uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
This addresses the challenge of accurate PVC detection for clinical applications, but it is incremental as it builds on existing deep learning methods with a custom architecture and training strategy.
The paper tackled the problem of detecting Premature Ventricular Contractions (PVCs) from single-lead ECG recordings with variability in waveforms, and the result was that uPVC-Net achieved AUCs between 97.8% and 99.1% on held-out datasets, including 99.1% on wearable data.
Introduction: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains challenging due to variability in electrocardiogram (ECG) waveforms caused by differences in lead placement, recording conditions, and population demographics. Methods: We developed uPVC-Net, a universal deep learning model to detect PVCs from any single-lead ECG recordings. The model is developed on four independent ECG datasets comprising a total of 8.3 million beats collected from Holter monitors and a modern wearable ECG patch. uPVC-Net employs a custom architecture and a multi-source, multi-lead training strategy. For each experiment, one dataset is held out to evaluate out-of-distribution (OOD) generalization. Results: uPVC-Net achieved an AUC between 97.8% and 99.1% on the held-out datasets. Notably, performance on wearable single-lead ECG data reached an AUC of 99.1%. Conclusion: uPVC-Net exhibits strong generalization across diverse lead configurations and populations, highlighting its potential for robust, real-world clinical deployment.