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

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 I...

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Bibliographic Details
Published in:Frontiers in cardiovascular medicine 2022-02, Vol.8, p.768419
Main Authors: Herrero Martin, Clara, Oved, Alon, Chowdhury, Rasheda A, Ullmann, Elisabeth, Peters, Nicholas S, Bharath, Anil A, Varela, Marta
Format: Article
Language:English
Subjects:
arrhythmia (any)
atrial fibrillation
cardiac electrophysiology
Cardiovascular Medicine
optical mapping
parameter estimation
Physics Informed Neural Network (PINN)
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Summary: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 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 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.
ISSN:2297-055X
2297-055X
DOI:10.3389/fcvm.2021.768419