Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Background: Atrial 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...

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Bibliographic Details
Main Authors: Giorgio Luongo, MSc, Luca Azzolin, MSc, Steffen Schuler, MSc, Massimo W. Rivolta, PhD, Tiago P. Almeida, PhD, Juan P. Martínez, PhD, Diogo C. Soriano, PhD, Armin Luik, MD, Björn Müller-Edenborn, MD, Amir Jadidi, MD, Olaf Dössel, PhD, Roberto Sassi, PhD, Pablo Laguna, PhD, Axel Loewe, PhD
Format: Article
Language:English
Published: Elsevier 2021-04-01
Series:Cardiovascular Digital Health Journal
Subjects:
Atrial fibrillation
Atrial ablation
Machine learning
Noninvasive
12-lead electrocardiogram
Pulmonary vein isolation
Online Access:http://www.sciencedirect.com/science/article/pii/S2666693621000244

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http://www.sciencedirect.com/science/article/pii/S2666693621000244

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