ArticleRoyal Society open science2018
Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- [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
- Canine model of electrical conduction recurrence after radiofrequency catheter ablation constructed by CARTO3 and preliminary application evaluation of DOX-L.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2023Article
- Machine learning in the detection and management of atrial fibrillation.Clinical research in cardiology : official journal of the German Cardiac Society · 2022Review
- Artificial intelligence in the diagnosis and management of arrhythmias.European heart journal · 2021Review
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- Machine Learning in Arrhythmia and Electrophysiology.Circulation research · 2021Review
- Comprehensive evaluation of electrophysiological and 3D structural features of human atrial myocardium with insights on atrial fibrillation maintenance mechanisms.Journal of molecular and cellular cardiology · 2021Review
- Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a HybridFrontiers in physiology · 2021Article
- Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping.Circulation. Arrhythmia and electrophysiology · 2020Article
- Applications of Machine Learning in Cardiac Electrophysiology.Arrhythmia & electrophysiology review · 2020Review
- Spectral Analysis and Mutual Information Estimation of Left and Right Intracardiac Electrograms during Ventricular Fibrillation.Sensors (Basel, Switzerland) · 2020Article
- Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience.Computing in cardiology · 2020Article
- Unified mechanism of local drivers in a percolation model of atrial fibrillation.Physical review. E · 2019Article
- Identifying Potential Re-Entrant Circuit Locations From Atrial Fibre Maps.Computing in cardiology · 2019Article
- Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation.Computer methods and programs in biomedicine · 2019Review
- Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.Computers in biology and medicine · 2019Review
- Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.Royal Society open science · 2018Article
- Understanding the transition from paroxysmal to persistent atrial fibrillation.Physical review researchArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.
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