ArticlePloS one2020
Deep learning-based reduced order models in cardiac electrophysiology.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Probabilistic Cardiac Digital Twins for Robust Patient-Specific Modeling.bioRxiv : the preprint server for biology · 2026Article
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Models of cardiomyocyte-non-myocyte electrical interactions.The Journal of physiology · 2026Review
- Full-field surrogate modeling of cardiac electrophysiology encoding geometric variability.Computer methods in applied mechanics and engineering · 2026Article
- Learning cardiac activation and repolarization times with operator learning.PLoS computational biology · 2026Article
- Multifidelity deep learning modeling of spatiotemporal lung mechanics.Frontiers in physiology · 2025Article
- Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators.NPJ digital medicine · 2024Article
- Branched Latent Neural Maps.Computer methods in applied mechanics and engineering · 2024Article
- Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches.Sensors (Basel, Switzerland) · 2023Article
- Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification.Frontiers in physiology · 2022Article
- A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms.Frontiers in physiology · 2022Review
- Pacemaking function of two simplified cell models.PloS one · 2022Article
- Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications.Biophysics reviews · 2021Article
- Data integration for the numerical simulation of cardiac electrophysiology.Pacing and clinical electrophysiology : PACE · 2021Review
- Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function.International journal for numerical methods in biomedical engineering · 2021Article
- POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium.Frontiers in physiology · 2021Article
- EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.Frontiers in cardiovascular medicine · 2021Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depolarization cycle occurring at every heart beat that models the time evolution of the electrical potential across the cell membrane, as well as a set of ionic variables. Multiple solutions of these systems, corresponding to different model inputs, are required to evaluate outputs of clinical interest, such as activation maps and action potential duration. More importantly, these models feature coherent structures that propagate over time, such as wavefronts. These systems can hardly be reduced to lower dimensional problems by conventional reduced order models (ROMs) such as, e.g., the reduced basis method. This is primarily due to the low regularity of the solution manifold (with respect to the problem parameters), as well as to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To overcome this difficulty, in this paper we propose a new, nonlinear approach relying on deep learning (DL) algorithms-such as deep feedforward neural networks and convolutional autoencoders-to obtain accurate and efficient ROMs, whose dimensionality matches the number of system parameters. We show that the proposed DL-ROM framework can efficiently provide solutions to parametrized electrophysiology problems, thus enabling multi-scenario analysis in pathological cases. We investigate four challenging test cases in cardiac electrophysiology, thus demonstrating that DL-ROM outperforms classical projection-based ROMs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.