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.

翻译：引言：室性早搏（PVCs）是起源于心室的常见心律失常。由于导联放置位置、记录条件和人群特征的差异导致心电图（ECG）波形存在变异性，其精确检测仍然具有挑战性。方法：我们开发了uPVC-Net，一种通用的深度学习模型，用于从任何单导联心电图记录中检测室性早搏。该模型基于四个独立的心电图数据集开发，这些数据集共包含830万个心搏，采集自动态心电图监护仪和现代可穿戴心电图贴片。uPVC-Net采用定制架构和多源、多导联训练策略。在每次实验中，一个数据集被留出用于评估分布外（OOD）泛化能力。结果：在留出的数据集上，uPVC-Net取得了97.8%至99.1%的AUC。值得注意的是，在可穿戴单导联心电图数据上的性能达到了99.1%的AUC。结论：uPVC-Net在不同的导联配置和人群中表现出强大的泛化能力，突显了其在现实临床环境中稳健部署的潜力。