| Página principal > Artículos > Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG > MARC |
000109482 001__ 109482 000109482 005__ 20230323131628.0 000109482 0247_ $$2doi$$a10.1016/j.cvdhj.2021.03.002 000109482 0248_ $$2sideral$$a125675 000109482 037__ $$aART-2021-125675 000109482 041__ $$aeng 000109482 100__ $$aLuongo, Giorgio 000109482 245__ $$aMachine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG 000109482 260__ $$c2021 000109482 5060_ $$aAccess copy available to the general public$$fUnrestricted 000109482 5203_ $$aBackground: 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. 000109482 536__ $$9info:eu-repo/grantAgreement/ES/MICIU/PID2019-105674RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIA$$9info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS 000109482 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000109482 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000109482 700__ $$aAzzolin, Luca 000109482 700__ $$aSchuler, Steffen 000109482 700__ $$aRivolta, Massimo W. 000109482 700__ $$aAlmeida, Tiago P. 000109482 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, Juan P.$$uUniversidad de Zaragoza 000109482 700__ $$aSoriano, Diogo C. 000109482 700__ $$aLuik, Armin 000109482 700__ $$aMüller-Edenborn, Björn 000109482 700__ $$aJadidi, Amir 000109482 700__ $$aDössel, Olaf 000109482 700__ $$aSassi, Roberto 000109482 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza 000109482 700__ $$aLoewe, Axel 000109482 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac. 000109482 773__ $$g2, 2 (2021), 126-136$$pCardiovasc. digit. health j.$$tCardiovascular digital health journal$$x2666-6936 000109482 8564_ $$s1213152$$uhttps://zaguan.unizar.es/record/109482/files/texto_completo.pdf$$yVersión publicada 000109482 8564_ $$s2723355$$uhttps://zaguan.unizar.es/record/109482/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000109482 909CO $$ooai:zaguan.unizar.es:109482$$particulos$$pdriver 000109482 951__ $$a2023-03-23-12:59:34 000109482 980__ $$aARTICLE
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