Cite
Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
MLA
Siyi Tang, et al. “Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.” Circulation: Arrhythmia and Electrophysiology, vol. 15, Aug. 2022. EBSCOhost, https://doi.org/10.1161/circep.122.010850.
APA
Siyi Tang, Orod Razeghi, Ridhima Kapoor, Mahmood I. Alhusseini, Muhammad Fazal, Albert J. Rogers, Miguel Rodrigo Bort, Paul Clopton, Paul J. Wang, Daniel L. Rubin, Sanjiv M. Narayan, & Tina Baykaner. (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. https://doi.org/10.1161/circep.122.010850
Chicago
Siyi Tang, Orod Razeghi, Ridhima Kapoor, Mahmood I. Alhusseini, Muhammad Fazal, Albert J. Rogers, Miguel Rodrigo Bort, et al. 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 (August). doi:10.1161/circep.122.010850.