System and method to detect stable arrhythmia heartbeat and to calculate and detect cardiac mapping annotations

Patent metadata

Field	Value
Publication number	US-2021386355-A1
Application number	US-202117341917-A
Country	US
Kind code	A1
Filing date	Jun 8, 2021
Priority date	Jun 10, 2020
Publication date	Dec 16, 2021
Grant date	—

Abstract

A system and method for detecting a mapping annotation for an electrophysiological (EP) mapping system. The system includes a processor comprising a machine learning algorithm configured to receive a first heartbeat at an identified cardiac spatial location including a first set of attributes information corresponding to the first heartbeat; receive a second heartbeat at the identified cardiac spatial location including a second set of attributes information corresponding to the second heartbeat; compare the first set of attributes information with the second set of attributes information; and determine which of the first heartbeat and the second heartbeat has optimal characteristics based on the compared attribute information.

First claim

What is claimed is:

1 . A system for detecting a heartbeat with optimal characteristics for an electrophysiological (EP) mapping system, the system comprising:
a processor comprising a machine learning algorithm configured to:
receive a first heartbeat at an identified cardiac spatial location including first attribute information corresponding to the first heartbeat;
receive a second heartbeat at the identified cardiac spatial location including a second attribute information corresponding to the second heartbeat;
compare the first attribute information with the second attribute information; and
determine which of the first heartbeat and the second heartbeat has optimal characteristics based on the compared attribute information.

2 . The system of claim 1 , wherein the machine learning algorithm further outputs the determination of the heartbeat with optimal characteristics to the EP mapping system.

3 . The system of claim 1 , wherein at least one of the first heartbeat including first attribute information and the second heartbeat including second attribute information is stored in a database in communication with the processor.

4 . The system of claim 3 , wherein one of the first heartbeat and the second heartbeat is a heartbeat manually accepted in the EP mapping system and the other one of the first heartbeat and the second heartbeat is a heartbeat deleted by a physician from the EP mapping system at the same spatial location up to a threshold tolerance.

5 . The system of claim 4 , wherein the machine learning algorithm learns to determine which of the first heartbeat and the second heartbeat has optimal characteristics based on the results of the manually accepted heartbeat in the EP mapping system and the heartbeat deleted by the physician in the EP mapping system, where the manually accepted heartbeat and the deleted heartbeat are at the same location up to a threshold tolerance.

6 . The system of claim 1 , wherein the attribute information comprises at least one of:
an intracardiac electrogram signal received by an electrode of a catheter; an electrocardiogram signal received from a mapping electrode; an electrocardiogram signal received by one or more body surface electrodes; a tissue proximity indication (TPI) of the mapping electrode at a time of a reference annotation; a force value detected by a force sensor of a mapping catheter; a spatial location of the mapping electrode at the reference annotation; a respiration status vector around the reference annotation; a position of the reference annotation inside a respiration cycle; a difference between a current respiration cycle length and a previous respiration cycle length; a ratio of the current respiration cycle length and the previous respiration cycle length; a difference between the current respiration cycle length and an average or median respiration cycle length; a ratio of the current respiration cycle length and the average or median respiration cycle length; an indication of whether a physician manually accepted a heartbeat into the EP mapping system or deleted the heartbeat; and a distance of the mapping electrode at a current reference annotation from the spatial location of the same electrode at a previous reference annotation.

7 . The system of claim 1 , wherein the machine learning algorithm determines which of the first heartbeat and the second heartbeat has optimal characteristics based on at least one of the heartbeat with less noise, the heartbeat with a more obvious local activation time (LAT), the heartbeat with a more obvious late potential, the heartbeat with a more stable fractionation, and the heartbeat with less V interference.

8 . The system of claim 1 , wherein the determination of whether the first heartbeat or the second heartbeat has optimal characteristics based on the compared attribute information is a binary determination.

9 . The system of claim 1 , wherein the machine learning algorithm is a neural network.

10 . The system of claim 9 , wherein the neural network is a convolutional neural network.

11 . A method for detecting a heartbeat with optimal characteristics for an electrophysiological (EP) mapping system by a machine learning algorithm, the method comprising:
receiving first data comprising a first heartbeat at an identified cardiac spatial location including first attribute information corresponding to the first heartbeat; receiving second data comprising a second heartbeat at the identified cardiac spatial location including second attribute information corresponding to the second heartbeat; comparing the first data with the second data; and outputting a determination of which of the first heartbeat and the second heartbeat has optimal characteristics based on the comparing.

12 . The method of claim 11 , further comprising outputting the determination to the EP mapping system.

13 . A system for detecting a mapping annotation for an electrophysiological (EP) mapping system, the system comprising:
a processor comprising a machine learning algorithm configured to:
receive input data comprising attribute data for each of a plurality of heartbeats obtained at the same spatial location;
compare the attribute data for each of a plurality of heartbeats with predefined threshold values; and
determine which heartbeat to use as the mapping annotation based on the attribute data.

14 . The system of claim 13 , wherein the machine learning algorithm further outputs the determination of the heartbeat to use as the mapping annotation to the EP mapping system.

15 . The system of claim 13 , wherein one of the plurality of heartbeats is manually acquired by a physician in the EP mapping system.

16 . The system of claim 15 , wherein the machine learning algorithm learns to determine which of the plurality of heartbeats to use as the mapping annotation based on the results of the heartbeat acquired by the physician in the EP mapping system.

17 . The system of claim 13 , wherein the attribute data comprises at least one of:
an intracardiac electrogram signal received by an electrode of a catheter; an electrocardiogram signal received from a mapping electrode; an electrocardiogram signal received by one or more body surface electrodes; a local annotation time of an acquired heartbeat; a tissue proximity indication (TPI) of the mapping electrode at the time of a reference annotation; a force value detected by a force sensor of a mapping catheter a spatial location of the mapping electrode at the reference annotation; a respiration gating status; a respiration status vector around the reference annotation; a position of the reference annotation inside a respiration cycle; a difference between a current respiration cycle length and a previous respiration cycle length; a ratio of the current respiration cycle length and the previous respiration cycle length; a difference between the current respiration cycle length and an average or median respiration cycle length; a ratio of the current respiration cycle length and the average or median respiration cycle length; an indication of whether a physician manually accepted a heartbeat into the EP mapping system or deleted the heartbeat; an indication of whether there was any body surface activation at a time of this heartbeat; and a distance of the mapping electrode at a current reference annotation from the spatial location of the same electrode at a previous reference annotation.

18 . The system of claim 13 , wherein the machine lea

Assignees

• Biosense Webster Israel Ltd

Inventors

• Ravuna Eliyahu
• Botzer Lior
• Yarnitsky Jonathan
• Nakar Elad
• Katz Natan Sharon

Classifications

• A61B5/283

  Invasive · CPC title

• A61B5/339

  Displays specially adapted therefor · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/09

  Supervised learning · CPC title