Methods and systems for statistically analyzing electrograms for local abnormal ventricular activities and mapping the same

Patent metadata

Field	Value
Publication number	US-10398331-B2
Application number	US-201615367895-A
Country	US
Kind code	B2
Filing date	Dec 2, 2016
Priority date	Dec 4, 2015
Publication date	Sep 3, 2019
Grant date	Sep 3, 2019

Abstract

Cardiac activity (e.g., a cardiac electrogram) is analyzed for local abnormal ventricular activity (LAVA), such as by using a LAVA detection and analysis module incorporated into an electroanatomical mapping system. The module transforms the electrogram signal into the wavelet domain to compute as scalogram; computes a one-dimensional LAVA function of the scalogram; detects one or more peaks in the LAVA function; and computes a peak-to-peak amplitude of the electrogram signal. If the peak-to-peak amplitude does not exceed a preset amplitude threshold, then the module can compute one or more of a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function and a LAVA probability parameter for the electrogram signal.

First claim

What is claimed is:

1. A method of analyzing cardiac activity for local abnormal ventricular activity (LAVA), comprising:
receiving an electrogram signal at a signal processor; and
using the signal processor:
transforming the electrogram signal into the wavelet domain, thereby computing a scalogram;
computing a one-dimensional LAVA function of the scalogram;
detecting one or more peaks in the LAVA function;
computing a peak-to-peak amplitude of the electrogram signal, and, if the peak-to-peak amplitude does not exceed a preset amplitude threshold:
computing a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function; and
computing a LAVA probability parameter for the electrogram signal; and

generating a graphical representation of one or more of the LAVA lateness parameter and the LAVA probability parameter on a cardiac model.

2. The method according to claim 1 , wherein transforming the electrogram signal into the wavelet domain comprises applying a continuous wavelet transformation to the electrogram signal to compute the scalogram.

3. The method according to claim 1 , further comprising setting values of the scalogram less than a preset noise threshold to zero.

4. The method according to claim 1 , wherein computing a one-dimensional LAVA function of the scalogram comprises computing a one-dimensional LAVA function of the scalogram at a preset cardiac activity frequency.

5. The method according to claim 4 , wherein the preset cardiac activity frequency is 300 Hz.

6. The method according to claim 1 , wherein detecting one or more peaks in the LAVA function comprises:
detecting one or more dominant activity peaks in the LAVA function;
categorizing each dominant activity peak of one or more dominant activity peaks as a near-field peak, a far-field peak, or a noise peak.

7. The method according to claim 6 , wherein detecting a plurality of dominant activity peaks in the LAVA function comprises detecting one or more local maximum peaks in the LAVA function where a maximum value of a slope of the electrogram signal exceeds a preset discrete activity threshold within a preset refractory window surrounding the local maximum peak.

8. The method according to claim 7 , wherein the preset discrete activity threshold is 0.2 mV/msec.

9. The method according to claim 6 , wherein categorizing each dominant activity peak of one or more dominant activity peaks as a near-field peak, a far-field peak, or a noise peak comprises:
categorizing a dominant activity peak as a near-field peak if the LAVA function exceeds a preset near field threshold at the dominant activity peak;
categorizing the dominant activity peak as a far-field peak if the LAVA function exceeds a preset far field threshold and not the preset near field threshold at the dominant activity peak; and
categorizing the dominant activity peak as a noise peak otherwise.

10. The method according to claim 1 , wherein computing a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function comprises computing the LAVA lateness parameter as a time difference between:
a latest far-field peak of the one or more peaks detected in the LAVA function and a reference ECG QRS, if the one or more peaks detected in the LAVA function do not include any near-field peaks; and
a latest near-field peak of the one or more peaks detected in the LAVA function and the reference ECG QRS if the one or more peaks detected in the LAVA function do include at least one near-field peak.

11. The method according to claim 1 , wherein computing a LAVA probability parameter for the electrogram signal comprises computing a LAVA probability parameter using one or more of: the LAVA lateness parameter for the electrogram signal; a fractionation parameter for the electrogram signal; a number of peaks detected in the LAVA function; a number of far-field peaks detected in the LAVA function; a near field activity span for the electrogram signal; a number of isolated QRS activities for the electrogram signal; and a QRS activity duration for the electrogram signal.

12. The method according to claim 11 , wherein the fractionation parameter for the electrogram signal is computed as a number of sign changes in a slope of the electrogram signal between an earliest near-field peak detected in the LAVA function and a latest near-field peak detected in the LAVA function.

13. The method according to claim 11 , wherein the fractionation parameter for the electrogram signal is computed as a number of sign changes in a slope of the electrogram signal, after the electrogram signal has been filtered to remove noise, between an earliest near-field peak detected in the LAVA function and a latest near-field peak detected in the LAVA function.

14. The method according to claim 11 , wherein the QRS activity duration for the electrogram signal is computed using the one or more peaks detected in the LAVA function.

15. A method of analyzing a cardiac electrogram for local abnormal ventricular activity (LAVA) in an electroanatomical mapping system, the method comprising the electroanatomical mapping system:
transforming the cardiac electrogram into the wavelet domain, thereby computing a scalogram;
computing a one-dimensional LAVA function of the scalogram;
detecting one or more peaks in the LAVA function;
computing a peak-to-peak amplitude of the electrogram signal, and, if the peak-to-peak amplitude does not exceed a preset amplitude threshold, computing at least one of:
a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function; and
a LAVA probability parameter for the electrogram signal; and

generating a graphical representation of the at least one of the LAVA lateness parameter and the LAVA probability parameter on a cardiac model.

16. The method according to claim 15 , wherein detecting one or more peaks in the LAVA function comprises detecting one or more local maximum peaks in the LAVA function where:
a maximum value of a slope of the electrogram signal exceeds a preset discrete activity threshold within a preset refractory window surrounding the local maximum peak; and
the LAVA function exceeds at least a preset far field threshold at the local maximum peak.

17. An electroanatomical mapping system configured to analyze an electrogram signal for local abnormal ventricular activity (LAVA), the electroanatomical mapping system comprising:
a LAVA analysis processor configured to:
transform the electrogram signal into the wavelet domain, thereby computing a scalogram;
compute a one-dimensional LAVA function of the scalogram;
detect one or more peaks in the LAVA function; and
compute one or more of a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function and a LAVA probability parameter for the electrogram signal; and

a mapping processor configured to generate a graphical representation of the one or more of the LAVA lateness parameter and the LAVA probability parameter on a cardiac model.

18. The electroanatomical mapping system according to claim 17 , wherein the LAVA analysis processor is configured to compute the one or more of the LAVA lateness parameter and the LAVA probability parameter when a peak-to-peak amplitude of the electrogram signal does not exceed a preset amplitude threshold.

19. A method of analyzing cardiac activity for local abnormal ventricular activity (LAVA), comp

Assignees

• St Jude Medical Cardiology Div Inc
• Jais Pierre

Inventors

• Relan Jatin Surendra
• Jais Pierre

Classifications

• A61B2505/05

  Surgical care · CPC title

• A61B5/7275

  Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor · CPC title

• A61B5/7264

  Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems · CPC title

• A61B5/726 Primary

  using Wavelet transforms · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title