ArticleCardiovascular digital health journal2021
Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG.
Article in Cardiovascular digital health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Review
- Defining myocardial fiber bundle architecture in atrial digital twins.Computers in biology and medicine · 2025Article
- Complexity and recurrence of body surface electrocardiograms correlate with estimated reentrant atrial activity using electrocardiographic imaging in atrial fibrillation patients.BMC cardiovascular disorders · 2025Article
- Evolution in electrophysiology 100 years after Einthoven: translational and computational innovations in rhythm control of atrial fibrillation.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
- Article
- Postoperative Atrial Fibrillation: A Review.Biomedicines · 2024Review
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.Scientific reports · 2023Article
- MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.Scientific data · 2023Article
- Differences in atrial substrate localization using late gadolinium enhancement-magnetic resonance imaging, electrogram voltage, and conduction velocity: a cohort study using a consistent anatomical reference frame in patients with persistent atrial fibrillation.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 · 2023Article
- The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.Reviews in cardiovascular medicine · 2023Review
- A systematic review of cardiacProgress in biomedical engineering (Bristol, England) · 2023Review
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Exploring interpretability in deep learning prediction of successful ablation therapy for atrial fibrillation.Frontiers in physiology · 2023Article
- Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram.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 · 2022Article
- Detection of focal source and arrhythmogenic substrate from body surface potentials to guide atrial fibrillation ablation.PLoS computational biology · 2022Article
- Artificial intelligence for the detection, prediction, and management of atrial fibrillation.Herzschrittmachertherapie & Elektrophysiologie · 2022Review
- Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.Circulation. Arrhythmia and electrophysiology · 2022Article
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
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Authors and funding
14 authors at 7 institutions in 5 countries.
Funding
Abstract
backgroundAtrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers.
objectivesTo discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data.
methodsAF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources).
resultsThe classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class.
conclusionMachine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.
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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.