Simulated Heart Anatomy Interface for Noninvasive Arrhythmia Localization
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Solution Overview
Problem
Current methods for identifying the source locations of heart disorders, such as arrhythmias, are complex, cumbersome, and expensive, and can lead to complications, while existing technologies like electrophysiology catheters and body surface vests are limited and ineffective in sensing certain areas of the heart.
Innovation Solution
A machine learning-based system (MLMO) generates a classifier for classifying electromagnetic data from the heart using a computational model and simulated electromagnetic outputs, allowing for the identification of arrhythmia sources through simulated cardiograms and vectorcardiograms, which can be trained using a combination of simulated and actual patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrophysiology catheters are used to identify arrhythmia sources, then measurement precision is improved, but device complexity and cost increase, and harmful factors are introduced
Solution Approach 1:
The patent creates a computational copy of the patient's heart anatomy using imaging data (CT or MRI scans) to generate a 3D model. This virtual model is then used to simulate electromagnetic signals and identify arrhythmia sources without requiring physical catheter insertion. The simulation replicates the electrical activity and signal propagation patterns that would be measured by invasive catheters, providing equivalent diagnostic information through non-invasive means.
Solution Approach 2:
The patent replaces the mechanical electrophysiology catheter system with a computational simulation system. Instead of physically inserting electrodes into the heart chambers to measure electrical signals, the system uses computer algorithms to simulate electromagnetic signal propagation through the 3D anatomical model. This substitution eliminates the need for mechanical intervention while maintaining the ability to detect and localize arrhythmia sources.
2Ease of operation
If body surface vests with electrodes are used to collect measurements, then ease of operation is improved, but measurement precision deteriorates due to inability to sense septal areas
Solution Approach 1:
The patent transitions from 2D surface electrode measurements to 3D volumetric analysis by creating a three-dimensional computational model of the heart anatomy. This 3D model allows the system to calculate and analyze electromagnetic signals throughout the entire volume of the heart, including the interventricular and interatrial septa, which are inaccessible to surface electrodes. The dimensional transformation enables comprehensive signal propagation analysis through all cardiac structures.
Solution Approach 2:
The patent introduces a computational model as an intermediary between the patient's anatomy and the analysis system. This virtual model acts as a mediator that translates limited surface measurements into comprehensive 3D electromagnetic field distributions. The computational intermediary fills in the gaps by calculating signals in regions not directly measured, such as the septal areas, using physics-based simulations of electrical propagation through cardiac tissue.
3Measurement precision
If invasive procedures are used to identify arrhythmia sources, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent performs preliminary computational analysis using non-invasive imaging data and simulations to identify potential arrhythmia sources before any clinical intervention. By pre-localizing the arrhythmia generator through virtual experimentation and signal propagation modeling, the system provides a roadmap that guides subsequent clinical procedures, reducing the need for repeated invasive attempts and minimizing exposure to procedural risks.
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.