  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.
