Detection of fractionated signals in stable arrhythmias

Patent metadata

Field	Value
Publication number	US-11969255-B2
Application number	US-202117548558-A
Country	US
Kind code	B2
Filing date	Dec 12, 2021
Priority date	Dec 12, 2021
Publication date	Apr 30, 2024
Grant date	Apr 30, 2024

Abstract

In an example, a method includes receiving a cardiac signal that is sensed by an electrode at a tissue location inside the heart. Fractionations are identified in the cardiac signal. The fractionations identified at the tissue location are compared between first and second cardiac cycles of the cardiac signal. Based on the comparing, a likelihood is estimated, that the tissue location is causing a stable arrhythmia. Based on the estimated likelihood, the tissue location is indicated to a user as likely to be causing the stable arrhythmia.

First claim

The invention claimed is:

1. A method, comprising:
receiving a cardiac signal that is sensed by an electrode at a tissue location inside the heart;
identifying fractionations in the cardiac signal;
comparing the fractionations, identified at the tissue location, between first and second cardiac cycles of the cardiac signal;
based on the comparing, estimating a likelihood that the tissue location is causing a stable arrhythmia; and
based on the estimated likelihood, indicating the tissue location to a user as likely to be causing the stable arrhythmia.

2. The method according to claim 1 , wherein indicating the tissue location comprises indicating to the user a score of the likelihood that the tissue location is causing the stable arrhythmia.

3. The method according to claim 1 , wherein indicating the tissue location comprises recommending ablating the tissue location.

4. The method according to claim 1 , wherein receiving the cardiac signal comprises receiving a bipolar electrogram.

5. The method according to claim 1 , wherein identifying the fractionations comprises estimating baseline noise of the cardiac signal.

6. The method according to claim 5 , wherein identifying the fractionations comprises dynamically adjusting the threshold for a non-linear energy operator (NLEO) according to a ratio between a baseline noise and a peak-to-peak value of the EGM signal, the baseline noise given by a minimum value of a sliding maximum window of an absolute value of the signal.

7. The method according to claim 1 , wherein identifying the fractionations comprises comparing time periods of the cardiac signal in which at least a minimal predefined portion of the cardiac cycle does not contain any fractionation.

8. The method according to claim 1 , wherein identifying the fractionations comprises calculating, over a time interval, a ratio between peak-to-peak voltage of the cardiac signal and a duration of the time interval, and using the ratio to discriminate the fractionations from noise.

9. The method according to claim 1 , wherein estimating the likelihood comprises applying a logistic-regression-based classifier.

10. The method according to claim 1 , wherein comparing the fractionations comprises adjusting time-onset and time-offset of at least one of the fractionations.

11. The method according to claim 1 , wherein identifying the fractionations in a given cardiac cycle comprises using an extended window-of-interest (WOI) to capture all fractionations starting within a cardiac cycle.

12. The method according to claim 1 , wherein estimating the likelihood is performed based on a number of zero crossings occurring in the cardiac signal within each fractionation.

13. The method according to claim 1 , wherein estimating the likelihood is performed based on a number of local extrema occurring in a low-pass filtered cardiac signal within each fractionation.

14. The method according to claim 1 , wherein comparing the fractionations comprises representing fractionation windows as triangles.

15. The method according to claim 1 , wherein indicating the tissue location comprises generating and displaying an electrophysiological (EP) map to the user, overlaid with a score of the likelihood that the tissue location is causing the stable arrhythmia.

16. The method according to claim 15 , and comprising, using a scale on a graphical user interface (GUI), adjusting a threshold score above which only tissue locations whose score is above that threshold are displayed on the EP map.

17. A system, comprising:
a memory configured to store cardiac signals; and
a processor, which is configured to:
receive a cardiac signal that is sensed by an electrode at a tissue location inside the heart;
identify fractionations in the cardiac signal;
compare the fractionations, identified at the tissue location, between first and second cardiac cycles of the cardiac signal;
based on the comparing, estimate a likelihood that the tissue location is causing a stable arrhythmia; and
based on the estimated likelihood, indicate the tissue location to a user as likely to be causing the stable arrhythmia.

18. The system according to claim 17 , wherein the processor is configured to indicate the tissue location by indicating to the user a score of the likelihood that the tissue location is causing the stable arrhythmia.

19. The system according to claim 17 , wherein the processor is configured to indicate the tissue location by recommending ablating the tissue location.

20. The system according to claim 17 , wherein the processor is configured to receive the cardiac signal by receiving a bipolar electrogram.

21. The system according to claim 17 , wherein the processor is configured to identify the fractionations by estimating baseline noise of the cardiac signal.

22. The system according to claim 21 , wherein the processor is configured to identify the fractionations by dynamically adjusting the threshold for a non-linear energy operator (NLEO) according to a ratio between a baseline noise and a peak-to-peak value of the EGM signal, the baseline noise given by a minimum value of a sliding maximum window of an absolute value of the signal.

23. The system according to claim 17 , wherein the processor is configured to identify the fractionations by comparing time periods of the cardiac signal in which at least a minimal predefined portion of the cardiac cycle does not contain any fractionation.

24. The system according to claim 17 , wherein the processor is configured to identify the fractionations by calculating, over a time interval, a ratio between peak-to-peak voltage of the cardiac signal and a duration of the time interval, and, using the ratio, to discriminate the fractionations from noise.

25. The system according to claim 17 , wherein the processor is configured to estimate the likelihood by applying a logistic-regression-based classifier.

26. The system according to claim 17 , wherein the processor is configured to compare the fractionations by adjusting time-onset and time-offset of at least one of the fractionations.

27. The system according to claim 17 , wherein the processor is configured to identify the fractionations in a given cardiac cycle by using an extended window-of-interest (WOI) to capture all fractionations starting within a cardiac cycle.

28. The system according to claim 17 , wherein the processor is configured to estimate the likelihood based on a number of zero crossings occurring in the cardiac signal within each fractionation.

29. The system according to claim 17 , wherein the processor is configured to estimate the likelihood based on a number of local extrema occurring in a low-pass filtered cardiac signal within each fractionation.

30. The system according to claim 17 , wherein the processor is configured to compare the fractionations by representing fractionation windows as triangles.

31. The system according to claim 17 , wherein the processor is configured to indicate the tissue location by generating and displaying an electrophysiological (EP) map to the user, overlaid with a score of the likelihood that the tissue location is causing the stable arrhythmia.

32. The system according to claim 31 , and comprising a scale on a graphical user interface (GUI), that the user can use to adjust a t

Assignees

• Biosense Webster Israel Ltd

Inventors

• Ravuna Eliyahu
• Yanovich Nir
• Etin Zait Natalia
• Zaides Leonid
• Segev Meytel
• Nakar Elad

Classifications

• A61B5/287

  Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS] · 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

• A61B5/7425

  Displaying combinations of multiple images regardless of image source, e.g. displaying a reference anatomical image with a live image · CPC title

• A61B5/364

  Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats · CPC title

• A61B5/352

  Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval · CPC title