Enhancing Origin Prediction: Deep Learning Model for Diagnosing Premature Ventricular Contractions with Dual-Rhythm Analysis Focused on Cardiac Rotation

Background Several algorithms can differentiate inferior axis premature ventricular contractions (PVCs) originating from the right side and left side on 12-lead electrocardiograms (ECGs). However, it is unclear whether distinguishing the origin should rely solely on PVC or incorporate sinus rhythm (SR). Aims We compared the Dual-Rhythm model (incorporating both SR and PVC) to the PVC model (using PVC alone), and quantified the contribution of each ECG lead in predicting the PVC origin for each cardiac rotation. Methods This multicenter study enrolled 593 patients from 11 centers—493 from Japan and Germany, and 100 from Belgium, which used as the external validation dataset. Using a hybrid approach combining a Resnet50-based convolutional neural network and a Transformer model, we developed two variants—the PVC and Dual-Rhythm models—to predict PVC origin. Results In the external validation dataset, the Dual-Rhythm model outperformed the PVC model in accuracy (0.84 vs. 0.74, respectively; p < 0.01), precision (0.73 vs. 0.55, respectively; p < 0.01), specificity (0.87 vs. 0.68, respectively; p < 0.01), area under the receiver operating characteristic curve (0.91 vs. 0.86, respectively; p = 0.03), and F1-Score (0.77 vs. 0.68, respectively; p = 0.03). The contributions to PVC origin prediction were 77.3% for PVC and 22.7% for the SR. However, in patients with counterclockwise rotation, SR had a greater contribution in predicting the origin of right-sided PVC. Conclusions Our deep learning-based model, incorporating both PVC and SR morphologies, resulted in a higher prediction accuracy for PVC origin. Considering SR is particularly important for predicting right-sided origin in patients with counterclockwise rotation.