Learning System for Cardiac CPM Matrix Estimation

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Solution Overview

Problem

Current methods for generating comprehensive current-to-position (CPM) matrices in cardiac mapping are time and labor-intensive, requiring extensive data acquisition across various locations, which hinders efficient visualization and treatment of cardiac arrhythmias like atrial fibrillation.

Innovation Solution

A system and method utilizing a learning system to estimate a supplemented CPM matrix from a sparse CPM matrix, leveraging historical data to correlate electrical signals with catheter locations, allowing for the generation of new CPM data without the need for extensive location information, thereby reducing the time required for data acquisition.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If comprehensive CPM matrices are generated using traditional data acquisition methods, then mapping precision is improved, but time consumption and labor requirements increase significantly

Engineering Contradiction:

Improvemapping precisionVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by collecting historical CPM matrix data and training the neural network model before the actual cardiac mapping procedure. This pre-training phase enables the system to rapidly generate supplemented CPM matrices during the procedure without requiring extensive real-time data acquisition, thus reducing time consumption while maintaining mapping precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual copy of the comprehensive CPM matrix by using the neural network to generate supplemented CPM matrices from sparse input data. Instead of physically acquiring data at every possible location, the system copies the essential mapping information through intelligent algorithms, significantly reducing the time and labor required for data acquisition.

Inventive Principle:

Principle #26Copying

2Measurement precision

If comprehensive CPM matrices are generated using traditional data acquisition methods, then mapping precision is improved, but labor requirements increase significantly

Engineering Contradiction:

Improvemapping precisionVSAvoidlabor requirements

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically generating supplemented CPM matrices through the trained neural network without requiring manual data collection at numerous locations. The algorithm independently processes sparse input data and produces comprehensive mapping results, eliminating the need for extensive manual labor in data acquisition while maintaining high mapping precision.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Inventive Principle:

Principle #26Copying

3Productivity

If sparse CPM matrices are used directly, then time and labor are reduced, but mapping resolution and accuracy deteriorate

Engineering Contradiction:

Improvedata acquisition efficiencyVSAvoidmapping resolution

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system applies parameter changes by transforming the density and completeness parameters of the CPM matrix. The neural network takes sparse CPM matrices with lower data density and converts them into supplemented matrices with higher effective density, thereby improving mapping resolution while maintaining rapid data acquisition efficiency.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Inventive Principle:

Principle #10Preliminary action

4Loss of time

If supplemented CPM matrices are generated from sparse data using learning systems, then data acquisition time is reduced, but system complexity increases

Engineering Contradiction:

Improvedata acquisition timeVSAvoidsystem complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system replaces the mechanical data acquisition process with an intelligent computational system. Instead of physically moving the catheter to numerous locations to collect data, the system uses a trained neural network to computationally generate the necessary mapping information from sparse inputs, thereby reducing data acquisition time while managing system complexity through software-based solutions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20220338939A1System and method to determine the location of a catheter

Publication Date: 2022.10.27 BIOSENSE WEBSTER (ISRAEL) LTD

AI summary

Systems, devices, and techniques are disclosed for automatically generating CPM matrices. The system includes a processor configured to receive a plurality of historical, sparse CPM matrices and a plurality of historical, supplemented CPM matrices, wherein each sparse CPM matrix is associated with a respective supplemented CPM matrix; train a learning system based on the plurality of historical, sparse CPM matrices and the plurality of historical, supplemented CPM matrices, wherein the learning system is trained so as to generate a supplemented CPM matrix given a sparse CPM matrix; receive, by the trained learning system, a new, sparse CPM matrix; and generate, with the trained learning system, a new supplemented CPM matrix.