Simulated Cardiogram Calibration for Arrhythmia Source Localization
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Solution Overview
Problem
Current methods for identifying the source locations of heart disorders are complex, cumbersome, and expensive, often involving costly and risky procedures like electrophysiology catheters, which can lead to complications and are ineffective in sensing certain arrhythmia sources.
Innovation Solution
A machine learning-based system that generates classifiers to identify electromagnetic source configurations within the heart using simulated anatomies and clinical data, allowing for the prediction of arrhythmia locations and guiding treatment procedures without the need for invasive and expensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electrophysiology catheters are used to identify arrhythmia source locations, then measurement precision is improved, but device complexity 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 scans) to generate a 3D model. This virtual anatomical model serves as a safe copy that can be manipulated and analyzed without risking patient safety, yet provides sufficient detail for accurate arrhythmia source localization through simulated cardiogram generation and comparison.
Solution Approach 2:
The patent replaces the mechanical electrophysiology catheter system with a computational modeling system. Instead of physically inserting catheters into the heart to map electrical activity, the system uses computer simulations to generate virtual cardiograms from the 3D anatomical model and compares them with actual patient ECG data to identify arrhythmia sources non-invasively.
2Measurement precision
If electrophysiology catheters are used to identify arrhythmia sources, then measurement precision is improved, but object-affected harmful factors increase
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 anatomical model serves as a safe copy that can be manipulated and analyzed without risking patient safety, yet provides sufficient detail for accurate arrhythmia source localization through simulated cardiogram generation and comparison.
Solution Approach 2:
The patent introduces a computational model as an intermediary between the patient's actual heart and the diagnostic analysis. The 3D anatomical model and simulated cardiograms act as intermediaries that translate complex internal heart electrical activity into comparable external ECG patterns, enabling accurate diagnosis without direct catheter contact and associated risks.
3Ease of operation
If body surface vests with electrodes are used to collect measurements, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary actions by first creating a detailed 3D anatomical model of the patient's heart using imaging data before conducting the actual diagnostic measurement. This pre-established anatomical framework enables the system to accurately interpret standard ECG measurements and pinpoint arrhythmia sources that would otherwise be undetectable with body surface electrodes alone.
Solution Approach 2:
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 anatomical model serves as a safe copy that can be manipulated and analyzed without risking patient safety, yet provides sufficient detail for accurate arrhythmia source localization through simulated cardiogram generation and comparison.
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.