Apparatus and methods for analysis of cardiac device stored episodes containing sensed signals and waveforms

• US 20030204146A1
• Filed: 04/29/2002
• Published: 10/30/2003
• Est. Priority Date: 04/29/2002
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Carlson, Dan B.
• Granted Patent: US 7,263,399 B2
• Time in Patent Office: Days
• US Class Current: 600/515
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/7232 involving compression of the physiological signal, e.g. to extend the signal recording period

Abstract

Apparatus and methods for analyzing implantable medical device (IMD) data and to distinguish specific morphologies for review. EGM waveforms of a cardiac cycle can be grouped according to waveform shape similarities, preferably accomplished using clustering algorithms. The EGM waveform can be divided into segments represented by a one dimensional vector for each segment, and nearest neighbor vector clustering used to group the waveforms according to morphological similarity. The waveforms can be further grouped at a second level into chronologically contiguous similar EGM waveforms. Interval data for a cardiac cycle may also be grouped according to interval similarity and chronology. The chronologically contiguous waveform and interval groupings may be further grouped. Large quantities of EGM data are automatically analyzed and annotated to focus the attention of a cardiologist on likely areas of concern, reducing the need to visually inspect endless streams of EGM waveforms.

Claims

1. A method for analyzing cardiac waveform data for a plurality of cardiac cycles, wherein the cardiac cycles have an associated waveform cycle segment, the method comprising grouping the cycles into one or more cycle waveform identity group based on waveform similarities with other members of the group, with members of the same waveform identity group having similar waveform shapes to the other waveform identity group members.

2. A method as in claim 1, further comprising processing the identity groups by executing one of a step of storing the identity group to non-volatile storage, marking the groups, displaying the waveforms with visual indicia of group membership, printing the waveforms with visual indicia of group membership, and transmitting the groups over a data communications network.

3. A method for analyzing stored cardiac waveform data for a plurality of first heart chamber cycles, the method comprising grouping the cycles into a first level of cycle waveform groups, with members of the same first level waveform group having similar waveform shapes to the other first level waveform group members.

4. A method as in claim 3, further comprising:

grouping the cycles into a second level of beat waveform groups, with members of the same second level waveform group having similar waveform shapes to the group members and being substantially chronologically contiguous to the other second level waveform group members.

5. A method as in claim 4, wherein the cycles have an associated cycle interval, further comprising:

grouping the cycles into a second level of beat interval groups, with members of the same second level interval group having similar intervals to the other second level interval group members and being substantially chronologically contiguous to the other second level interval group members; and

grouping the cycles into a third level of cycle first chamber groups, wherein the members of the third level first chamber group are substantially all members of both the same second level cycle waveform group and the same second level cycle interval group.

6. A method as in claim 4, wherein the cycles have an associated cycle interval, further comprising:

grouping the cycles into a second level of cycle interval groups, with members of the same second level interval group having similar intervals to the other second level interval group members and being substantially chronologically contiguous to the other second level interval group members; and

grouping the cycles into a third level of first chamber groups, wherein members of the third level first chamber group are substantially all members of both the same second level beat waveform group and the same second level beat interval group, and wherein the members of the third level first chamber group are substantially chronologically coextensive with each other.

7. A method as in claim 5, wherein the data further includes data for a second heart chamber, wherein the second chamber cycle data includes waveform data and associated cycle intervals, the method further comprising:

grouping the second chamber beats into a first level of second chamber cycle waveform groups, with members of the same second chamber first level waveform group having similar waveform shapes to the other second chamber first level waveform group members;

grouping the second chamber cycles into a second chamber second level of cycle interval groups, with members of the same second chamber second level interval group having similar intervals to the other second chamber second level interval group members and being substantially chronologically contiguous to the other second chamber second level interval group members; and

grouping the second chamber cycles into a second chamber third level of cycle groups, wherein the members of the same second chamber third level group are substantially all members of both the same second chamber second level cycle waveform group and the same second chamber second level cycle interval group; and

grouping the first and second chamber third level groups into a fourth level of beat groups, wherein the members of the same fourth level group are substantially chronologically coextensive.

21. A method as in claim 3, wherein the clustering step includes the nearest neighbor vector clustering.

8. A method for analyzing stored cardiac EGM waveform data for a plurality of a first heart chamber cycles, the data including a plurality of waveform cycle segments, the data initially originating from an implanted device, the method comprising:

grouping the waveform cycle segments into a first set of cycle groups as a function of waveform shape similarity, wherein each of the first set group members have similar waveform shapes to the other members of the same first set group

9. A method as in claim 8, further comprising processing the first set groups by executing one of a step of marking, storing, displaying, and outputting the group memberships, and combinations thereof.

10. A method as in claim 8, wherein the grouping includes clustering the waveform cycle segments based on waveform shape similarity.

11. A method as in claim 10, wherein the clustering includes nearest neighbor clustering.

12. A method as in claim 11, wherein the waveform cycle segments are treated as vectors, and the clustering includes nearest neighbor vector clustering.

13. A method as in claim 12, wherein the clustering clusters using the Euclidean distance between the vectors.

14. A method as in claim 10, wherein the waveform beat segments are treated as vectors, and the clustering clusters using the Euclidean distance between the vectors.

15. A method as in claim 8, further comprising:

grouping the waveform cycle segments into a second set of cycle groups, wherein the second set group members are substantially chronologically contiguous to the other second set group members and are members of the same first set group.

16. A method as in claim 15, further comprising processing the first set groups by executing one of a step of marking, storing, displaying, and outputting the group memberships, and combinations thereof.

17. A method for analyzing stored cardiac EGM data from an implantable device, the method comprising:

retrieving the stored data, wherein the data includes EGM waveform data;

dividing the EGM waveform data into a plurality of cardiac cycle waveform segments, wherein each heartbeat waveform segment is associated with a heartbeat; and

grouping the EGM cardiac cycle waveform segments into waveform identity groups as a function of waveform shape similarities.

18. A method as in claim 17, wherein the grouping includes calculating a waveform shape distance between the cardiac cycle waveform segments and clustering the cardiac cycle waveform segments as a function of the shape distance between the heartbeat waveform segments.

19. A method as in claim 18, wherein each cardiac cycle waveform segment is represented as a vector and the shape distance is a distance between the vectors.

20. A method as in claim 19, wherein the vector distance is a Euclidean distance.

22. A method as in claim 19, further comprising marking the EGM heartbeat waveform segments with an indicia of identity group membership and storing the marked EGM identity waveform segments in nonvolatile storage.

23. A method as in claim 17, further comprising the step of visually displaying the EGM heartbeat waveform segments together with a visual indicia of the waveform identity group membership for each EGM heartbeat waveform segment.

24. A method as in claim 17, further comprising collecting temporally adjacent heartbeat waveform segments having the same heartbeat waveform identity group membership into chronologically contiguous similar cardiac cycle waveform segments.

25. A method as in claim 24, wherein the temporally adjacent cardiac cycle waveform segments are located at a distance at no more than n cardiac cycles from each other, wherein n is selected from a group consisting of 1, 2, and 3 cardiac cycles, wherein n=0 indicates perfect adjacency.

26. A method as in claim 24 further comprising marking the collections with indicia of collection membership and storing the marked collections to nonvolatile storage.

27. A method as in claim 24, further comprising visually displaying the collections with a visual indication of cardiac cycle waveform segment collection membership.

28. A method of processing stored cardiac interval data from an implantable device, a method comprising:

retrieving the stored interval data, wherein the interval data includes interval data for a plurality of cardiac cycle for a first heart chamber, wherein the interval data includes interval data for each cardiac cycle;

clustering the interval data into similarity groups having similar intervals to other members of the same group; and

organizing substantially similar and chronologically contiguous cardiac cycle intervals into collections of substantially chronologically contiguous beats.

29. A method as in claim 28, further comprising marking the collections of substantially chronologically contiguous cardiac cycle intervals with indicia of collection membership and storing the collections.

30. In a computer, having a data storage device, a display device, and a processor for executing a program, a method for preprocessing stored cardiac data originating from an implantable device, the method comprising:

retrieving the stored data from the data storage device, wherein the data includes EGM waveform segment data for a plurality of beats for a first heart chamber;

clustering the EGM waveform segments into morphological similarity groups having similar morphology with other members of the same group; and

organizing morphologically similar and substantially chronologically contiguous cardiac cycle segments into collections of similar and substantially chronologically contiguous cycles.

31. A method as in claim 30, further comprising marking the collections with indicia of collection membership and storing the marked collections in the data storage device.

32. A method as in claim 31, further comprising displaying the collections with visual indication of collection membership on the computer display device.

33. A method as in claim 32, further comprising classifying the collections into preexisting classifications of waveform morphologies and visually displaying the collection classifications on the computer display device.

34. A method as in claim 30, wherein the retrieved EGM waveform data is stored as a vector of wave amplitudes for each cardiac cycle segment;

and

wherein the clustering includes clustering the EGM waveform vectors using nearest neighbor vector clustering.

35. A method as in claim 32, wherein the clustering utilizes Euclidian distance between the EGM waveform vectors to cluster the EGM waveforms.

36. A method for analyzing cardiac first sensor waveform data for a plurality of cardiac cycle, the method comprising grouping the cycles into a first level of first sensor waveform groups, with members of the same first level first sensor waveform group having similar first sensor waveform shapes to the other first level first sensor waveform group members.

37. A method as in claim 36, further comprising:

grouping the beats into a second level of first sensor waveform groups, with members of the same second level first sensor waveform group having similar first sensor waveform shapes to the group members and being substantially chronologically contiguous to the other second level first sensor waveform group members.

38. A method as in claim 37, wherein the cycles have an associated second sensor waveform, further comprising:

grouping the cycles into a first level of second sensor waveform groups, with members of the same first level second sensor waveform group having similar second sensor waveform shapes to the other first level second sensor waveform group members;

grouping the cycles into a second level of second sensor waveform groups, with members of the same second level second sensor waveform group having similar waveforms to the other second level second sensor waveform group members and being substantially chronologically contiguous to the other second level second sensor group members; and

grouping the cycles into a third level of beat groups, wherein the members of the third level beat group are substantially all members of both the same second level first sensor waveform group and the same second level second sensor waveform group.

39. A method as in claim 37, wherein the cycles have an associated second sensor waveform, further comprising:

grouping the cycles into a second level of second sensor waveform groups, with members of the same second level second sensor waveform group having similar intervals to the other second level second sensor group members and being substantially chronologically contiguous to the other second level second sensor group members; and

grouping the cycles into a third level of beat groups, wherein members of the third level beat group are substantially all members of both the same second level first sensor waveform group and the same second level second sensor waveform group, and wherein the members of the third level cycle group are substantially chronologically coextensive with each other.

40. A method as in claim 36, further comprising selecting at a first sensor waveform from a first sensor first level waveform group as an exemplar waveform from that group and performing one of a step of storing, printing, displaying, and transmitting the exemplar waveform, as an example of the waveforms of that first level first sensor group.

41. A method as in claim 37, further comprising selecting a second level, first sensor group, selecting at a first sensor waveform from the selected second level first sensor waveform group as an exemplar waveform from second level first sensor group, determining the time or beat repeat length of that second level first sensor waveform group, and performing one of a step of storing, printing, displaying, and transmitting the exemplar waveform and repeat length, such that a second level first sensor waveform group is compressed by selecting a waveform and number of repeats to represent the second level first waveform group.

42. A computer program in a computer readable media for ultimate execution in a data processing system for analyzing cardiac first sensor waveform data for a plurality of cardiac cycles, the program comprising instructions for:

grouping the cycles into a first level of first sensor waveform groups, with members of the same first level first sensor waveform group having similar first sensor waveform shapes to the other first level first sensor waveform group members.

43. A computer program in a computer readable media as in claim 42, the program further comprising instructions for:

grouping the cycles into a second level of first sensor waveform groups, with members of the same second level first sensor waveform group having similar first sensor waveform shapes to the group members and being substantially chronologically contiguous to the other second level first sensor waveform group members.

44. A computer program in a computer readable media as in claim 43, wherein the cycles have an associated second sensor waveform, the program further comprising instructions for:

grouping the cycles into a first level of second sensor waveform groups, with members of the same first level second sensor waveform group having similar second sensor waveform shapes to the other first level second sensor waveform group members;

grouping the cycles into a second level of second sensor waveform groups, with members of the same second level second sensor waveform group having similar waveforms to the other second level second sensor waveform group members and being substantially chronologically contiguous to the other second level second sensor group members; and

grouping the cycles into a third level of beat groups, wherein the members of the third level cycle group are substantially all members of both the same second level first sensor waveform group and the same second level second sensor waveform group.

45. A computer program in a computer readable media as in claim 43, wherein the cycles have an associated second sensor waveform, the program further comprising instructions for:

grouping the cycles into a second level of second sensor waveform groups, with members of the same second level second sensor waveform group having similar intervals to the other second level second sensor group members and being substantially chronologically contiguous to the other second level second sensor group members; and

grouping the cycles into a third level of cycle groups, wherein members of the third level beat group are substantially all members of both the same second level first sensor waveform group and the same second level second sensor waveform group, and wherein the members of the third level cycle group are substantially chronologically coextensive with each other.

46. A computer program in a computer readable media as in claim 42, the program further comprising instructions for:

selecting at a first sensor waveform from a first sensor first level waveform group as an exemplar waveform from that group and performing one of a step of storing, printing, displaying, and transmitting the exemplar waveform, as an example of the waveforms of that first level first sensor group.

47. A computer program in a computer readable media as in claim 43, the program further comprising instructions for:

selecting a second level, first sensor group, selecting at a first sensor waveform from the selected second level first sensor waveform group as an exemplar waveform from second level first sensor group, determining the time or cycle repeat length of that second level first sensor waveform group, and performing one of a step of storing, printing, displaying, and transmitting the exemplar waveform and repeat length, such that a second level first sensor waveform group is compressed by selecting a waveform and number of repeats to represent the second level first waveform group.

48. A computer-implemented software system in data communications with an implantable medical device (IMD), the software system in combination with the medical device comprising:

means for storing physiological data in the IMD;

means for retrieving said physiological data;

means for extracting cycle segments;

means for grouping into identity groups said cycle segments;

means for marking group membership among said identity groups;

means for storing said group membership; and

means for displaying said group membership.

49. The combination of claim 48 wherein said physiological data includes at least one cardiac cycle.

50. The combination of claim 49 wherein said cardiac cycle includes EGM data, ECG data, intervals between sensed and paced events, and atrial and ventricular cycles.