Electrocardiography plays an essential role in diagnosing and screening cardiovascular diseases in daily healthcare. Deep neural networks have shown the potentials to improve the accuracies of arrhythmia detection based on electrocardiograms (ECGs). However, more ECG records with ground truth are needed to promote the development and progression of deep learning techniques in automatic ECG analysis. Here we propose a web-based tool for ECG viewing and annotating, LabelECG. With the facilitation of unified data management, LabelECG is able to distribute large cohorts of ECGs to dozens of technicians and physicians, who can simultaneously make annotations through web-browsers on PCs, tablets and cell phones. Along with the doctors from four hospitals in China, we applied LabelECG to support the annotations of about 15,000 12-lead resting ECG records in three months. These annotated ECGs have successfully supported the First China ECG intelligent Competition. La-belECG will be freely accessible on the Internet to support similar researches, and will also be upgraded through future works.
@article{arxiv.1908.06553,
title = {LabelECG: A Web-based Tool for Distributed Electrocardiogram Annotation},
author = {Zijian Ding and Shan Qiu and Yutong Guo and Jianping Lin and Li Sun and Dapeng Fu and Zhen Yang and Chengquan Li and Yang Yu and Long Meng and Tingting Lv and Dan Li and Ping Zhang},
journal= {arXiv preprint arXiv:1908.06553},
year = {2019}
}