Computational Heart Model for Non-Invasive Arrhythmia Source Localization
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Solution Overview
Problem
Current methods for identifying the source locations of heart disorders are complex, costly, and prone to complications, such as cardiac perforation and tamponade, and are unable to accurately sense arrhythmia sources in interventricular and interatrial septa areas.
Innovation Solution
A machine learning-based system that generates classifiers to identify electromagnetic source configurations within the heart by simulating electromagnetic states and outputs, using computational models to analyze vectorcardiograms and other clinical data, allowing for the identification of arrhythmia sources without the need for expensive and invasive procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive electrophysiology catheter methods are used to identify arrhythmia sources, then measurement precision is improved, but device complexity and patient risk increase due to cardiac perforation and tamponade complications
Solution Approach 1:
The patent creates a virtual copy of the patient's heart anatomy using imaging data (CT or MRI) to build a computational model. This digital twin allows for non-invasive simulation of electrical activity and arrhythmia source localization, eliminating the need for physical catheter insertion while maintaining diagnostic accuracy through sophisticated electromagnetic field modeling and machine learning algorithms.
Solution Approach 2:
The patent replaces the mechanical invasive catheter-based measurement system with a computational modeling and machine learning system. Instead of physically inserting electrodes into the heart, the system uses external imaging data combined with electromagnetic simulations and AI algorithms to identify arrhythmia sources, substituting mechanical intervention with information processing and computational analysis.
2Measurement precision
If invasive electrophysiology catheter procedures are performed, then arrhythmia source identification capability is improved, but device complexity and procedural cost increase
Solution Approach 1:
The patent creates a virtual copy of the patient's heart anatomy using imaging data (CT or MRI) to build a computational model. This digital twin allows for non-invasive simulation of electrical activity and arrhythmia source localization, eliminating the need for physical catheter insertion while maintaining diagnostic accuracy through sophisticated electromagnetic field modeling and machine learning algorithms.
Solution Approach 2:
The patent replaces the mechanical invasive catheter-based measurement system with a computational modeling and machine learning system. Instead of physically inserting electrodes into the heart, the system uses external imaging data combined with electromagnetic simulations and AI algorithms to identify arrhythmia sources, substituting mechanical intervention with information processing and computational analysis.
3Object-affected harmful factors
If body surface vest electrodes are used to collect measurements, then non-invasive measurement is achieved, but measurement precision decreases due to inability to sense interventricular and interatrial septa areas
Solution Approach 1:
The patent creates a virtual copy of the patient's heart anatomy using imaging data (CT or MRI) to build a computational model. This digital twin allows for non-invasive simulation of electrical activity and arrhythmia source localization, eliminating the need for physical catheter insertion while maintaining diagnostic accuracy through sophisticated electromagnetic field modeling and machine learning algorithms.
Solution Approach 2:
The patent transitions from two-dimensional body surface electrode measurements to a three-dimensional computational model of the heart's internal structure. By reconstructing the volumetric anatomy and electrical activity from external imaging data, the system can visualize and analyze arrhythmia sources in all spatial dimensions, including the previously inaccessible interventricular and interatrial septa regions.
Data Source
AI summary
Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.