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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
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| 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. | |||
| This document is a artículoDate2022 | |||
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| 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. | |||
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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 |
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| https://doi.org/10.1161/CIRCEP.122.010850 |