109482 20230323131628.0 doi 10.1016/j.cvdhj.2021.03.002 sideral 125675 ART-2021-125675 eng Luongo, Giorgio Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG 2021 Access copy available to the general public Unrestricted 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 patients with additional extra-PV arrhythmogenic drivers. Objectives: To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods: AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources). Results: The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class. Conclusion: Machine learning–based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI. info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIA info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00 info:eu-repo/grantAgreement/ES/MICIU/PID2019-105674RB-I00 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Azzolin, Luca Schuler, Steffen Rivolta, Massimo W. Almeida, Tiago P. Martínez, Juan P. Universidad de Zaragoza (orcid)0000-0002-7503-3339 Soriano, Diogo C. Luik, Armin Müller-Edenborn, Björn Jadidi, Amir Dössel, Olaf Sassi, Roberto Laguna, Pablo Universidad de Zaragoza (orcid)0000-0003-3434-9254 Loewe, Axel 5008 800 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Teoría Señal y Comunicac. 2, 2 (2021), 126-136 Cardiovasc. digit. health j. Cardiovascular digital health journal 2666-6936 1213152 http://zaguan.unizar.es/record/109482/files/texto_completo.pdf Versión publicada 2723355 http://zaguan.unizar.es/record/109482/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:109482 articulos driver 2023-03-23-12:59:34 ARTICLE