Machine learning enables non-invasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

Abstract 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 non-invasive 12-lead ECG in a computational and clin ical framework, and to computationally predict the acute success of PVI in these cohorts of data. Methods AF drivers were induced in two computerized atrial models and combined with 8 torso models, resulting in 1,128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). 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 dis- crimination between patients with PVs drivers versus those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.