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Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

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Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

dc.contributor.author	Tang, Siyi
dc.contributor.author	Razeghi, Orod
dc.contributor.author	Kapoor, Ridhima
dc.contributor.author	Alhusseini, Mahmood I.
dc.contributor.author	Fazal, Muhammad
dc.contributor.author	Rogers, A. J.
dc.contributor.author	Rodrigo Bort, Miguel
dc.contributor.author	Clopton, Paul
dc.contributor.author	Wang Jiyou, Paul
dc.contributor.author	Rubin, Daniel L.
dc.date.accessioned	2023-05-22T13:47:39Z
dc.date.available	2023-05-22T13:47:39Z
dc.date.issued	2022
dc.identifier.citation	Tang, Siyi Razeghi, Orod Kapoor, Ridhima Alhusseini, Mahmood I. Fazal, Muhammad Rogers, A. J. Rodrigo Bort, Miguel Clopton, Paul Wang Jiyou, Paul Rubin, Daniel L. 2022 Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes Circulation-Arrhythmia And Electrophysiology 15 8 500 509
dc.identifier.uri	https://hdl.handle.net/10550/86824
dc.description.abstract	Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes.
dc.language.iso	eng
dc.relation.ispartof	Circulation-Arrhythmia And Electrophysiology, 2022, vol. 15, num. 8, p. 500-509
dc.subject	Enginyeria elèctrica
dc.subject	Urgències cardiovasculars
dc.title	Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
dc.type	journal article
dc.date.updated	2023-05-22T13:47:40Z
dc.identifier.doi	10.1161/CIRCEP.122.010850
dc.identifier.idgrec	158804
dc.rights.accessRights	open access