Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach

TL;DR

This paper introduces Geodesic-BP, a novel GPU-accelerated machine learning method for non-invasively reconstructing patient-specific cardiac electrophysiology models from surface ECGs, demonstrating high accuracy in synthetic and real data.

Contribution

The study presents Geodesic-BP, a new approach for solving the inverse eikonal problem that enables efficient, accurate, and non-invasive cardiac model personalization from ECG data.

Findings

High accuracy in synthetic cardiac activation reconstruction

Promising results on a rabbit model dataset

Potential for clinical application in personalized medicine

Abstract

The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive manner. Nonetheless, the fitting procedure remains a challenging task. The present study introduces a novel method, Geodesic-BP, to solve the inverse eikonal problem. Geodesic-BP is well-suited for GPU-accelerated machine learning frameworks, allowing us to optimize the parameters of the eikonal equation to reproduce a given ECG. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case, even in the presence of modeling inaccuracies. Furthermore, we apply our algorithm to a publicly available dataset of a biventricular rabbit model, with promising results. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models meeting clinical time constraints while maintaining the physiological accuracy of state-of-the-art cardiac models.