EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.

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. It is, however, 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 u

SUBMITTER: Herrero Martin C 

PROVIDER: S-EPMC8850959 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature