Reconstructing Intracardiac Electrical Behavior from 12-Lead ECGs
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Solution Overview
Problem
Current methods for treating sudden cardiac death due to ventricular fibrillation, such as radio-frequency ablation therapy, have limited success and rely on invasive procedures that are expensive and burdensome, while non-invasive electrocardiographic imaging is time-consuming and costly, and unable to reconstruct interior electrical potentials within the myocardium.
Innovation Solution
A computer-based system using a machine learning model, trained with actual and simulated ECGs and intracardiac electrical behavior data, to reconstruct the internal electrical behavior of the heart without the need for medical imaging or special equipment, utilizing a 12-lead cardiac ECG to generate cardiac activation maps and transmembrane potentials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intracardiac electrogram procedure is used to accurately identify cardiac tissue source of arrhythmia, then measurement precision is improved, but device complexity and loss of time increase due to invasive catheter insertion and 2-hour procedure duration
Solution Approach 1:
The patent creates a virtual copy of the intracardiac electrogram procedure by using machine learning models to generate synthetic intracardiac electrical behavior data from standard 12-lead ECGs. This virtual reconstruction eliminates the need for actual catheter insertion and 2-hour procedures, providing accurate cardiac tissue source identification through computational simulation rather than physical invasion.
Solution Approach 2:
The patent replaces the mechanical invasive catheter-based measurement system with a computational machine learning system. Instead of physically inserting electrodes into the heart to measure electrical potentials, the system uses algorithms trained on simulation data to reconstruct intracardiac electrical behavior from non-invasive ECG surface potentials, substituting mechanical intervention with information processing.
2Ease of operation
If non-invasive electrocardiographic imaging (ECGi) is used to collect similar data, then ease of operation is improved, but device complexity increases due to multi-electrode vest and CT/MRI requirements
Solution Approach 1:
The patent creates a simplified computational model that copies the essential function of ECGi (reconstructing cardiac electrical activity) but uses standard 12-lead ECG inputs instead of complex multi-electrode vests and imaging equipment. The machine learning model processes readily available ECG data to produce intracardiac electrical behavior reconstructions, eliminating the need for specialized expensive equipment.
Solution Approach 2:
The patent replaces expensive, complex, one-time-use multi-electrode vests and imaging procedures with inexpensive, reusable machine learning algorithms that process standard ECG data. The computational model serves as a cost-effective alternative that can be applied repeatedly without requiring expensive disposable medical equipment.
3Ease of operation
If ECGi is used for non-invasive reconstruction, then ease of operation is improved, but measurement precision worsens due to inability to reconstruct interior electrical potentials within the myocardium
Solution Approach 1:
The patent performs preliminary training using extensive simulation data to prepare the machine learning model for accurate reconstruction tasks. By pre-training on synthetic intracardiac electrical behavior data from cardiac simulations, the model learns to accurately map surface ECG potentials to interior myocardial electrical potentials, enabling precise non-invasive reconstruction without requiring actual invasive measurements during operation.
Solution Approach 2:
The patent introduces machine learning models trained on simulation data as an intermediary between standard 12-lead ECGs and intracardiac electrical behavior reconstruction. This computational intermediary bridges the gap between non-invasive surface measurements and interior cardiac potentials, enabling accurate reconstruction of myocardial electrical activity without direct intracardiac electrode contact.
4Reliability
If radio-frequency ablation therapy is used to treat ventricular fibrillation, then reliability of treatment is improved, but loss of time and ease of operation worsen due to invasive catheter navigation and cauterization procedure
Solution Approach 1:
The patent performs preliminary reconstruction of intracardiac electrical behavior and cardiac activation maps using machine learning models before any ablation procedure. By accurately identifying the precise location and characteristics of arrhythmia sources through computational analysis of ECG data, the system enables targeted ablation planning that reduces procedure time and improves efficacy by focusing energy only on the most critical arrhythmogenic regions.
Data Source
AI summary
A computer-based system and process are disclosed for reconstructing the internal electrical behavior of a patient's heart based partly or wholly on the patient's electrocardiogram (ECG). The output of the process may include, for example, a cardiac activation map, and/or a representation of transmembrane potentials over time. The process advantageously does not require any medical imaging of the patient, and does not require any special medical equipment. For example, the patient's activation map and transmembrane potentials may be reconstructed based solely on a preexisting or newly-obtained 12-lead cardiac ECG of the patient. The process makes use of a machine learning model, such as a neural network based model, trained with actual and/or simulated ECGs and intracardiac electrical data (typically transmembrane potentials) of many thousands of patients. Because an insufficient quantity of such data exists for actual patients, model training may be performed using ECGs and intracardiac electrical data obtained through computer simulations.