Simulated Cardiogram Calibration for Arrhythmia Source Localization
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Solution Overview
Problem
Current methods for identifying the source of heart disorders such as arrhythmias are complex, cumbersome, and expensive, and can lead to complications, while existing simulation methods are limited in accuracy and unable to detect interventricular and interatrial septa sources.
Innovation Solution
A machine learning-based system (MLMO) generates a classifier using simulated cardiograms to accurately identify heart disorder sources by training on a computational model of electromagnetic heart data, incorporating patient-specific features, and normalizing EM data for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrophysiology catheter or body surface vest methods are used to identify arrhythmia source locations, then measurement capability is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent creates a computational copy of the patient's heart anatomy using imaging data (CT or MRI) to generate a 3D mesh model. This virtual anatomical model serves as a simplified substitute for physical catheters or vests, allowing electromagnetic field simulations to identify arrhythmia sources without requiring complex medical devices to be inserted into or worn by the patient.
Solution Approach 2:
The patent replaces mechanical measurement systems (physical catheters with electrodes or body surface vests) with a computational electromagnetic field simulation system. By solving Maxwell's equations in the virtual anatomical model, the system identifies arrhythmia source locations through mathematical calculations rather than physical sensor measurements, eliminating the need for complex mechanical devices.
2Measurement precision
If electrophysiology catheter or body surface vest methods are used to identify arrhythmia source locations, then measurement capability is improved, but procedure cost and patient risk increase
Solution Approach 1:
The patent creates a computational copy of the patient's heart anatomy using imaging data (CT or MRI) to generate a 3D mesh model. This virtual anatomical model serves as a simplified substitute for physical catheters or vests, allowing electromagnetic field simulations to identify arrhythmia sources without requiring complex medical devices to be inserted into or worn by the patient.
Solution Approach 2:
The patent replaces mechanical measurement systems (physical catheters with electrodes or body surface vests) with a computational electromagnetic field simulation system. By solving Maxwell's equations in the virtual anatomical model, the system identifies arrhythmia source locations through mathematical calculations rather than physical sensor measurements, eliminating the need for complex mechanical devices.
3Productivity
If existing simulation methods are used, then computational speed is improved, but measurement precision and ability to detect septa sources deteriorate
Solution Approach 1:
The patent performs preliminary actions by first creating a detailed 3D mesh model of the patient's specific heart anatomy from imaging data, and pre-calculating the electromagnetic field distribution throughout the cardiac tissue. This preparatory computational work enables accurate source localization when arrhythmia signals are detected, while the mesh structure is designed to specifically include interventricular and interatrial septa regions that were previously undetectable.
Solution Approach 2:
The patent applies local quality by creating a patient-specific 3D mesh model that captures detailed anatomical variations in different regions of the heart, particularly in the interventricular and interatrial septa areas. The electromagnetic field simulation uses this heterogeneous mesh structure with varying tissue properties (conductivity, permittivity) assigned to different anatomical regions, enabling precise localization of arrhythmia sources in previously undetectable septal regions.
Data Source
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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.