Method and apparatus to find abnormal activations in intra-cardiac electrocardiograms based on specificity and sensitivity

Patent metadata

Field	Value
Publication number	US-11278233-B2
Application number	US-201916685496-A
Country	US
Kind code	B2
Filing date	Nov 15, 2019
Priority date	Nov 15, 2019
Publication date	Mar 22, 2022
Grant date	Mar 22, 2022

Abstract

Methods, apparatus, and systems for medical procedures are disclosed herein and include detecting points of an intra-cardiac area that exhibits abnormal activations, such as local abnormal ventricular activations (LAVAs). Points that exhibit such abnormal activations may be referred to as seed points that are identified during a first step of the process disclosed herein. The seed points may be identified using one or more inputs such as unipolar and bipolar mapping channels, body surface ECGs, past activations, neighboring points and the like during the first step which prioritizes high specificity over sensitivity. During a second step which prioritizes high sensitivity, electrical activations of neighboring points near the seed points are analyzed to determine if the activations are similar (e.g., have a similar time) as the abnormal activations corresponding to the corresponding seed points.

First claim

The invention claimed is:

1. A method for identifying abnormal activations in intracardiac electrograms, the method comprising:
identifying a seed point, based on a high specificity, with abnormal activations at a first time;
identifying at least one neighboring point proximate to the seed point;
determining a sensitivity of the at least one neighboring point, wherein the sensitivity is determined by determining that the at least one neighboring point exhibits activations at a similar time as the first time; and
identifying the neighboring point as an abnormal neighboring point based on determining that the neighboring point exhibits activations at a similar time as the first time.

2. The method of claim 1 , further comprising receiving and using input information, the input information comprising one or more of a bipolar ECG of a mapping channel, a distal and proximal unipolar ECG of a mapping channel, a lead body surface ECG, and an intra-cardiac spatial information.

3. The method of claim 2 , further comprising providing the input information to one or more analysis modules, the analysis modules comprising one or more of a QRS detection module, wavefront activations module, fractionation detecting module, and a local abnormal ventricular activation (LAVA) logic module.

4. The method of claim 3 , wherein the seed point is identified based on an output of the one or more analysis modules.

5. The method of claim 3 , wherein the QRS detection module comprises one or more of a pre-processing operations and a QRS detection logic operation.

6. The method of claim 5 , wherein the QRS detection logic operation outputs a start of QRS and an end of QRS.

7. The method of claim 3 , wherein the wavefront activations module comprises one or more of a preprocessing operations, a wavefront logic operation, a feature extraction operation, and a fuzzy logic operation.

8. The method of claim 3 , wherein the wavefront activations module outputs one or more sets of timestamps and fuzzy scores.

9. The method of claim 3 , wherein the fractionation detection module outputs an interval comprising a start of fractionation and an end of fractionation.

10. The method of claim 1 , wherein determining at least one of the seed points and the neighboring point is based on one or more of a fuzzy score, a time consistency and a location consistency.

11. The method of claim 10 , wherein the time consistency is based on identifying the abnormal activations at a previous activation within a deviation tolerance based on a cycle length.

12. The method of claim 10 , wherein the location consistency is based on a determination of a distance between the seed point and the neighboring point divided by a wavefront velocity.

13. The method of claim 12 , wherein the location consistency is further based on a deviation tolerance based on a cycle length.

14. The method of claim 1 , further comprising identifying a far field distance.

15. The method of claim 14 , further comprising determining that at least one neighboring point of the neighboring points is within the far field distance and identifying the neighboring point within the far field distance as a far field neighboring point.

16. The method of claim 1 , wherein the seed point is determined based on one or more of a QRS time, time consistency, fuzzy score, fractionation period, and slope amplitude.

17. The method of claim 1 , wherein the at least one neighboring point is within 12 mm from the seed point.

18. The method of claim 1 , wherein identifying the neighboring point as an abnormal neighboring point is based on one or more of a location consistency between the seed point and the neighboring point, a fuzzy score, and a slope amplitude.

19. An apparatus for identifying abnormal activations in intracardiac electrograms, the apparatus comprising:
a memory; and
a processor operatively coupled with the memory and in communication with the memory, the processor configured to:
identify a seed point with abnormal activations at a first time, based on a high specificity;
identify at least one neighboring point proximate to the seed point;
determine that the neighboring point exhibits activations at a similar time as the first time; and
identify the neighboring point as an abnormal neighboring point based on determining that the neighboring point exhibits activations at a similar time as the first time.

20. A non-transitory computer-readable medium for identifying abnormal activations in intracardiac electrograms, the non-transitory computer-readable medium having instructions recorded thereon, that when executed by the processor, cause the processor to perform operations including:
identifying a seed point with abnormal activations at a first time, based on a high specificity;
identifying at least one neighboring point proximate to the seed point;
determining that the neighboring point exhibits activations at a similar time as the first time; and
identifying the neighboring point as an abnormal neighboring point based on determining that the neighboring point exhibits activations at a similar time as the first time.

Assignees

• Biosense Webster Israel Ltd

Inventors

• Ravuna Eliyahu
• Kadoshi Yaron
• Itah Refael
• Nakar Elad
• Alroy Levy Michal

Classifications

• A61B5/366 Primary

  Detecting abnormal QRS complex, e.g. widening · CPC title

• A61B5/283

  Invasive · CPC title

• A61B5/349 Primary

  Detecting specific parameters of the electrocardiograph cycle · CPC title

• A61B5/7264

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

• A61B5/053 Primary

  Measuring electrical impedance or conductance of a portion of the body · CPC title