000099467 001__ 99467
000099467 005__ 20230914083308.0
000099467 0247_ $$2doi$$a10.22489/CinC.2020.181
000099467 0248_ $$2sideral$$a123202
000099467 037__ $$aART-2020-123202
000099467 041__ $$aeng
000099467 100__ $$aLuongo, Giorgio
000099467 245__ $$aMachine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG
000099467 260__ $$c2020
000099467 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099467 5203_ $$aAtrial 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.
000099467 536__ $$9info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIA
000099467 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099467 592__ $$a0.257$$b2020
000099467 593__ $$aComputer Science (miscellaneous)$$c2020
000099467 593__ $$aCardiology and Cardiovascular Medicine$$c2020
000099467 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099467 700__ $$aAzzolin, Luca
000099467 700__ $$aRivolta, Massimo Walter
000099467 700__ $$aPaggi de Almeida, Tiago
000099467 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, Juan Pablo$$uUniversidad de Zaragoza
000099467 700__ $$aCoutinho Soriano, Diogo
000099467 700__ $$aDoessel, Olaf
000099467 700__ $$aSassi, Roberto
000099467 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna Lasaosa, Pablo$$uUniversidad de Zaragoza
000099467 700__ $$aLoewe, Axel
000099467 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000099467 773__ $$g47 (2020), [4 pp.]$$pComput. cardiol.$$tComputing in Cardiology$$x2325-8861
000099467 8564_ $$s2230814$$uhttps://zaguan.unizar.es/record/99467/files/texto_completo.pdf$$yVersión publicada
000099467 8564_ $$s2575049$$uhttps://zaguan.unizar.es/record/99467/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099467 909CO $$ooai:zaguan.unizar.es:99467$$particulos$$pdriver
000099467 951__ $$a2023-09-13-10:56:35
000099467 980__ $$aARTICLE