ArticleFrontiers in physiology2021
Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Emerging rapid detection methods for the monitoring of cardiovascular diseases: Current trends and future perspectives.Materials today. Bio · 2025Review
- Early prediction of CKD from time series data using adaptive PSO optimized echo state networks.Scientific reports · 2025Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- Reconstruction of three-dimensional scroll waves in excitable media from two-dimensional observations using deep neural networks.Physical review. E · 2023Article
- Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study.Machine learning with applications · 2022Article
- A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks.Chaos (Woodbury, N.Y.) · 2022Article
- Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.Computational and mathematical methods in medicine · 2022Article
- Quantifying Distributions of Parameters for Cardiac Action Potential Models Using the Hamiltonian Monte Carlo Method.Computing in cardiology · 2021Article
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Authors and funding
6 authors.
Funding
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Abstract
The electrical signals triggering the heart's contraction are governed by non-linear 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, non-linear 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 at least 15-20 beats with a high degree of accuracy, with ESNs typically two orders of magnitude faster than RNN approaches for the same network size.
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