ArticleFrontiers in physiology2021
Deep Learning Classification of Unipolar Electrograms in Human Atrial Fibrillation: Application in Focal Source Mapping.
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 16 papers.
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Who cites it
16 citing papers in PubMed, 24 citations in OpenAlex.
- Convolutional neural network for real‑time localization of ganglionated plexi from bipolar intracardiac electrograms.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2026Article
- Automatic Localization of Stable Highest Dominant Frequency Area in AF Patients Based on Spatial Aggregation.Bioengineering (Basel, Switzerland) · 2026Article
- Machine Learning and Arrhythmia: Advances in Atrial Fibrillation Detection and Management.Current atherosclerosis reports · 2025Review
- 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
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.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 · 2025Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Analyzing the performance of biomedical time-series segmentation with electrophysiology data.Scientific reports · 2025Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- 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
- [Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation.Arrhythmia & electrophysiology review · 2023Review
- Machine learning in the detection and management of atrial fibrillation.Clinical research in cardiology : official journal of the German Cardiac Society · 2022Review
- Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation.International journal of molecular sciences · 2022Article
- AI in the EP lab - mapping, imaging, and signal interpretation.Indian pacing and electrophysiology journalReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (
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Registered trials
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.