doi:10.22489/CinC.2020.181engLuongo, GiorgioAzzolin, LucaRivolta, Massimo WalterPaggi de Almeida, TiagoMartínez, Juan PabloCoutinho Soriano, DiogoDoessel, OlafSassi, RobertoLaguna Lasaosa, PabloLoewe, AxelMachine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECGART-2020-123202Atrial fibrillation (AF) is the most frequent irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. The non-invasive localization of AF drivers can lead to improved personalized ablation strategy, suggesting pulmonary vein (PV) isolation or more complex extra-PV ablation procedures in case the driver is on other atrial regions. We used a Machine Learning approach to characterize and discriminate simulated single stable rotors (1R) location: PVs, left atrium (LA) excluding the PVs, and right atrium (RA), utilizing solely non-invasive signals (i.e., the 12-lead ECG). 1R episodes sustaining AF were simulated. 128 features were extracted from the signals. Greedy forward algorithm was implemented to select the best feature set which was fed to a decision tree classifier with hold-out cross-validation technique. All tested features showed significant discriminatory power, especially those based on recurrence quantification analysis (up to 80.9% accuracy with single feature classification). The decision tree classifier achieved 89.4% test accuracy with 18 features on simulated data, with sensitivities of 93.0%, 82.4%, and 83.3% for RA, LA, and PV classes, respectively. Our results show that a machine learning approach can potentially identify the location of 1R sustaining AF using the 12-lead ECG.2020http://zaguan.unizar.es/record/9946710.22489/CinC.2020.181http://zaguan.unizar.es/record/99467oai:zaguan.unizar.es:99467info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIAThis project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIAComputing in Cardiology 47 (2020), [4 pp.]byhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccess