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Front. Physiol. | doi: 10.3389/fphys.2021.734178

Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing

Provisionally accepted
The final, formatted version of the article will be published soon
  • 1School of Computational Science and Engineering, Georgia Institute of Technology, United States
  • 2School of Physics, College of Sciences, Georgia Institute of Technology, United States
  • 3Department of Physics and Astronomy, California State University Northridge, United States

The electrical signals triggering the heart's contraction are governed by nonlinear processes that can produce complex irregular activity, especially during or preceding the onset of cardiac arrhythmias. Forecasts of cardiac voltage time series in such conditions could allow new opportunities for intervention and control but would require efficient computation of highly accurate predictions. Although machine-learning (ML) approaches hold promise for delivering such results, nonlinear time-series forecasting poses significant challenges. In this manuscript, we study the performance of two recurrent neural network (RNN) approaches along with echo state networks (ESNs) from the reservoir computing (RC) paradigm in predicting cardiac voltage data in terms of accuracy, efficiency, and robustness. We show that these ML time-series prediction methods can forecast synthetic and experimental cardiac action potentials for around 15 beats with a high degree of accuracy, with ESNs typically two orders of magnitude faster than RNN approaches for the same network size.

Keywords: reservoir computing, recurrent neural network, Echo state network, time series forecasting, cardiac action potential

Received: 30 Jun 2021; Accepted: 27 Aug 2021.

Copyright: © 2021 Shahi, Marcotte, Herndon, Fenton, Shiferaw and Cherry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Shahrokh Shahi, School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, Colorado, United States,