ArticleFrontiers in physiology2021
Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks.
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 14 papers.
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Who cites it
14 citing papers in PubMed, 19 citations in OpenAlex.
- All-optical closed-loop control of human cardiomyocyte networks exploiting holographic optogenetics.Communications engineering · 2026Article
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Smart Optogenetics for Real-Time Automated Control of Cardiac Electrical Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Panoramic voltage-sensitive optical mapping of contracting hearts using cooperative multiview motion tracking with 12 cameras.The Journal of physiology · 2026Article
- Atrial fibrillation in continuous flow-left ventricular assist device (CF-LVAD) patients: impact on pathophysiology.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Review
- Atrial Cardiomyopathy in Atrial Fibrillation: A Multimodal Diagnostic Framework.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Dreaming of electrical waves: Generative modeling of cardiac excitation waves using diffusion models.APL machine learning · 2024Article
- Atrial cardiomyopathy revisited-evolution of a concept: a clinical consensus statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), the Asian Pacific Heart Rhythm Society (APHRS), and the Latin American Heart Rhythm Society (LAHRS).Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Article
- Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Article
- Optical mapping and optogenetics in cardiac electrophysiology research and therapy: a state-of-the-art review.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation.Arrhythmia & electrophysiology review · 2023Review
- Reconstruction of three-dimensional scroll waves in excitable media from two-dimensional observations using deep neural networks.Physical review. E · 2023Article
- Terminating spiral waves with a single designed stimulus: Teleportation as the mechanism for defibrillation.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.
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