Separation of a subcutaneous cardiac signal from a plurality of composite signals

• US 20040230128A1
• Filed: 12/19/2003
• Published: 11/18/2004
• Est. Priority Date: 04/11/2003
• Status: Active Grant

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Ricci, Carlos Alberto, Brockway, Marina
• Granted Patent: US 7,236,819 B2
• Time in Patent Office: Days
• US Class Current: 600/510
• CPC Class Codes: A61N 1/05 for implantation or insertion into the body, e.g. heart electrode A61N1/06 takes precedence
• CPC Class Codes: A61N 1/0568 with drug delivery
• CPC Class Codes: A61N 1/0587 Epicardial electrode systems; Endocardial electrodes piercing the pericardium
• CPC Class Codes: A61N 1/36542 controlled by body motion, e.g. acceleration
• CPC Class Codes: A61N 1/36585 controlled by two or more physical parameters
• CPC Class Codes: A61N 1/3956 Implantable devices for applying electric shocks to the heart, e.g. for cardioversion
• CPC Class Codes: A61N 1/39622 Pacing therapy

Abstract

A cardiac monitoring and/or stimulation method and systems provide monitoring, defibrillation and/or pacing therapies, including systems detecting and/or treating cardiac arrhythmia. A system includes a housing coupled to a plurality of electrodes configured for subcutaneous non-intrathoracic sensing. A signal processor receives a plurality of composite signals associated with a plurality of sources, separates a signal from the plurality of composite signals using blind source separation, and identifies a cardiac signal. The signal processor may iteratively separate signals from the plurality of composite signals until the cardiac signal is identified. A method of signal separation includes detecting a plurality of composite signals at a plurality of locations, separating a signal using blind source separation, and identifying a cardiac signal. The separation may include a principal component analysis and/or an independent component analysis. The composite signals may be filtered before separation using band-pass filtering, adaptive, or other filters.

Claims

1. A signal separation method, comprising:

detecting a plurality of composite signals at a plurality of subcutaneous non-intrathoracic locations, the plurality of composite signals associated with a plurality of sources;

separating a signal from the plurality of composite signals using blind source separation; and

identifying the separated signal as a cardiac signal.

2. The method of claim 1, further comprising iteratively separating signals from the plurality of composite signals until the cardiac signal is identified.

3. The method of claim 1, wherein the plurality of composite signals comprises the cardiac signal and at least one non-cardiac signal.

4. The method of claim 1, wherein the blind source separation comprises a principal component analysis.

5. The method of claim 4, wherein the principal component analysis includes a singular value decomposition.

6. The method of claim 4, wherein the principal component analysis includes an eigenvalue decomposition.

7. The method of claim 1, wherein the blind source separation comprises an independent component analysis.

8. The method of claim 1, wherein the blind source separation comprises a principal component analysis and an independent component analysis.

9. The method of claim 1, further comprising filtering each of the plurality of composite signals before separating the signal.

10. The method of claim 1, further comprising band-pass filtering each of the plurality of composite signals.

11. The method of claim 1, further comprising determining a cardiac condition using the cardiac signal.

12. The method of claim 1, further comprising detecting a cardiac condition using the separated signal, wherein detecting the cardiac condition comprises performing a rate based analysis of the cardiac signal.

13. The method of claim 1, further comprising detecting a cardiac condition using the separated signal, wherein detecting the cardiac condition comprises performing a morphology based analysis of the cardiac signal.

14. The method of claim 1, further comprising detecting a cardiac condition using the separated signal, wherein detecting the cardiac condition comprises performing a pattern and rate based analysis of the cardiac signal.

15. The method of claim 1, further comprising detecting a cardiac condition using the cardiac signal and implantably treating the cardiac condition.

16. The method of claim 15, wherein implantably treating the cardiac condition comprises delivering a cardiac stimulation therapy.

17. The method of claim 15, wherein implantably treating the cardiac condition comprises delivering a cardiac stimulation therapy using an implanted subcutaneous non-intrathoracic electrode.

18. The method of claim 15, wherein detecting the plurality of composite signals at the plurality of subcutaneous locations comprises using a subcutaneous non-intrathoracic electrode array and wherein implantably treating the cardiac condition comprises using the electrode array to provide a cardiac stimulation therapy.

19. The method of claim 1, further comprising:

forming a composite signal matrix from the plurality of composite signals; and

performing a principal component analysis on the composite signal matrix, thereby producing a set of eigenvalues and associated eigenvectors.

20. The method of claim 19, wherein an eigenvector of the associated eigenvectors that is associated with a largest magnitude eigenvalue of the set of eigenvalues is used to separate the signal from the plurality of composite signals.

21. The method of claim 20, wherein the signal is separated from the plurality of composite signals by multiplying the composite signal matrix by the eigenvector.

22. The method of claim 20, wherein the signal is separated iteratively from the plurality of composite signals using an eigenvector associated with a next-largest magnitude eigenvalue for each iteration, until the cardiac signal is identified.

23. The method of claim 22, wherein the cardiac signal is identified using a local peak density of the separated signal, wherein the local peak density is within a predetermined range.

24. The method of claim 22, wherein the cardiac signal is identified using beat detection on the separated signal, wherein a beat rate is within a predetermined range.

25. The method of claim 22, wherein the cardiac signal is identified using a local rate of occurrence of significant points in the separated signal, wherein the local rate of occurrence is within a predetermined range.

26. The method of claim 22, wherein the cardiac signal is identified using a morphology of the separated signal, wherein the morphology satisfies a physiological characteristic.

27. The method of claim 19, further comprising forming an intermediate signal matrix by multiplying the composite signal matrix by a matrix composed of the eigenvectors corresponding to at least the two largest eigenvalues, and finding a unitary matrix corresponding to a rotational transformation of the intermediate signal matrix that increases the independence of the intermediate signals.

28. The method of claim 27, further comprising forming an independent signal matrix by multiplying the intermediate signal matrix by the unitary matrix, wherein the independent signal matrix comprises a set of independent signal vectors.

29. The method of claim 28, wherein a signal vector from the set of independent signal vectors is iteratively examined until a cardiac signal is identified.

30. The method of claim 29, wherein the cardiac signal is identified using a local peak density of the examined signal, wherein the local peak density is within a predetermined range.

31. The method of claim 29, wherein the cardiac signal is identified using beat detection on the examined signal, wherein a beat rate is within a predetermined range.

32. The method of claim 29, wherein the cardiac signal is identified using a local rate of occurrence of significant points in the examined signal, wherein the local rate of occurrence is within a predetermined range.

33. The method of claim 29, wherein the cardiac signal is identified using a morphology of the examined signal, wherein the morphology satisfies a physiological characteristic.

34. The method of claim 1, further comprising forming an estimate of a spatial covariance matrix for a composite signal matrix formed from the plurality of composite signals.

35. The method of claim 34, further comprising performing a principal component analysis on the estimate of the spatial covariance matrix, producing a set of eigenvalues and associated eigenvectors.

36. The method of claim 35, wherein an eigenvector of the associated eigenvectors that is associated with a largest magnitude eigenvalue of the set of eigenvalues is used to separate the signal from the plurality of composite signals.

37. The method of claim 36, wherein the signal is separated from the plurality of composite signals by multiplying the composite signal matrix by the eigenvector.

38. The method of claim 36, wherein the signal is separated iteratively from the plurality of composite signals using an eigenvector associated with a next-largest magnitude eigenvalue for each iteration, until the cardiac signal is identified.

39. The method of claim 38, wherein the cardiac signal is identified using a local peak density of the separated signal, wherein the local peak density is within a predetermined range.

40. The method of claim 38, wherein the cardiac signal is identified using beat detection on the separated signal, wherein a beat rate is within a predetermined range.

41. The method of claim 38, wherein the cardiac signal is identified using a local rate of occurrence of significant points in the separated signal, wherein the local rate of occurrence is within a predetermined range.

42. The method of claim 38, wherein the cardiac signal is identified using a morphology of the separated signal, wherein the morphology satisfies a physiological characteristic.

43. The method of claim 35, further comprising forming an intermediate signal matrix by multiplying the composite signal matrix by a matrix composed of the eigenvectors corresponding to at least the two largest eigenvalues, and finding a unitary matrix corresponding to a rotational transformation of the intermediate signal matrix that increases the independence of the intermediate signals.

44. The method of claim 43, further comprising forming an independent signal matrix by multiplying the intermediate signal matrix by the unitary matrix, wherein the independent signal matrix comprises a set of independent signal vectors.

45. The method of claim 44, wherein a signal vector from the set of independent signal vectors is iteratively examined until a cardiac signal is identified.

46. The method of claim 45, wherein the cardiac signal is identified using a local peak density of the examined signal, wherein the local peak density is within a predetermined range.

47. The method of claim 45, wherein the cardiac signal is identified using beat detection on the examined signal, wherein a beat rate is within a predetermined range.

48. A signal separation method, comprising:

detecting, using a device implanted within subcutaneous non-intrathoracic tissue, a plurality of composite signals at a plurality of locations, the composite signals produced by a plurality of sources;

filtering the detected composite signals;

separating a signal from the filtered signals; and

determining a cardiac condition using the separated signal.

49. The method of claim 48, wherein filtering comprises performing a spatial correlation of the plurality of composite signals.

50. The method of claim 48, wherein filtering comprises filtering the plurality of composite signals prior to separating the signal.

51. The method of claim 48, wherein filtering comprises using a band-pass filter on the plurality of composite signals.

52. The method of claim 48, further comprising forming an estimate of a spatial covariance matrix for the plurality of composite signals.

53. The method of claim 48, wherein separating the signal from the filtered signals comprises identifying the separated signal as a cardiac signal or a non-cardiac signal.

54. The method of claim 48, wherein separating the signal from the filtered signals comprises performing a principal component analysis.

55. The method of claim 54, wherein the principal component analysis produces a set of eigenvalues and associated eigenvectors, and an eigenvector associated with a largest magnitude eigenvalue is used to separate the signal from the filtered signals.

56. The method of claim 55, wherein the signal is separated iteratively from the plurality of composite signals using a next-largest magnitude eigenvalue for each iteration, until a cardiac signal is separated from the filtered signals.

57. The method of claim 48, further comprising implantably treating the cardiac condition using the implanted device.

58. The method of claim 48, wherein the plurality of composite signals comprises a cardiac signal and at least one non-cardiac signal.

59. An implantable cardiac device, comprising:

a plurality of electrodes configured for subcutaneous non-intrathoracic sensing; and

a signal processor coupled to the plurality of electrodes and configured to receive a plurality of composite signals associated with a plurality of sources sensed by at least some of the electrodes, the signal processor further configured to separate a signal from the plurality of composite signals and identify the separated signal as a cardiac signal.

60. The device of claim 59, wherein the signal processor iteratively separates signals from the plurality of composite signals until the cardiac signal is identified.

61. The device of claim 59, wherein the signal processor comprises a filter configured to filter the plurality of composite signals prior to separating the signal.

62. The device of claim 59, wherein the signal processor comprises a filter configurable to band-pass filter the plurality of composite signals.

63. The device of claim 59, wherein the signal processor forms an estimate of a spatial covariance matrix for the plurality of composite signals.

64. The device of claim 59, wherein the signal processor performs a principal component analysis on the plurality of composite signals to separate the signal from the plurality of composite signals.

65. The device of claim 64, wherein the signal processor performs the principal component analysis to produce a set of eigenvalues and associated eigenvectors, and the signal processor uses an eigenvector with a largest magnitude eigenvalue to separate the signal from the plurality of composite signals.

66. The device of claim 65, wherein the signal processor iteratively separates the signal from the plurality of composite signals using a next-largest magnitude eigenvalue for each iteration, until the cardiac signal is identified.

67. The device of claim 59, wherein the signal processor detects a cardiac condition using the cardiac signal.

68. The device of claim 67, wherein the signal processor performs a rate based analysis of the separated signal to detect the cardiac condition.

69. The device of claim 67, wherein the signal processor performs a morphology based analysis of the separated signal to detect the cardiac condition.

70. The device of claim 59, wherein at least some of the plurality of electrodes are configured for subcutaneous non-intrathoracic energy delivery, the device further comprising an energy delivery system coupled to the at least some of the plurality of electrodes, the energy delivery system delivering a cardiac stimulation therapy to treat the cardiac condition.

71. The device of claim 59, further comprising at least one lead coupled to the plurality of subcutaneous non-intrathoracic electrodes.

72. The device of claim 59, further comprising a plurality of leads, wherein at least one of the plurality of leads is coupled to an array of electrodes.

73. An implantable cardiac device, comprising:

a plurality of electrodes configured for subcutaneous non-intrathoracic sensing; and

a signal processor coupled to the plurality of electrodes, the signal processor configured to receive a composite signal associated with a plurality of sources sensed by at least two of the electrodes, and to extract a cardiac signal from the plurality of composite signals using blind source separation.

74. The device of claim 73, further comprising at least one lead coupled to the plurality of subcutaneous non-intrathoracic electrodes.

75. The device of claim 73, further comprising a plurality of leads coupled to the plurality of subcutaneous non-intrathoracic electrodes.

76. The device of claim 73, further comprising a plurality of leads, wherein at least one of the plurality of leads is coupled to an array of electrodes.

77. The device of claim 73, wherein the signal processor further comprises an adaptive filter, the adaptive filter altering its filtering characteristics based on a level of ambient noise.

78. The device of claim 77, wherein the level of ambient noise is determined using the independent component analysis.

79. The device of claim 73, wherein the blind source separation includes a principal component analysis that produces a set of eigenvalues and associated eigenvectors, and an eigenvector with a largest magnitude eigenvalue is used to extract the cardiac signal from the plurality of composite signals.

80. The device of claim 79, wherein a signal is separated iteratively from the plurality of composite signals using a next-largest magnitude eigenvalue for each iteration, until the cardiac signal is extracted from the plurality of composite signals.

81. An implantable cardiac device, comprising:

means for subcutaneously detecting a plurality of composite signals produced by a plurality of sources;

means for separating a signal from the plurality of composite signals; and

means for identifying the separated signal as a cardiac signal.

82. The device of claim 81, wherein the separating means performs a principal component analysis on the plurality of composite signals.

83. The device of claim 81, wherein the separating means produces a set of eigenvalues and associated eigenvectors, and an eigenvector with a largest magnitude eigenvalue is used for separating the signal from the plurality of composite signals.

84. The device of claim 83, wherein the separating means iteratively separates the signal from the plurality of composite signals using a next-largest magnitude eigenvalue for each iteration, until the cardiac signal is separated from the plurality of composite signals.

85. The device of claim 81, further comprising means for detecting a cardiac condition using the cardiac signal.

86. The device of claim 85, further comprising means for treating the cardiac condition.