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

• URL: http://arxiv.org/abs/2308.08410v2
• Date: Tue, 5 Dec 2023 15:52:36 GMT
• Title: Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach
• Authors: Thomas Grandits, Jan Verh\"ulsdonk, Gundolf Haase, Alexander Effland, Simone Pezzuto
• Abstract summary: We introduce a novel method, Geodesic-BP, to solve the inverse eikonal problem. We show that Geodesic-BP can reconstruct a simulated cardiac activation with high accuracy in a synthetic test case. Given the future shift towards personalized medicine, Geodesic-BP has the potential to help in future functionalizations of cardiac models.
• Score: 39.36827689390718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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