ArticleCirculation. Arrhythmia and electrophysiology2020
Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.
Article in Circulation. Arrhythmia and electrophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 32 citations in OpenAlex.
- Complexity analysis of electrical activity during endocardial and epicardial biventricular mapping of ventricular fibrillation.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- Optical mapping: pioneering the frontier of preclinical antiarrhythmic drug evaluation.iScience · 2026Review
- Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography.Nature cardiovascular research · 2026Article
- Accurate estimation of lesion metrics in radiofrequency ablation using machine learning model.European heart journal open · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- Artificial intelligence and real-time directional guidance in arrhythmia management.Heart rhythm O2 · 2025Article
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- The Electrophysiology Lab of the Future.The Journal of innovations in cardiac rhythm management · 2025Article
- Contrastive pretraining improves deep learning classification of endocardial electrograms in a preclinical model.Heart rhythm O2 · 2025Article
- Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: anFrontiers in cardiovascular medicine · 2025Article
- Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation.Computing in cardiology · 2023Article
- Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?Biology · 2023Review
- Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.Frontiers in physiology · 2023Article
- Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.BMC medical informatics and decision making · 2022Article
- Atrial fibrillation driver identification through regional mutual information networks: a modeling perspective.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2022Article
- Genetic and non-genetic risk factors associated with atrial fibrillation.Life sciences · 2022Review
- Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation.International journal of molecular sciences · 2022Article
- Big Data in electrophysiology.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Understanding PITX2-Dependent Atrial Fibrillation Mechanisms through Computational Models.International journal of molecular sciences · 2021Review
- Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.Cardiovascular research · 2021Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAtrial fibrillation (AF) can be maintained by localized intramural reentrant drivers. However, AF driver detection by clinical surface-only multielectrode mapping (MEM) has relied on subjective interpretation of activation maps. We hypothesized that application of machine learning to electrogram frequency spectra may accurately automate driver detection by MEM and add some objectivity to the interpretation of MEM findings.
methodsTemporally and spatially stable single AF drivers were mapped simultaneously in explanted human atria (n=11) by subsurface near-infrared optical mapping (NIOM; 0.3 mm
resultsTargeted driver ablation and NIOM activation maps efficiently defined the center and periphery of AF driver preferential tracks and provided validated annotations for driver versus nondriver electrodes in MEM arrays. Compared with analysis of single electrogram frequency features, averaging the features from each of the 8 neighboring electrodes, significantly improved classification of AF driver electrograms. The classification metrics increased when less strict annotation, including driver periphery electrodes, were added to driver center annotation. Notably, f1-score for the binary classification of higher-density catheter data set was significantly higher than that of lower-density catheter (0.81±0.02 versus 0.66±0.04,
conclusionsThe machine learning model pretrained on Fourier spectrum features allows efficient classification of electrograms recordings as AF driver or nondriver compared with the NIOM gold-standard. Future application of NIOM-validated machine learning approach may improve the accuracy of AF driver detection for targeted ablation treatment in patients.
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