleep states.

31. The method according to claim 28, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

32. Diagnostic apparatus, comprising:

a sensor, which is adapted to acquire a physiological signal from a patient during a period of sleep; and

a diagnostic processor, which is adapted to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, and to display a plot indicative of the levels of membership of the segments in the sequence over time.

33. The apparatus according to claim 32, wherein the processor is adapted to apply fuzzy clustering to the segments so as to define the states.

34. The apparatus according to claim 32, wherein the plot comprises a density plot, in which the levels of membership are represented by color variations.

35. The apparatus according to claim 32, wherein the plot comprises an accumulation plot, showing cumulative levels of membership of the segments in the plurality of frequency states over the sequence.

36. The apparatus according to claim 32, wherein the plot comprises an accumulation plot showing cumulative durations of the segments in each of the plurality of frequency states.

37. The apparatus according to claim 36, wherein the processor is adapted to determine and output respective accumulation rates of the cumulative durations in at least two of the frequency states.

38. The apparatus according to claim 32, wherein the processor is adapted to assign each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum, and to display an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

39. The apparatus according to claim 32, wherein the sensor comprises at least one electrode, and wherein the physiological signal comprises an electroencephalogram (EEG) signal.

40. Diagnostic apparatus, comprising:

a sensor, which is adapted to acquire a physiological signal from a patient during a period of sleep; and

a diagnostic processor, which is adapted to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute a fundamental frequency of each segment in the time sequence responsively to a moment of the respective frequency spectrum of the segment, and to display a plot showing the fundamental frequency of the segments in the sequence over time.

41. The apparatus according to claim 40, wherein the processor is adapted to display in the plot at least one of a trend and a variance of the fundamental frequency.

42. The apparatus according to claim 40, wherein the processor is adapted to apply fuzzy clustering to the segments so as to define the states.

43. The apparatus according to claim 40, wherein the sensor comprises at least one electrode, and wherein the physiological signal comprises an electroencephalogram (EEG) signal.

44. Diagnostic apparatus, comprising:

a sensor, which is adapted to acquire a physiological signal from a patient during a period of sleep; and

a diagnostic processor, which is adapted to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, to determine, based on the respective levels of membership, a sleep quality indicator responsively to a statistical characteristic of the segments, and to display the sleep quality indicator.

45. The apparatus according to claim 44, wherein the processor is adapted to apply fuzzy clustering to the segments so as to define the states.

46. The apparatus according to claim 44, wherein the statistical characteristic comprises at least one duration measure selected from a group of duration measures consisting of:

a cumulative duration of the segments associated with each of the frequency clusters;

a relative duration of the segments associated with each of the frequency clusters;

a mean duration of the segments associated with each of the frequency clusters;

a variance of a duration of the segments associated with each of the frequency clusters;

a total number of the segments associated with each of the frequency clusters; and

a relative duration of the segments associated with each of the frequency clusters.

47. The apparatus according to claim 44, wherein the processor is adapted to assign the segments to predefined sleep stages responsively to the frequency spectrum, and to compute the statistical characteristic with respect to each of the sleep stages.

48. The apparatus according to claim 44, wherein the processor is adapted to display a plot indicative of the levels of membership of the segments in the sequence over time.

49. The apparatus according to claim 44, wherein the processor is adapted to display a plot showing a fundamental frequency of the segments in the sequence over time.

50. The apparatus according to claim 44, wherein the processor is adapted to assign the segments in the time sequence to respective frequency states, and to compute probabilities of transition among the frequency states.

51. The apparatus according to claim 44, wherein the sensor comprises at least one electrode, and wherein the physiological signal comprises an electroencephalogram (EEG) signal.

52. The apparatus according to claim 51, wherein the processor is adapted to identify transient phenomena in the EEG signal, and to compute an index quantifying a frequency of occurrence of the transient phenomena.

53. The apparatus according to claim 52, wherein the transient phenomena comprise one or more of K-complexes and spindles.

54. The apparatus according to claim 44, wherein the sensor comprises a respiration sensor, and wherein the physiological signal comprises a respiration signal.

55. The apparatus according to claim 54, wherein the processor is adapted to identify respiratory events occurring during the period of sleep, and to compute statistical characteristics of the respiratory events.

56. The apparatus according to claim 55, wherein the processor is adapted to compute and display a respiratory event histogram.

57. The apparatus according to claim 54, wherein the processor is adapted to determine a heart rate of the patient, and to compute a relative heart rate index indicative of changes in the heart rate associated with the respiratory events.

58. The apparatus according to claim 54, wherein the processor is adapted to assign respective confidence levels to the respiratory events, and to display the confidence levels as a function of respiration state.

59. Diagnostic apparatus, comprising:

a sensor, which is adapted to acquire a physiological signal from a patient during a period of sleep; and

a diagnostic processor, which is adapted to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, to assign each of at least some of the segments, responsively to the respective levels of membership, to one of a waking state and a sleep state responsively to the frequency spectrum, and to display an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

60. The apparatus according to claim 59, wherein the processor is adapted to apply fuzzy clustering to the segments so as to define the states.

61. The apparatus according to claim 59, and wherein the processor is adapted to determine and output respective accumulation rates of the waking and sleep states.

62. The apparatus according to claim 59, wherein the sensor comprises at least one electrode, and wherein the physiological signal comprises an electroencephalogram (EEG) signal.

63. 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 acquire a physiological signal from a patient during a period of sleep, to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, and to display a plot indicative of the levels of membership of the segments in the sequence over time.

64. The product according to claim 63, wherein the instructions cause the computer to apply fuzzy clustering to the segments so as to define the states.

65. The product according to claim 63, wherein the plot comprises a density plot, in which the levels of membership are represented by color variations.

66. The product according to claim 63, wherein the plot comprises an accumulation plot, showing cumulative levels of membership of the segments in the plurality of frequency states over the sequence.

67. The product according to claim 63, wherein the plot comprises an accumulation plot showing cumulative durations of the segments in each of the plurality of frequency states.

68. The product according to claim 67, wherein the instructions cause the computer to determine and output respective accumulation rates of the cumulative durations in at least two of the frequency states.

69. The product according to claim 63, wherein the instructions cause the computer to assign each of at least some of the segments to one of a waking state and a sleep state responsively to the frequency spectrum, and to display an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

70. The product according to claim 63, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

71. 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 acquire a physiological signal from a patient during a period of sleep, to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute a fundamental frequency of each segment in the time sequence responsively to a moment of the respective frequency spectrum of the segment, and to display a plot showing the fundamental frequency of the segments in the sequence over time.

72. The product according to claim 71, wherein the instructions cause the computer to display in the plot at least one of a trend and a variance of the fundamental frequency.

73. The product according to claim 71, wherein the instructions cause the computer to apply fuzzy clustering to the segments so as to define the states.

74. The product according to claim 71, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

75. 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 acquire a physiological signal from a patient during a period of sleep, to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, to determine, based on the respective levels of membership, a sleep quality indicator responsively to a statistical characteristic of the segments, and to display the sleep quality indicator.

76. The product according to claim 75, wherein the instructions cause the computer to apply fuzzy clustering to the segments so as to define the states.

77. The product according to claim 75, wherein the statistical characteristic comprises at least one duration measure selected from a group of duration measures consisting of:

a cumulative duration of the segments associated with each of the frequency clusters;

a relative duration of the segments associated with each of the frequency clusters;

a mean duration of the segments associated with each of the frequency clusters;

a variance of a duration of the segments associated with each of the frequency clusters;

a total number of the segments associated with each of the frequency clusters; and

a relative duration of the segments associated with each of the frequency clusters.

78. The product according to claim 75, wherein the instructions cause the computer to assign the segments to predefined sleep stages responsively to the frequency spectrum, and to compute the statistical characteristic with respect to each of the sleep stages.

79. The product according to claim 75, wherein the instructions cause the computer to display a plot indicative of the levels of membership of the segments in the sequence over time.

80. The product according to claim 75, wherein the instructions cause the computer to display a plot showing a fundamental frequency of the segments in the sequence over time.

81. The product according to claim 75, wherein the instructions cause the computer to assign the segments in the time sequence to respective frequency states, and to compute probabilities of transition among the frequency states.

82. The product according to claim 75, wherein the physiological signal comprises an electroencephalogram (EEG) signal.

83. The product according to claim 82, wherein the instructions cause the computer to identify transient phenomena in the EEG signal, and to compute an index quantifying a frequency of occurrence of the transient phenomena.

84. The product according to claim 83, wherein the transient phenomena comprise one or more of K-complexes and spindles.

85. The product according to claim 75, wherein the sensor comprises a respiration sensor, and wherein the physiological signal comprises a respiration signal.

86. The product according to claim 85, wherein the instructions cause the computer to identify respiratory events occurring during the period of sleep, and to compute statistical characteristics of the respiratory events.

87. The product according to claim 86, wherein the instructions cause the computer to compute and display a respiratory event histogram.

88. The product according to claim 85, wherein the instructions cause the computer to determine a heart rate of the patient, and to compute a relative heart rate index indicative of changes in the heart rate associated with the respiratory events.

89. The product according to claim 85, wherein the instructions cause the computer to assign respective confidence levels to the respiratory events, and to display the confidence levels as a function of respiration state.

90. 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 acquire a physiological signal from a patient during a period of sleep, to segment the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum, to compute respective levels of membership of the segments in a plurality of frequency states responsively to the respective frequency spectrum, to assign each of at least some of the segments, responsively to the respective levels of membership, to one of a waking state and a sleep state responsively to the frequency spectrum, and to display an accumulation plot showing a cumulative assignment of the segments to the waking and sleep states over time.

91. The product according to claim 90, wherein the instructions cause the computer to apply fuzzy clustering to the segments so as to define the states.

92. The product according to claim 90, and wherein the instructions cause the computer to determine and output respective accumulation rates of the waking and sleep states.

93. The product according to claim 90, wherein the physiological signal comprises an electroencephalogram (EEG) signal.