Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG
Luongo, Giorgio ; Azzolin, Luca ; Schuler, Steffen ; Rivolta, Massimo W. ; Almeida, Tiago P. ; Martínez, Juan P. (Universidad de Zaragoza) ; Soriano, Diogo C. ; Luik, Armin ; Müller-Edenborn, Björn ; Jadidi, Amir ; Dössel, Olaf ; Sassi, Roberto ; Laguna, Pablo (Universidad de Zaragoza) ; Loewe, Axel
Resumen: Background: 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.
Idioma: Inglés
DOI: 10.1016/j.cvdhj.2021.03.002
Año: 2021
Publicado en: Cardiovascular digital health journal 2, 2 (2021), 126-136
ISSN: 2666-6936
Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
Financiación: info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PID2019-105674RB-I00
Tipo y forma: Article (Published version)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.
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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.
Idioma: Inglés
DOI: 10.1016/j.cvdhj.2021.03.002
Año: 2021
Publicado en: Cardiovascular digital health journal 2, 2 (2021), 126-136
ISSN: 2666-6936
Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-17R-BSICoS
Financiación: info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2019-104881RB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PID2019-105674RB-I00
Tipo y forma: Article (Published version)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. Exportado de SIDERAL (2023-03-23-12:59:34)
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Record created 2022-01-15, last modified 2023-03-23