# uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm

arXiv: https://arxiv.org/abs/2506.11238 · HTML: https://mlsanity.com/paper/2506.11238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [TorchAudio: Building Blocks for Audio and Speech Processing](https://mlsanity.com/paper/2110.15018/) (2021)
- [An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains](https://mlsanity.com/paper/2410.04133/) (2024)
- [Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks](https://mlsanity.com/paper/2303.11338/) (2023)
- [Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery](https://mlsanity.com/paper/1910.09570/) (2019)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [Generalization in medical AI: a perspective on developing scalable models](https://mlsanity.com/paper/2311.05418/) (2023)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [GONet: A Generalizable Deep Learning Model for Glaucoma Detection](https://mlsanity.com/paper/2502.19514/) (2025)

## Abstract (arXiv)

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.