Method and system for detection of biological rhythm disorders

• US 9,717,436 B2
• Filed: 08/29/2014
• Issued: 08/01/2017
• Est. Priority Date: 04/08/2010
• Status: Active Grant

• Current Assignee: Topera, Inc (Abbott Laboratories Incorporated), US Department of Veterans Affairs (Government of the United States of America), Regents of the University of California (University of California)
• Original Assignee: Topera, Inc (Abbott Laboratories Incorporated), US Department of Veterans Affairs (Government of the United States of America), Regents of the University of California (University of California)
• Inventors: Narayan, Sanjiv, Sehra, Ruchir
• Primary Examiner(s): CAREY, MICHAEL JAMES
• Application Number:
  US14/473,112
• Publication Number:
  US 20140371613A1
• Time in Patent Office: 1,068 Days
• Field of Search: 600512, 600513, 600515, 600516, 600509
• CPC Class Codes: A61B 5/0255 Recording instruments specially adapted therefor
• CPC Class Codes: A61B 5/287 Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/333 Recording apparatus specially adapted therefor
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/341 Vectorcardiography [VCG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/4857 Indicating the phase of biorhythm clocks or watches with indicators for biological cycles G04B19/264
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

System, assembly and method are provided to facilitate reconstruction of cardiac information representing a complex rhythm disorder associated with a patient'"'"'s heart to indicate a source of the heart rhythm disorder. The complex rhythm disorder can be treated by application of energy to modify the source of the rhythm disorder.

Claims

1. A method of processing cardiac activation information associated with a complex heart rhythm disorder, the method comprising:

obtaining cardiac information signals from sensors associated with a patient;

accessing, via a computing device, the cardiac information signals from the sensors associated with a plurality of locations of a patient'"'"'s heart, the signals including at least a first signal and a second signal; and

determining, via a computing device, activations in the cardiac information signals, wherein at least one activation associated with a beat in the first signal from the sensors at a first location is determined at least with reference to an activation associated with a beat in the second signal at a second location, the first location and second location being proximate neighbors.

2. The method of claim 1, further comprising ordering the activations to identify a location of the heart rhythm disorder.

3. The method of claim 1, further comprising using a phase method to identify a location of the heart rhythm disorder.

4. The method of claim 1, further comprising using a Hilbert transform to identify a location of the heart rhythm disorder.

5. The method of claim 1, further comprising using a time domain method to identify a location of the heart rhythm disorder.

6. The method of claim 1, further comprising using dominant frequency to identify a location of the heart rhythm disorder.

7. The method of claim 2, further comprising identifying a location of the source of the heart rhythm disorder using the activations as ordered.

8. The method of claim 2, further comprising identifying an activation trail based on the activations as ordered.

9. The method of claim 8, further comprising constructing a clinical representation of the activation trail of activations.

10. The method of claim 8, wherein the activation trail comprises a rotational pattern or a radially emanating pattern.

11. The method of claim 8, further comprising identifying an approximate core of the heart rhythm disorder based on the clinical representation of the activation trail of activations.

12. The method of claim 11, further comprising identifying an approximate core of the source of the heart rhythm disorder based on the activation trail.

13. The method of claim 11, wherein the approximate core is a rotor or focal source that radially emanates outwardly from the approximate core.

14. The method of claim 8, further comprising determining activations in the cardiac information signals, wherein at least one activation associated with a beat in a signal at a first location is determined at least in reference to activations associated with beats in signals at second locations, the first location and second locations being proximate neighbors.

15. The method of claim 1, further comprising analyzing the activations as determined in the first signal in reference to the second signal in order to determine a set of activations attributable to discernible beats of the proximate neighbors.

16. The method of claim 15, further comprising using the set of activations to determine discernable beats in the first signal.

17. The method of claim 15, further comprising ordering the set of activations to identify the source of the heart rhythm disorder.

18. The method of claim 17, further comprising determining the location of the heart rhythm disorder using the set of activations as ordered.

19. The method of claim 18, further comprising identifying an activation trail based on the set of activations as ordered.

20. The method of claim 19, wherein the activation trail comprises a rotational pattern or a radially emanating pattern.

21. The method of claim 19, further comprising identifying an approximate core of the heart rhythm disorder based on the activation trail.

22. The method of claim 21, wherein the approximate core is a rotor or a focal source that radially emanates outwardly from the approximate core.

23. The method of claim 21, further comprising identifying an approximate core region of the source of the heart rhythm disorder based on the activation trail.

24. The method of claim 1, wherein the proximate neighbors are adjacent.

25. The method of claim 1, wherein the proximate neighbors are not adjacent.

26. The method of claim 2, further comprising displaying the activations as ordered.

27. The method of claim 26, further comprising determining the location of the heart rhythm disorder using the activations as displayed.

28. The method of claim 24, further comprising ordering the activations to identify a location of the heart rhythm disorder.

29. The method of claim 28, further comprising identifying the location of the source of the heart rhythm disorder using the activations as ordered.

30. The method of claim 28, further comprising identifying an activation trail based on the activations as ordered.

31. The method of claim 30, wherein the activation trail comprises a rotational pattern or a radially emanating pattern.

32. The method of claim 30, further comprising displaying the activation trail.

33. The method of claim 30, further comprising identifying an approximate core of the heart rhythm disorder based on the activation trail.

34. The method of claim 33, wherein the approximate core is a rotor or focal source that radially emanates outwardly from the approximate core.

35. The method of claim 33, further comprising identifying an approximate core of the source of the heart rhythm disorder based on the activation trail.

36. The method of claim 28, further comprising displaying the activations as ordered.

37. The method of claim 36, further comprising determining the location of the heart rhythm disorder using the activations as displayed.

38. The method of claim 25, further comprising ordering the activations to identify a location of the heart rhythm disorder.

39. The method of claim 38, further comprising identifying the location of the source of the heart rhythm disorder using the activations as ordered.

40. The method of claim 38, further comprising identifying an activation trail based on the activations as ordered.

41. The method of claim 40, wherein the activation trail comprises a rotational pattern or a radially emanating pattern.

42. The method of claim 40, further comprising displaying the activation trail.

43. The method of claim 40, further comprising identifying an approximate core of the heart rhythm disorder based on the activation trail.

44. The method of claim 43, wherein the approximate core is a rotor or focal source that radially emanates outwardly from the approximate core.

45. The method of claim 43, further comprising identifying an approximate core of the source of the heart rhythm disorder based on the activation trail.

46. The method of claim 38, further comprising displaying the activations as ordered.

47. The method of claim 46, further comprising determining the location of the heart rhythm disorder using the activations as displayed.

48. A method of processing cardiac activation information associated with a heart rhythm disorder, the method comprising:

obtaining cardiac information signals from sensors associated with a patient;

accessing, via a computing device, the cardiac information signals from the sensors associated with a plurality of locations of a patient'"'"'s heart, the signals including at least a first signal and a second signal;

defining, via a computing device, a time interval in the first signal, indicating how early a beat is able to activate and how late a beat is able to terminate based on at least one predetermined property;

determining, via a computing device, activations in the first cardiac signal, wherein at least one activation associated with a beat in the first signal from the sensors at a first location is determined within the time interval, at least in reference to an activation associated with a beat in the second signal at a second location, the first location and second location being proximate neighbors; and

constructing, via a computing device, a clinical representation associated with the complex rhythm disorder using the activations as determined.

49. A method of identifying a source of a heart rhythm disorder, the method comprising:

obtaining cardiac information signals from sensors associated with a patient;

accessing, via a computing device, the cardiac information signals from the sensors associated with a plurality of locations of a patient'"'"'s heart, the signals including at least a first signal and a second signal; and

determining, via a computing device, activations in the cardiac information signals, wherein at least one activation associated with a beat in the first signal from the sensors at a first location is determined at least in reference to activations associated with beats in signals at second locations, the first location and second locations being proximate neighbors.

50. The method of claim 49, further comprising ordering the activations as determined.

51. The method of claim 49, further comprising ordering the activations to identify a location of the heart rhythm disorder.

52. The method of claim 49, further comprising using a phase method to identify a location of the heart rhythm disorder.

53. The method of claim 49, further comprising using a Hilbert transform to identify a location of the heart rhythm disorder.

54. The method of claim 49, further comprising using a time domain method to identify a location of the heart rhythm disorder.

55. The method of claim 49, further comprising using dominant frequency to identify a location of the heart rhythm disorder.

56. The method of claim 50, further comprising identifying a location of the source of the heart rhythm disorder using the activations as ordered.

57. The method of claim 50, further comprising identifying an activation trail based on the activations as ordered.

58. The method of claim 57, further comprising constructing a clinical representation of the activation trail of activations.

59. The method of claim 57, wherein the activation trail comprises a rotational pattern or a radially emanating pattern.

60. The method of claim 49, further comprising determining activations in the signals using at least one activation in a signal of a first location that is calculated in reference to signals of at least two locations that are neighbors to the first location.

61. The method of claim 49, further comprising selecting a reconciled activation based on the activation closest to at least one of the calculated activations or the average of the activations.

62. The method of claim 49, wherein the first signal is a high confidence signal and the second signal is a high confidence signal.

63. The method of claim 49, wherein the first signal is a low confidence signal and the second signal is a high confidence signal.

64. The method of claim 63, further comprising assigning the low confidence signal an activation.

65. The method of claim 64, further comprising assigning the high confidence signal an activation.

66. The method of claim 49, wherein the first signal is a high confidence signal and the second signal is a low confidence signal.

67. The method of claim 66, further comprising assigning the low confidence signal an activation.

68. The method of claim 66, further comprising assigning the high confidence signal an activation.

69. The method of claim 49, further comprising classifying the cardiac information signals into high and low confidence signals, wherein the high and low confidence signals are separated by a confidence threshold.

70. The method of claim 69, wherein the confidence threshold is associated with a predetermined percentage of beats having discernible activations out of total beats associated with each of the high and low confidence signals.

71. The method of claim 70, further comprising determining an activation associated with at least one low confidence signal within an acceptance time window, the acceptance time window associated with the low confidence signal indicating an earliest activation and a latest termination for a beat in the low confidence signal.

72. The method of claim 71, further comprising ordering the activations associated with the high and low confidence signals.

73. The method of claim 72, further comprising outputting the activations associated with the high and low confidence signals as ordered to indicate the source of the complex rhythm disorder.

74. The method of claim 69, further comprising identifying a plurality of discernible beats on high-confidence signals of sensors that are spatially adjacent to a sensor associated with a low-confidence signal, the discernible beats on the high-confidence signals corresponding to a non-discernible beat on the low-confidence signal.

75. The method of claim 74, further comprising computing a time vector between at least two activations associated with the identified discernible beats on the high-confidence signals through the non-discernible beat on the low-confidence signal.

76. The method of claim 75, further comprising defining a time interval associated with the non-discernible beat about a region of the low-confidence signal where the computed time vector crosses the non-discernible beat, the time interval indicating how early the non-discernible beat is able to activate based on a previous beat on the low-confidence signal that has a selected or determined activation and how late the non-discernible beat is able to terminate based on at least one predetermined property.

77. The method of claim 76, further comprising selecting a possible activation during the defined time interval that is closest to the computed time vector for the non-discernible beat.

78. The method of claim 72, wherein determining further comprises determining activations associated with the low confidence signals within associated acceptance time windows, each of the acceptance time windows associated with a low confidence signal indicating an earliest activation and a latest termination for a beat in the low confidence signal.

79. The method of claim 72, wherein ordering further comprises ordering the activations based on at least one of temporal, spatial and phase information.

80. The method of claim 72, wherein determining further comprises determining activations associated with the low confidence signals using a time vector connecting at least two discernible activations of the high confidence signals associated with spatially adjacent sensors.

81. The method of claim 72, wherein the complex rhythm disorder comprises no discernible period during which the cardiac information signals are quiescent.

82. The method of claim 72, wherein the complex rhythm disorder comprises no discernible earliest activation associated with the cardiac information signals.

83. The method of claim 72, wherein classifying further comprises using at least one of activation onset, cycle length (CL), action potential duration (APD), and amplitude to classify the cardiac information signals into the high and low confidence signals, wherein the activation onsets are determined by using at least one of maximum dV/dt, template matching, frequency and amplitude.

84. The method of claim 72, wherein the acceptance time window is determined using at least one of action potential duration (APD), conduction velocity (CV), fiber angle, time vector connecting at least two discernible activation onsets of the high confidence signals associated with spatially adjacent sensors, and anatomic factors.

85. The method of claim 72, further comprising disregarding at least one of the cardiac information signals using at least one of signal-to-noise ratio (SNR), template matching, frequency and amplitude.

86. The method of claim 72, further comprising classifying beats associated with the cardiac information signals based on a shape associated with the beats.

87. The method of claim 70, wherein classifying the cardiac information signals further comprises classifying beats associated with the cardiac information signals as high confidence beats in response to a cycle length (CL) associated with the beats being greater than or equal to a minimum action potential duration (APD) and less than or equal to a maximum CL.

88. The method of claim 78, further comprising modifying the time vector using at least one of beat shape, beat polarity, surrounding rotating emanation and radial emanation.

89. The method of claim 70, wherein determining activations associated with the low confidence signals further comprises determining activations using at least one of rolling average and phase lock.

90. The method of claim 70, wherein determining activations associated with the low confidence signals further comprises reconciling activations determined by using at least two of time vector, acceptance window, rolling average, and phase lock.