Deep Learning for Model Correction in Cardiac Electrophysiological ImagingDownload PDF

Published: 28 Feb 2022, Last Modified: 16 May 2023MIDL 2022Readers: Everyone
Keywords: Physics-based learning, Electrophysiology, Deep learning, Simulations
TL;DR: In this work, we propose a learning framework able to learn complex cardiac electrophysiology dynamics via a fast baseline model and data, and thus showing promise regarding the automated learning of cardiac electrophysiology models error.
Abstract: Imaging the electrical activity of the heart can be achieved with invasive catheterisation however the resulting data is sparse and noisy. Mathematical modelling of cardiac electrophysiology can help the analysis but solving the associated mathematical systems can become unfeasible. It is often computationally demanding, for instance when solving for different patient conditions. We present a new framework to model the dynamics of cardiac electrophysiology at lower cost. It is based on the integration of a low-fidelity physical model and a learning component implemented here via neural networks. The latter acts as a complement to the physical part, and handles all quantities and dynamics that the simplified physical model neglects. We demonstrate that this framework allows us to reproduce the dynamics of the complex transmembrane potential and to correctly identify the relevant physical parameters, even when only partial measurements are available. This combined model-based and data-driven approach could improve cardiac electrophysiological imaging and provide predictive tools.
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: methodological development
Primary Subject Area: Learning with Noisy Labels and Limited Data
Secondary Subject Area: Application: Other
Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
Code And Data: https://github.com/Inria-Asclepios/APHYN-EP
4 Replies

Loading