Non invasive prediction of atrial fibrillation driver location

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

IBT researcher Giorgio Luongo presented a machine learning-based classification of 12-lead ECG to discriminate between patients with pulmonary vein drivers vs. those with extra-pulmonary vein drivers of atrial fibrillation. This novel algorithm may aid to identify patients with high acute success rates to pulmonary vein isolation. This work is the result of a successful collaboration between 5 engineering and 2 clinical groups (Università degli Studi di Milano, University of Leicester, Universidad de Zaragoza, ABC Federal University, Städtisches Klinikum Karlsruhe, University-Heart-Center Freiburg-Bad Krozingen and IBT).
The paper is published in Cardiovascular Digital Health Journal and is freely available.