uPVC-Net可精准识别单导联心电图中的室性早搏，适应性强。
uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
- 基于多源数据训练的深度学习模型，支持任意单导联输入
- 在独立测试集上AUC达97.8%~99.1%，可穿戴设备数据达99.1%
- 适合临床部署，对不同人群和设备均有强泛化能力
室性早搏（PVC）是常见的心律失常，其检测因导联位置、记录条件及人群差异导致心电图波形变化而困难。本文提出uPVC-Net，一种通用深度学习模型，可从任意单导联心电图中检测PVC。模型基于四个独立心电图数据集，共包含830万次心跳，数据来自霍尔特监测仪和现代可穿戴心电贴片。uPVC-Net采用自定义架构与多源、多导联训练策略，每个实验均保留一个数据集用于评估分布外（OOD）泛化能力。结果表明，uPVC-Net在保留数据集上AUC为97.8%至99.1%，尤其在可穿戴单导联心电数据上达到99.1%。结论显示，uPVC-Net在不同导联配置和人群间具备强大泛化能力，具有良好的真实世界临床应用潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。