Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG

Luongo, Giorgio ; Azzolin, Luca ; Rivolta, Massimo Walter ; Paggi de Almeida, Tiago ; Martínez, Juan Pablo (Universidad de Zaragoza) ; Coutinho Soriano, Diogo ; Doessel, Olaf ; Sassi, Roberto ; Laguna Lasaosa, Pablo (Universidad de Zaragoza) ; Loewe, Axel

Resumen: 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.

Idioma: Inglés

DOI: 10.22489/CinC.2020.181

Año: 2020

Publicado en: Computing in Cardiology 47 (2020), [4 pp.]
ISSN: 2325-8861

Factor impacto SCIMAGO: 0.257 - Computer Science (miscellaneous) - Cardiology and Cardiovascular Medicine

Financiación: info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)