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1. (WO2005067790) PROCEDE ET DISPOSITIF PERMETTANT DE SURVEILLER DE DETECTER ET DE CLASSIFIER DES TROUBLES RESPIRATOIRES DU SOMMEIL A PARTIR D'UN ELECTROCARDIOGRAMME
Note: Texte fondé sur des processus automatiques de reconnaissance optique de caractères. Seule la version PDF a une valeur juridique

CLAIMS

1. A method of detecting sleep disordered breathing (SDB) and/or cardiac events and/or heart rate variability (HRV) in a subject from a physiological electrocardiogram (ECG) signal, including:
i) monitoring said ECG signal;
ii) extracting from said ECG signal parameters indicative of said SDB and/or cardiac events and/or HRV; and
iii) utilizing said parameters to detect said SDB and/or cardiac events and/or HRV.

2. A method according to claim 1 wherein said method is performed in real time.

3. A method according to claim 1 or 2 wherein said method is performed breath by breath.

4. A method according to claim 1 , 2 or 3 wherein said SDB and/or cardiac events and/or HRV are derived from interaction between the heart and lungs of said subject.

5. A method according to claim 1 wherein said method is performed post offline or acquisition of said ECG signal.

6. A method according to any one of the preceding claims including distinguishing OSA from CSA.

7. A method according to claim 6 wherein said step of distinguishing includes deriving respiratory parameters from said ECG signal.

8. A method according to claim 7 wherein said parameters include respiratory effort or residual respiration indicative of OSA classification.

9. A method according to claim 6 wherein said parameters include an absence or diminished respiratory effort or respiration indicative of CSA classification.

10. A method according to any one of the preceding claims wherein said detected cardiac events include incidence of arrhythmia and/or atrial fibrillation.

11. A method according to any one of the preceding claims wherein said detected SDB is classified into at least one of apnea, hypopnea, shallow breathing, CSR, CSA, OSA, MSA, arousal, body movement, artefact, RERA, TERA and unclassified SDB.

12. A method according to any one of the preceding claims wherein said utilizing includes comparing said ECG signal and/or said extracted parameters and/or SDB data with a predetermined signal pattern and/or patterns and/or a threshold level or levels and/or a reference data base which defines normal/safe or abnormal/risk operating regions.

13. A method according to any one of the preceding claims wherein said parameters include at least one of low frequency power, high frequency power, ratio of low frequency to high frequency power, HRV, R to R intervals, respiratory signal, abdominal breathing effort signal, thoracic breathing effort signal and EMG breathing effort signal.

14. A method according to any one of the preceding claims wherein said parameters include blood pressure variation and/or onset of hypertension and/or risk or severity of heart disease.

15. A method according to any one of the preceding claims including determining a treatment or countermeasure for said SDB, cardiac events and/or HRV.

16. A method according to claim 15 wherein said treatment or countermeasure includes APAP, CPAP, BIPAP, VPAP, ventilation, oxygen concentration, pacemaker, drug administration and/or drug perfusion.

17. A method according to claim 15 or 16 wherein said step of determining is adapted to prevent arrhythmia or a condition which may lead to elevated cardiac risk including excessive blood pressure and/or a state of hypertension.

18. A method according to claim 17 wherein said step of determining includes varying said treatment to avoid an abnormal ECG signal or an ECG signal that reflects said elevated cardiac risk.

19. A method according to claim 11 wherein classifying said SDB into CSR includes monitoring said HRV and/or cardiogenic oscillations, particularly for patients diagnosed with congestive heart failure.

20. A method according to any one of the preceding claims wherein said ECG signal has sufficient bandwidth to enable extraction of an electromyogram (EMG) signal.

21. A method according to claim 20 wherein said EMG signal provides a marker for distinguishing breathing effort characteristic of OSA classification from breathing effort characteristic of CSA classification.

22. A method according to claim 21 wherein a characteristic of said OSA classification includes at least one anti-phase signal.

23. A method according to claims 21 or 22 wherein said marker characteristic of OSA classification includes an increased EMG signal indicative of breathing effort.

24. A method according to claim 21 or 22 wherein said marker characteristic of CSA classification includes a decreased EMG signal indicative of breathing effort or an absence of EMG signal indicative of breathing effort.

25. A method according to any one of the preceding claims wherein said ECG signal is provided via at least one ECG electrode attached to said subject.

26. A method according to claim 25 wherein said ECG signal is provided via three ECG electrodes attached to said subject such that abdominal breathing effort and thoracic breathing effort may be monitored separately.

27. A method according to claim 26 wherein at least one impedance path between said ECG electrodes is substantially orthogonal relative to another impedance path between said ECG electrodes.

28. A method according to claim 26 or 27 wherein said ECG electrodes are attached to said subject such that strength and/or signal to noise ratio of said ECG signal may be optimised.

29. A method according to any one of claims 25 to 28 including guiding and verifying correct placement of said at least one ECG electrode via feedback means.

30. A method according to claim 29 wherein said feedback means includes a visual display.

31. A method according to any one of the preceding claims including displaying data extracted from said ECG signal, including one or more phase relationships in said data.

32. Apparatus for detecting sleep disordered breathing (SDB) and/or cardiac events and/or heart rate variability (HRV) in a subject from a physiological electrocardiogram (ECG) signal, including:
i) means for monitoring said ECG signal;
ii) means for extracting from said ECG signal parameters indicative of said SDB and/or cardiac events and/or HRV; and
iii) means utilizing said parameters to detect said SDB and/or cardiac events and/or HRV.

33. Apparatus according to claim 32 wherein said SDB, cardiac events and/or HRV are detected in real time.

34. Apparatus according to claim 32 or 33 wherein said SDB, cardiac events and/or HRV are detected breath by breath.

35. Apparatus according to claim 32, 33 or 34 wherein said SDB and/or cardiac events and/or HRV are derived from interaction between the heart and lungs of said subject.

36. Apparatus according to claim 32 wherein said SDB, cardiac events and/or HRV are detected offline or post acquisition of said ECG signal.

37. Apparatus according to any one of claims 32 to 36 including means for distinguishing OSA from CSA.

38. Apparatus according to claim 37 wherein said means for distinguishing includes means for deriving respiratory parameters from said ECG signal.

39. Apparatus according to claim 38 wherein said parameters include respiratory effort or residual respiration indicative of OSA classification.

40. Apparatus according claim 38 wherein said parameters include an absence or diminished respiratory effort or respiration indicative of CSA classification.

41. Apparatus according to any one of claims 32 to 40 wherein said detected cardiac events include incidence of arrhythmia and/or atrial fibrillation.

42. Apparatus according to any one of claims 32 to 41 wherein said utilizing means includes means for classifying said SDB into at least one of apnea, hypopnea, shallow breathing, CSR, CSA, OSA, MSA, arousal, body movement, artifact, RERA, TERA and unclassified SDB.

43. Apparatus according to any one of claims 32 to 42 wherein said utilizing means is adapted to compare said ECG signal and/or said extracted parameters and/or SDB data with a predetermined signal pattern and/or patterns and/or a threshold level or levels and/or reference data base which defines normal/safe or abnormal/risk operating regions.

44. Apparatus according to any one of claims 32 to 43 wherein said parameters include at least one of normalized low frequency power, high frequency power, ratio of low frequency to high frequency power, HRV, R to R intervals, respiratory signal, abdominal breathing effort signal, thoracic breathing effort signal and EMG breathing effort signal.

45. Apparatus according to any one of claims 32 to 44 wherein said parameters include blood pressure variation and/or onset of hypertension and/or risk or severity of heart disease.

46. Apparatus according to claim 32 to 45 including means for determining a treatment or countermeasure for said SDB and/or cardiac events and/or HRV.

47. Apparatus according to claim 46 wherein said treatment includes APAP, CPAP, BIPAP, VPAP ventilation, oxygen concentration, pacemaker, drug administration and/or drug perfusion.

48. Apparatus according to claim 46 or 47 wherein said means for determining is adapted to prevent arrhythmia or a condition which may lead to elevated cardiac risk including excessive blood pressure and/or a state of hypertension.

49. Apparatus according to claim 48 wherein said means for determining includes means for varying said treatment to avoid an abnormal ECG signal or an ECG signal that reflects said elevated cardiac risk.

50. Apparatus according to claim 42 wherein said means for classifying includes means for monitoring said HRV and/or cardiogenic oscillations, particularly for patients diagnosed with congestive heart failure.

51. Apparatus according to any one of claims 32 to 50 wherein said ECG signal has sufficient bandwidth to enable extraction of an electromyogram (EMG) signal.

52. Apparatus according to claim 51 wherein said EMG signal provides a marker for distinguishing breathing effort characteristic of OSA classification, from breathing effort characteristic of CSA classification.

53. Apparatus according to claim 52 wherein a characteristic of said OSA classification includes at least one anti-phase signal.

54. Apparatus according to claim 52 or 53 wherein said marker characteristic of OSA classification includes an increased EMG signal indicative of breathing effort.

55. Apparatus according to claim 52 or 53 wherein said marker characteristic of CSA classification includes a decreased EMG signal indicative of breathing effort or an absence of EMG signal indicative of breathing effort.

56. Apparatus according to any one of claims 32 to 53 wherein said means for monitoring includes at least one ECG electrode attached to said subject.

57. Apparatus according to claim 56 wherein said means for monitoring includes three ECG electrodes attached to said subject such that abdominal breathing effort and thoracic breathing effort may be monitored separately.

58. Apparatus according to claim 57 wherein at least one impedance path between said ECG electrodes is substantially orthogonal relative to another impedance path between said ECG electrodes.

59. Apparatus according to claim 57 or 58 wherein said ECG electrodes are attached to said subject such that strength and/or signal to noise ratio of said ECG signal may be optimised.

60. Apparatus according to any one of claims 56 to 59 including means for guiding and verifying correct placement of said at least one electrode upon said subject.

61. Apparatus according to claim 60 wherein said means for guiding and verifying correct placement includes feedback means.

62. Apparatus according to claim 61 wherein said feedback means includes a visual display.

63. Apparatus according to any one of claims 32 to 62 including means for displaying data extracted from said ECG signal including one or more phase relationships.

64. A method of detecting an electromyogram (EMG) signal superimposed on a physiological electrocardiogram (ECG) signal, including:
i) monitoring said ECG signal; and
ii) extracting said superimposed EMG signal from said ECG signal.

65. A method according to claim 64 wherein said ECG signal includes QRS extremeties and said extracting includes gating said QRS extremeties to amplify and assess presence of said EMG signal.

66. A method according to claim 64 or 65 including processing said extracted EMG signal to provide a marker for distinguishing breathing effort characteristic of OSA from breathing effort characteristic of CSA.

67. A method according to claim 66 wherein a characteristic of OSA includes at least one anti-phase signal.

68. Apparatus for detecting an electromyogram (EMG) signal superimposed on a physiological electrocardiogram (ECG) signal including:
i) means for monitoring said ECG signal; and
ii) means for extracting said superimposed EMG from said ECG signal.

69. Apparatus according to claim 68 wherein said ECG signal includes QRS extremeties and said means for extracting includes means for gating said QRS extremeties to amplify and assess presence of said EMG signal.

70. Apparatus according to claim 68 or 69 including means for processing said extracted EMG signal to provide a marker for distinguishing breathing effort characteristic of OSA from breathing effort characteristic of CSA.

71. Apparatus according to claim 70 wherein a characteristic of OSA includes at least one anti-phase signal.

72. A method of detecting SDB and/or cardiac events and/or HRV from a physiological ECG signal substantially as herein described with reference to the accompanying drawings.

73. Apparatus for detecting SDB and/or cardiac events and/or HRV from a physiological ECG signal substantially as herein described with reference to the accompanying drawings.

74. A method of detecting an EMG signal superimposed on an ECG signal substantially as herein described with reference to the accompanying drawings.

75. Apparatus for detecting an EMG signal superimposed on an ECG signal substantially as herein described with reference to the accompanying drawings.

76. An ambulatory holter device including apparatus according to any one of claims 32 to 63, 68 to 71 , 73 and 75.