Machine Learning‐Based Classification of Right and Left Ventricular Outflow Tract Premature Ventricular Contractions

INTRODUCTION: Idiopathic premature ventricular contractions (PVCs) commonly originate from the right and left ventricular outflow tracts (RVOT and LVOT). The surface 12-lead electrocardiogram (ECG) is commonly used to differentiate the anatomic site of origin prior to catheter ablation. Machine learning methods can further enhance diagnostic accuracy. MATERIALS AND METHODS: In this study, 64 ECG-derived features, along with age and gender, were analyzed from 43 patients (65% males, median age 55.6, IQR 30 years), who underwent successful catheter ablation of outflow tract-originating idiopathic PVCs. The site of origin was depicted through 3-D electroanatomical mapping. A logistic regression model was trained to classify the site of origin as either RVOT or LVOT using repeated stratified five-fold cross-validation with five repeats. A model restricted to ECG features and four previously published ECG algorithms was evaluated for comparison. RESULTS: Across the 25 held-out partitions, the model achieved an accuracy of 0.817 ± 0.143, precision of 0.888 ± 0.122, recall of 0.824 ± 0.206, F1-score of 0.836 ± 0.139 and ROC-AUC of 0.917 ± 0.094. Using the pooled out-of-fold predictions, accuracy was 0.860 (95% CI 0.744-0.953) and ROC-AUC 0.910 (95% CI 0.800-0.987). Age was the most discriminative feature, with increasing values favoring LVOT origin. The ECG-only model achieved an ROC-AUC of 0.853 (95% CI 0.721-0.958; ΔAUC +0.057, p = 0.104). CONCLUSIONS: These findings suggest that machine learning models, based on ECG parameters and demographic characteristics, can classify outflow tract ventricular arrhythmias, supporting their use in automated differentiation and pre-procedural planning.