System and method for developing a database of body surface ECG flutter wave data maps for classification of atrial flutter

• US 6,556,860 B1
• Filed: 11/28/2000
• Issued: 04/29/2003
• Est. Priority Date: 03/15/2000
• Status: Expired due to Fees

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Groenewegen, Arne Sippens
• Primary Examiner(s): Schaetzle, Kennedy
• Assistant Examiner(s): DROESCH, KRISTEN L
• Application Number:
  US09/724,947
• Time in Patent Office: 882 Days
• Field of Search: 128/897-898, 128/901, 128/920, 128/923, 600/509, 600/515, 600/518
• US Class Current: 600/509
• CPC Class Codes: A61B 5/061 Determining position of a probe within the body employing means separate from the probe, e.g. sensing internal probe position employing impedance electrodes on the surface of the body A61B1/00158 takes precedence
• CPC Class Codes: A61B 5/282 Holders for multiple electrodes for introduction into the body A61B5/287
• CPC Class Codes: A61B 5/322 Physical templates or devices for measuring ECG waveforms, e.g. electrocardiograph rulers or calipers by template matching A61B5/35
• CPC Class Codes: A61B 5/35 by template matching
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method and apparatus for developing a database of mean body surface ECG flutter wave data maps for classification of atrial flutter are described. ECG signals from a plurality of torso sites and multisite endocardial recordings are obtained during CCW and CW typical atrial flutter and atypical atrial flutter. Flutter wave episodes are divided into two or three successive time intervals showing stable potential distributions from which data maps are computed. Body surface mapping of CCW and CW typical atrial flutter and atypical atrial flutter is compared with endocardial activation sequence mapping to confirm validity between the body surface ECG pattern and the underlying right or left atrial activation sequence. The body surface ECG map patterns of CCW and CW typical atrial flutter are characterized by a stereotypical spatial voltage distribution that can be directly related to the underlying activation sequence and are highly specific to the direction of flutter wave rotation. The mean body surface ECG flutter wave data maps present a unique reference database for improved clinical detection and classification of typical and atypical atrial flutter.

Claims

1. A system for developing a database of body surface ECG flutter wave data for classification of atrial flutter wherein said database is derived from data of two or more subjects comprising:

a) means for generating atrial flutter data;

b) means for receiving the data from a subject;

c) means for classifying the data as typical counterclockwise (CCW), typical clockwise (CW), or atypical atrial flutter;

d) means for averaging the classified data; and

e) means for storing and accessing the averaged data in the database.

2. The system as set forth in claim 1, wherein the flutter wave data are in the form of flutter wave maps.

3. The system as set forth in claim 1, further comprising means for inducing said atrial flutter by electrically stimulating an arrhythmogenic substrate of an atrium of the subject using a probe.

4. The system as set forth in claim 1, wherein the receiving means comprises means for sensing heart cycle signals wherein said subject is spontaneously producing said atrial flutter or wherein said atrial flutter is being induced.

5. The system as set forth in claim 4, wherein the receiving means comprises means for detecting a heart cycle signal with a plurality of sensors proximate a subject'"'"'s torso.

6. The system as set forth in claim 4, wherein the receiving means comprises means for separating from said heart signal an atrial signal obscured by a ventricular signal.

7. The system as set forth in claim 6, wherein the receiving means comprises means for selecting at least one reference cycle from among a plurality of heart cycles to determine said atrial flutter.

8. The system as set forth in claim 7, wherein the receiving means comprises means for selecting time intervals of the reference cycle and comparing signals from a plurality of sensors during said selected time intervals.

9. The system as set forth in claim 8, wherein the receiving means comprises means for generating a data matrix by integrating the separated signals from associated sensor locations within said selected time intervals to define one or more integral values, and arranging the integral values within the matrix according to locations of said associated sensor locations along a surface of a subject'"'"'s torso.

10. The system as set forth in claim 9, wherein the receiving means comprises means for computing integral maps over selected time intervals with stable voltage and plotting said data matrix.

11. The system as set forth in claim 10, wherein the computing means comprises means for determining lines of constant integral values, and identifying characteristic body surface maps of said atrial flutter using the lines of constant integral values mapped upon representations of a surface of the subject'"'"'s torso.

12. The system as set forth in claim 10, wherein the computing means comprises means for representing different integral values by one or more different colors mapped upon representations of a surface of the subject'"'"'s torso.

13. The system set forth in claim 8, wherein the receiving means comprises means for generating a data matrix of potential signals from associated sensor locations within said selected time intervals to define one or more potential values, and arranging the potential values within the matrix according to said locations of said associated sensor locations along a body surface of a torso.

14. The system as set forth in claim 13, wherein said generating means comprises means for determining lines of constant potential values, and identifying characteristic body surface maps of said atrial flutter using the lines of constant potential values mapped upon representations of a surface of a subject'"'"'s torso.

15. The system as set forth in claim 13, wherein said generating means comprises means for representing different potential values by one or more different colors mapped upon representations of a surface of a subject'"'"'s torso.

16. The system as set forth in claim 1, wherein the classifying means comprises means for determining characteristic body surface maps specific to CCW or CW typical or atypical atrial flutter data.

17. The system as set forth in claim 16 wherein the classifying means comprises means for defining said CCW typical flutter data as a right atrial arrhythmia characterized by CCW wavefront rotation around a tricuspid annulus and critical dependence on a subeustachian isthmus.

18. The system as set forth in claim 16, wherein the classifying means comprises means for defining said CW typical flutter data as a right atrial arrhythmia characterized by CW wavefront rotation around a tricuspid annulus and critical dependence on a subeustachian isthmus.

19. The system as set forth in claim 16, wherein the classifying means comprises means for verifying said CCW or CW typical flutter data.

20. The system as set forth in claim 19, wherein the verifying means comprises means for using multipolar endocardial catheter recordings from a right atrium and/or coronary sinus to demonstrate characteristic said flutter rotation and critical dependence.

21. The system as set forth in claim 19, wherein the verifying means comprises means for evaluating intersubject comparability of said atrial flutter data with the same direction of rotation.

22. The system of claim 21 wherein the verifying means further comprises means for computing correlation coefficients between maps to validate use of subject data.

23. The system as set forth in claim 21, wherein the averaging means comprises means for calculating mean maps from verified atrial flutter data maps to construct a database of characteristic body surface ECG flutter wave maps.

24. The system as set forth in claim 23, wherein said means for calculating mean maps comprises means for comparing and differentiating CCW flutter wave rotation from CW flutter wave rotation.

25. The system as set forth in claim 1, wherein the storing means comprises means for using a computer to store, search and analyze said averaged data.

26. The system as set forth in claim 25, wherein the storing means comprises means for using said computer to detect and classify typical or isthmus-dependent atrial flutter.

27. A method for developing a database of body surface ECG flutter wave data for classification of atrial flutter wherein said database is derived from data of two or more subjects comprising the steps of:

a) receiving atrial wave flutter data from the subjects;

b) classifying the data as typical counterclockwise (CCW) or clockwise (CW) and atypical atrial flutter;

c) averaging the classified data; and

d) storing and accessing the averaged data in the database.

28. The method as set forth in claim 27, wherein the flutter wave data are flutter wave maps.

29. The method as set forth in claim 28, wherein the developing method comprises inducing said atrial flutter by electrically stimulating an arrhythmogenic substrate of an atrium of the subject using a probe.

30. The method as set forth in claim 27, wherein the receiving method comprises sensing heart cycle signals wherein said subject is spontaneously producing said atrial flutter or wherein said atrial flutter is being induced.

31. The method as set forth in claim 30, wherein the receiving method comprises detecting a heart cycle signal with a plurality of sensors proximate a subject'"'"'s torso.

32. The method as set forth in claim 30, wherein the receiving step comprises separating from said heart signal an atrial signal obscured by a ventricular signal.

33. The method as set forth in claim 32, wherein the receiving step further comprises selecting at least one reference cycle from among a plurality of heart cycles to determine said atrial flutter.

34. The method as set forth in claim 33, wherein the receiving step further comprises selecting time intervals of the reference cycle and comparing signals from a plurality of sensors during said selected time intervals.

35. The method as set forth in claim 34, wherein the receiving step comprises generating a data matrix by integrating the separated signals from associated sensor locations within said selected time intervals to define an integral value, and arranging the integral values within the matrix according to locations of said associated sensor locations along a body surface of a torso.

36. The method as set forth in claim 35, wherein the receiving step comprises computing integral maps over selected time intervals with stable voltage and plotting said data matrix.

37. The method as set forth in claim 36, wherein the integral maps are computed by determining lines of constant integral values along the plots, and identifying characteristic body surface maps of said atrial flutter using the lines of constant integral values mapped upon representations of surface of the subject'"'"'s torso.

38. The method of claim 36, wherein the integral maps represent different integral values by one or more different colors mapped upon representations of a surface of the subject'"'"'s torso.

39. The method of claim 34, wherein the receiving step comprises generating a data matrix of potential signals from associated sensor locations within said selected time intervals to define one or more potential values, and arranging the potential values within the matrix according to said locations of said associated sensor locations along a body surface of a torso.

40. The method of claim 39, wherein the generating step comprises determining lines of constant potential values, and identifying characteristic body surface maps of said atrial flutter using the lines of constant potential values mapped upon representations of a surface of a subject'"'"'s torso.

41. The method of claim 39, wherein the generating step comprises representing different potential values by one or more different colors mapped upon representations of a surface of a subject'"'"'s torso.

42. The method as set forth in claim 27, wherein the classifying method comprises determining characteristic body surface maps specific to CCW or CW typical or atypical atrial flutter data.

43. The method as set forth in claim 42, wherein the classifying method comprises defining said CCW typical flutter data as a right atrial arrhythmia characterized by CCW wavefront rotation around a tricuspid annulus and critical dependence on a subeustachian isthmus.

44. The method as set forth in claim 42, wherein the classifying method comprises defining said CW typical flutter data as a right atrial arrhythmia characterized by CW wavefront rotation around a tricuspid annulus and critical dependence on a subeustachian isthmus.

45. The method as set forth in claim 42, wherein the classifying method comprises verifying said CCW or CW typical flutter data.

46. The method as set forth in claim 45, wherein the verifying method comprises using multipolar endocardial catheter recordings from a right atrium and/or coronary sinus to demonstrate characteristic flutter rotation and critical dependence.

47. The method as set forth in claim 45, wherein the verifying method comprises evaluating intersubject comparability of said atrial flutter data with the same direction of rotation.

48. The method of claim 47 further comprising computing correlation coefficients between said body surface maps to validate use of subject data.

49. The method as set forth in claim 27, wherein the averaging method comprises calculating mean maps from said atrial flutter data to construct a database of characteristic said body surface ECG flutter wave maps.

50. The method as set forth in claim 49, wherein the method of developing said database comprises comparing and differentiating CCW flutter wave rotation from CW flutter wave rotation.

51. The method as set forth in claim 27, wherein the storing method comprises using a computer to store, search and analyze said averaged data.

52. The method as set forth in claim 51, wherein the storing method comprises using said computer to detect and classify typical or isthmus-dependent atrial flutter.

53. An apparatus for classifying typical or isthmus-dependent and atypical atrial flutter, comprising:

a) a signal processor means for receiving electrical signals indicative of a heart'"'"'s electrical activity from a plurality of torso sites;

b) a QRST subtraction means for removing ventricular activity superimposed on the atrial activity from said electrical signals, and for outputting an atrial flutter baseline signal;

c) a means for receiving said baseline signal to classify said atrial flutter as CCW typical, CW typical or atypical atrial flutter, wherein the atrial flutter is classified based on a comparison of said baseline signal to a database of mean CCW typical, CW typical or atypical body surface ECG flutter wave data map patterns; and

d) transformation means for graphically representing averaged atrial flutter data obtained from said baseline signal, on a torso representation of a subject.

54. A system for developing an arrhythmia database from a plurality of subjects, the system comprising:

a) at least one sensor array coupleable to the subject, said at least one sensor array transmitting atrial flutter data in response to an atrial flutter of the subject;

b) a processor receiving said atrial flutter data from said sensor array;

c) a classification module reciving said atrial flutter data from said processor, the classification module classifying said atrial flutter as typical counterclockwise, typical clockwise, or atypical; and

d) a combining module that combines said atrial flutter data with said database according to the classification of said atrial flutter data.