METHODOLOGY FOR AUTOMATED SIGNAL MORPHOLOGY ANALYSIS IN IMPLANTABLE ELECTROTHERAPY AND DIAGNOSTIC SYSTEMS

• US 20070260149A1
• Filed: 05/04/2006
• Published: 11/08/2007
• Est. Priority Date: 05/04/2006
• Status: Active Grant

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: Sweeney, Robert, Mazar, Scott, Woellenstein, Matthias, Simms, Howard
• Granted Patent: US 7,702,383 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/333 Recording apparatus specially adapted therefor
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle

Abstract

Systems and related methods for analyzing data sensed from a device implanted in a patient, such as a cardiac pacing system. The system detects and evaluates electric signals within the patient that share a common event marker. By using algorithms and graphical presentation of the collected signals having common event markers, deviations in signals over time can be identified and evaluated in consideration of taking further action related to the patient and the implanted device. The system can also be used in conjunction with an advanced patient management system that includes a programmer or repeater capable of gathering information from the implanted device and transmitting the data to a host via a communications network for evaluation at a remote location.

Claims

1. A method of monitoring electrical activity in a patient, the method comprising:

monitoring electrical signals within the patient using an implanted device;

identifying event markers corresponding to events identified by the implanted device;

categorizing into groups the time periods of the electrical signals representing a type of event marker; and

comparing the time periods of the electrical signals within at least one of the groups to identify changes over time.

2. The method of claim 1, further comprising reporting the comparison for analysis by a physician.

3. The method of claim 1, wherein comparing the time periods includes plotting a plurality of the time periods on a graph.

4. The method of claim 1, further comprising analyzing the identified changes over time to determine a recommended action.

5. The method of claim 1, wherein monitoring the electrical signals includes monitoring intra-cardiac electrograms generated by an implanted cardiac rhythm management device.

6. The method of claim 5, wherein the time periods of the electrical signal include waveform data and identify threshold deviations in the waveform data over time.

7. The method of claim 1, wherein the electrical signals originate from a signal source selected from the group consisting of a mechanical sensor, a chemical sensor, intrinsic electrical activity generated by the patient'"'"'s body.

8. A patient analysis system, comprising:

an implanted device configured to monitor electrical signals within the patient; and

an analyzing module configured to identify individual events represented in the electrical signals and compare the electrical signals associated with like events that occur over a predetermined time period;

wherein the system is configured to highlight electric signals of the like events that have abnormal occurrences.

10. The system of claim 8, wherein the electrograms are generated by a plurality of intra-cardiac leads.

11. The system of claim 8, wherein the event markers represent events associated with leads of the implanted medical device.

12. The system of claim 8, wherein the waveform data includes a combination of electrograms having the same event markers at a given point in time.

13. The system of claim 8, wherein the repeater is configured to compile the plurality of electrograms.

14. The system of claim 13, further comprising a host in communication with the repeater through a network, wherein the host is configured to compile the plurality of electrograms.

15. The system of claim 13, wherein the system is configured to identify the threshold deviations.

9. An advanced patient management system, comprising:

an implanted medical device configured to generate a plurality of intra-cardiac electrograms and a plurality of event markers; and

a repeater unit configured to collect the electrogram signal;

wherein the system compiles the plurality of electrogram and event markers over time as waveform data and identifies threshold deviations in the waveform data over time.

16. A method of analyzing signal morphology in a patient using an implanted device, the method comprising:

generating a plurality of electric signals and event markers with the implanted device;

collecting the electric signals and event markers;

compiling the collected electric signals and event markers as current waveform data; and

detecting threshold deviations in the waveform data over time.

17. The method of claim 16, further comprising communicating information about the collected electric signals and event markers to physician for analysis.

18. The method of claim 16, wherein a repeater collects the electric signals and event markers, and information about the collected electric signals and event markers is communicated to a host unit.

19. The method of claim 16, wherein compiling the collected electric signals and event markers includes aligning segments of at least two of the electric signals that represent a given beat of a patient'"'"'s heart and that share a selected event marker.

20. The method of claim 16, wherein detecting threshold deviations includes comparing the current waveform data to a predetermined waveform data.

21. The method of claim 16, wherein the step of detecting deviations is conducted by a physician.

22. The method of claim 18, wherein the step of detecting threshold deviations is conducted automatically by the repeater unit or the host unit.

23. The method of claim 16, wherein event markers include at least one of a atrial sense marker, a left ventricular sense marker, a right ventricular sense marker, an atrial pace marker, a left ventricular pace marker, and a right ventricular pace marker.

24. The method of claim 16, wherein the step of detecting threshold deviations includes extracting comparable waveform data.

25. The method of claim 24, wherein extracting comparable waveform data includes extracting waveform data specific to a lead of a pulse generator.

26. The method of claim 16, wherein the step of compiling the collected electric signals and event markers as current waveform data includes binning the waveform data.

27. The method of claim 26, wherein the step of binning the waveform data includes binning the waveform data based on maximum tracking rate (MTR) versus paced events at a lower rate limit (LRL).

28. The method of claim 26, wherein the step of binning the waveform data includes binning the waveform data based on altered pacing support states comprising post-shock pacing or atrial tachycardia response mode switching.

29. The method of claim 16, further comprising using short and long term waveform data deviations to detect system faults.

30. The method of claim 16, wherein the step of detecting threshold deviations in waveform data includes using algorithms to detect the threshold deviations, the algorithms selected from higher order linear algorithms, non-linear algorithms, hysteretic algorithms, and adaptive threshold algorithms.

31. The method of claim 16, further comprising graphically presenting waveform data deviations for analysis by a medical technician.