EP-PINNs: Cardiac Electrophysiology Characterisation using Physics-Informed Neural Networks

TL;DR

EP-PINNs utilize physics-informed neural networks to accurately simulate and estimate electrophysiological tissue properties from sparse data, aiding in arrhythmia diagnosis and treatment.

Contribution

This paper introduces EP-PINNs, a novel neural network approach that combines physics-based modeling with data-driven methods for electrophysiology analysis.

Findings

Successfully reconstructs spatio-temporal action potential evolution.

Predicts key electrophysiological parameters such as APD and excitability.

Identifies tissue heterogeneities linked to arrhythmogenic pathologies.

Abstract

Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation, but it is notoriously difficult to perform. We present EP-PINNs (Physics-Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation, from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.