Algorithms for detecting cardiac arrhythmia and methods and apparatuses utilizing the algorithms

• US 20060167364A1
• Filed: 01/26/2005
• Published: 07/27/2006
• Est. Priority Date: 01/26/2005
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Houben, Richard P.M.
• Granted Patent: US 7,412,282 B2
• Time in Patent Office: Days
• US Class Current: 600/515
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: G06F 2218/18 by plotting the signal segments against each other, e.g. analysing scattergrams

Abstract

A method for detecting a cardiac arrhythmia from an electrocardiogram includes the steps of identifying a plurality of R-waves in the electrocardiogram during a predetermined time interval; extracting heartbeat complexes corresponding to the identified R-waves; identifying a key region within each heartbeat complex that is morphologically altered in the event of the cardiac arrhythmia; calculating a statistical measurement of an ensemble of the key regions from each of the heartbeat complexes; and determining from the statistical measurement whether the cardiac arrhythmia occurred during the predetermined time interval. An apparatus is also provided that includes a processor that is coupled to receive an electrocardiogram, and is configured in response thereof to perform the method for detecting a cardiac arrhythmia.

Claims

1. A method for detecting a cardiac arrhythmia from an electrocardiogram, the method comprising a series of wave morphology analysis steps, comprising:

identifying a plurality of wave patterns that are repeated in the electrocardiogram during a predetermined time interval, each wave pattern being indicative of an individual heartbeat;

extracting a heartbeat complex that corresponds to each of the identified wave patterns;

identifying a key region within each heartbeat complex that is morphologically altered in the event of the cardiac arrhythmia;

calculating a statistical measurement of an ensemble of the key regions from each of the heartbeat complexes; and

determining from the statistical measurement whether the cardiac arrhythmia occurred during the predetermined time interval.

2. A method according to claim 1, wherein the wave patterns in the electrocardiogram comprise R-waves.

3. A method according to claim 1, wherein the cardiac arrhythmia comprises an episode of atrial fibrillation.

4. A method according to claim 3, wherein the key region morphologically altered in the event of the cardiac arrhythmia comprises a P-wave region.

5. A method according to claim 1, wherein the statistical measurement comprises a signal average of the key regions from each of the heartbeat complexes.

6. A method according to claim 5, wherein the cardiac arrhythmia comprises atrial fibrillation, and the statistical measurement comprises a signal average of the P-wave regions from each of the heartbeat complexes.

7. A method according to claim 3, further comprising a series of event-based analysis steps for detecting atrial fibrillation, comprising:

defining a signature metric for atrial fibrillation representative of a distribution of data points during atrial fibrillation episodes;

determining an interval for each time period that occurs between successive ventricular heartbeats;

plotting each succeeding interval as a data point in a scatter-plot;

developing a discriminatory metric signifying a degree of sparseness or clustering of the data points in the scatter-plot; and

concluding that atrial fibrillation occurred if the discriminatory metric satisfies the atrial fibrillation signature metric representative of data points during atrial fibrillation.

8. A method according to claim 7, wherein the scatter-plot comprises a Lorenz plot having an origin, and the plotting step further comprises assigning an abscissa value and an ordinate value referenced to the origin of the Lorenz plot to each data point, whereby the data point comprises a two-dimensional (2-D) data point.

9. A method according to claim 8, further comprising the steps of:

defining 2-D bins of the Lorenz plot with reference to the origin of the Lorenz plot; and

correlating each 2-D data point of the Lorenz plot with a bin of the Lorenz plot that the 2-D data point falls within, and wherein,

the step of developing a discriminatory metric further comprises;

counting the number of 2-D data points added to each bin over the segment,

determining the highest number of 2-D data points in a single bin as a MaxB,

counting the number of bins containing at least one 2-D data point as LowC,

counting the number of bins containing at least one 2-D data point and a predetermined number of 2-D data points as a nozC, and

calculating a cluster signature metric (CSM) using the formula;

CSM=nozC+lowC−

MaxB,

the step of defining an atrial fibrillation signature metric comprises establishing a CSM signature metric for atrial fibrillation; and

the concluding step comprises declaring atrial fibrillation if the derived CSM satisfies the CSM signature metric for atrial fibrillation.

10. A method according to claim 7, wherein the event-based analysis steps for detecting atrial fibrillation are performed prior to the series of wave morphology analysis steps.

11. A method according to claim 7, wherein the event-based analysis steps for detecting atrial fibrillation are performed following the series of wave morphology analysis steps.

12. A method according to claim 1, wherein the wave patterns that are repeated in the electrocardiogram comprise P-waves.

13. A method according to claim 1, wherein the key region that is morphologically altered in the event of the cardiac arrhythmia comprises at least one T-wave region.

14. A method according to claim 13, wherein the cardiac arrhythmia comprises a T-wave alternans condition.

15. A method according to claim 13, wherein the cardiac arrhythmia comprises one of:

an acute ischemic episode, a silent ischemia episode, a chronic ischemic condition.

16. A method according to claim 1, wherein the statistical measurement from which the cardiac arrhythmia occurrence is detected is selected from the group consisting of a signal average, a signal median, variability from the signal average, variability from the signal median, standard deviation from the signal average, standard deviation from the signal median, a signal strength in a predetermined percentile range.

17. A method according to claim 1, further comprising the steps of:

recording the electrocardiogram into a memory using a loop recorder; and

recording new electrocardiograms over previously recorded electrocardiograms unless it is determined from the statistical measurement that the cardiac arrhythmia occurred during the predetermined time interval.

18. A method according to claim 1, further comprising the step of:

producing a communication signal after determining from the statistical measurement that the cardiac arrhythmia occurred during the predetermined time interval.

19. An apparatus for detecting a cardiac arrhythmia from an electrocardiogram, comprising:

a processor that is coupled to receive the electrocardiogram, and is configured in response thereof to perform a series of wave morphology analysis steps, comprising;

identifying a plurality of wave patterns that are repeated in the electrocardiogram during a predetermined time interval, each wave pattern being indicative of an individual heartbeat;

extracting a heartbeat complex that corresponds to each of the identified wave patterns;

identifying a key region within each heartbeat complex that is morphologically altered in the event of the cardiac arrhythmia;

calculating a statistical measurement of an ensemble of the key regions from each of the heartbeat complexes; and

determining from the statistical measurement whether the cardiac arrhythmia occurred during the predetermined time interval.

20. An apparatus according to claim 19, wherein the wave patterns that are repeated in the electrocardiogram comprise R-waves.

21. An apparatus according to claim 19, wherein the cardiac arrhythmia comprise an atrial fibrillation condition.

22. An apparatus according to claim 20, wherein the key region that is morphologically altered in the event of the cardiac arrhythmia comprises a P-wave region.

23. An apparatus according to claim 19, wherein the statistical measurement from which the cardiac arrhythmia occurrence is detected is a signal average of the key regions from each of the heartbeat complexes.

24. An apparatus according to claim 22, wherein the cardiac arrhythmia is atrial fibrillation, and the statistical measurement from which the cardiac arrhythmia occurrence is detected is a signal average of the P-wave regions from each of the heartbeat complexes.

25. An apparatus according to claim 21, wherein the processor is further configured to perform a series of event-based analysis steps for detecting atrial fibrillation, comprising:

defining a signature metric for atrial fibrillation representative of a distribution of data points during atrial fibrillation episodes;

determining an interval for each time period that occurs between successive ventricular heartbeats;

plotting each succeeding interval as a data point in a scatter-plot;

developing a discriminatory metric signifying a degree of sparseness or clustering of the data points in the scatter-plot; and

concluding that atrial fibrillation occurred if the discriminatory metric satisfies the atrial fibrillation signature metric representative of data points during atrial fibrillation.

26. An apparatus according to claim 25, wherein the scatter-plot is a Lorenz plot having an origin, and the plotting step further comprises assigning an abscissa value and an ordinate value referenced to the origin of the Lorenz plot to each data point, whereby the data point is a two-dimensional (2-D) data point.

27. An apparatus according to claim 26, wherein the processor is further configured to perform, as part of the series of event-based analysis steps for detecting atrial fibrillation, the steps of:

defining 2-D bins of the Lorenz plot with reference to the origin of the Lorenz plot; and

correlating each 2-D data point of the Lorenz plot with a bin of the Lorenz plot that the 2-D data point falls within, and wherein,

the step of developing a discriminatory metric further comprises;

counting the number of 2-D data points added to each bin over the segment,

determining the highest number of 2-D data points in a single bin as a MaxB,

counting the number of bins containing at least one 2-D data point as LowC,

counting the number of bins containing at least one 2-D data point and a predetermined number of 2-D data points as a nozC, and

calculating a cluster signature metric (CSM) using the formula;

CSM=nozC+lowC−

MaxB,

the step of defining an atrial fibrillation signature metric comprises establishing a CSM signature metric for atrial fibrillation; and

the concluding step comprises declaring atrial fibrillation if the derived CSM satisfies the CSM signature metric for atrial fibrillation.

28. An apparatus according to claim 25, wherein the processor is configured to perform the event-based analysis steps for detecting atrial fibrillation prior to performing the series of wave morphology analysis steps.

29. An apparatus according to claim 25, wherein the processor is configured to perform the event-based analysis steps for detecting atrial fibrillation following the series of wave morphology analysis steps.

30. An apparatus according to claim 19, wherein the wave patterns that are repeated in the electrocardiogram are P-waves.

31. An apparatus according to claim 19, wherein the key region that is morphologically altered in the event of the cardiac arrhythmia is a T-wave region.

32. An apparatus according to claim 31, wherein the cardiac arrhythmia is T-wave alternans.

33. An apparatus according to claim 31, wherein the cardiac arrhythmia comprises an ischemic event.

34. An apparatus according to claim 19, wherein the statistical measurement from which the cardiac arrhythmia occurrence is detected is selected from the group consisting of a signal average, a signal median, variability from the signal average, variability from the signal median, standard deviation from the signal average, standard deviation from the signal median, and signal strength in a predetermined percentile range.

35. An apparatus according to claim 19, wherein the apparatus is an implantable medical device.

36. An apparatus according to claim 35, wherein the implantable medical device is a pacemaker device.

37. An apparatus according to claim 19, further comprising:

a programmable memory that is coupled to the processor and that records the electrocardiogram, wherein the memory is programmed to record new electrocardiograms over previously recorded electrocardiograms unless it is determined from the statistical measurement that the cardiac arrhythmia occurred during the predetermined time interval.

38. An apparatus according to claim 19, further comprising:

a communication device coupled to the programmer to produce a communication signal after it is determined from the statistical measurement that the cardiac arrhythmia occurred during the predetermined time interval.