99467 20230914083308.0 doi 10.22489/CinC.2020.181 sideral 123202 ART-2020-123202 eng Luongo, Giorgio Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG 2020 Access copy available to the general public Unrestricted Atrial 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. 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/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 0.257 2020 Computer Science (miscellaneous) 2020 Cardiology and Cardiovascular Medicine 2020 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Azzolin, Luca Rivolta, Massimo Walter Paggi de Almeida, Tiago Martínez, Juan Pablo Universidad de Zaragoza (orcid)0000-0002-7503-3339 Coutinho Soriano, Diogo Doessel, Olaf Sassi, Roberto Laguna Lasaosa, 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. 47 (2020), [4 pp.] Comput. cardiol. Computing in Cardiology 2325-8861 2230814 http://zaguan.unizar.es/record/99467/files/texto_completo.pdf Versión publicada 2575049 http://zaguan.unizar.es/record/99467/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:99467 articulos driver 2023-09-13-10:56:35 ARTICLE