Methodology for automated signal morphology analysis in implantable electrotherapy and diagnostic systems

• US 8,874,196 B2
• Filed: 03/03/2010
• Issued: 10/28/2014
• 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: Woellenstein, Matthias Daniel, Simms, Howard, Mazar, Scott, Sweeney, Robert
• Primary Examiner(s): Koharski, Christopher D
• Assistant Examiner(s): KIMBALL, JEREMIAH T
• Application Number:
  US12/716,521
• Publication Number:
  US 20100160806A1
• Time in Patent Office: 1,700 Days
• Field of Search: 600/509
• US Class Current: 600/509
• 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. An advanced patient management system, comprising:

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

an external device including a repeater unit configured to collect the plurality of intra-cardiac electrograms and the plurality of event markers from the implantable medical device, the external device configured to;

compile the plurality of intra-cardiac electrograms and the plurality of event markers over time as waveform data;

categorize morphological aspects of the plurality of intra-cardiac electrograms into groups each representing a type of event marker of the plurality of event markers; and

compare the morphological aspects of the plurality of intra-cardiac electrograms within at least one of the groups to identify changes in the morphological aspects of the plurality of intra-cardiac electrograms over time.

2. The system of claim 1, wherein the electrograms are generated using a plurality of intra-cardiac leads.

3. The system of claim 1, wherein the event markers represent events associated with leads of the implantable medical device.

4. The system of claim 1, wherein the waveform data includes a combination of electrograms having the same event markers at a given point in time.

5. The system of claim 1, wherein the repeater is configured to compile the plurality of electrograms.

6. The system of claim 1, wherein the external device further comprises a host in communication with the repeater through a network, wherein the host is configured to compile the plurality of electrograms.

7. 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 waveform data;

categorizing morphological aspects of the collected electrical signals into groups each representing a type of event marker of the collected event markers; and

detecting changes in morphological aspects of the collected electric signals over time by comparing the morphological aspects of the collected electrical signals within at least one of the groups using an external device configured to interrogate the implanted device.

8. The method of claim 7, further comprising communicating information about the collected electric signals and event markers to physician for analysis.

9. The method of claim 7, wherein collecting the electric signals and event markers includes collecting the electric signals and event markers using a repeater and communicating information about the collected electric signals and event markers to a host unit in communication with the repeater through a network, and wherein detecting the changes including detecting the changes automatically by the repeater or the host.

10. The method of claim 7, 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.

11. The method of claim 7, wherein detecting the changes includes comparing the current waveform data to a predetermined waveform data.

12. The method of claim 7, 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.

13. The method of claim 7, wherein detecting the changes includes extracting comparable waveform data.

14. The method of claim 13, wherein extracting comparable waveform data includes extracting waveform data specific to a lead of a pulse generator.

15. The method of claim 7, wherein compiling the collected electric signals and event markers as current waveform data includes binning the waveform data.

16. The method of claim 15, wherein binning the waveform data includes binning the waveform data based on maximum tracking rate (MTR) versus paced events at a lower rate limit (LRL).

17. The method of claim 15, wherein 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.

18. The method of claim 7, further comprising using short and long term waveform data deviations to detect system faults.

19. The method of claim 7, wherein detecting the changes in waveform data includes using algorithms to detect the changes, the algorithms selected from higher order linear algorithms, non-linear algorithms, hysteretic algorithms, and adaptive threshold algorithms.

20. The method of claim 7, further comprising graphically presenting waveform data deviations for analysis by a medical technician.