LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation
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. LabelECG will be freely accessible on the Internet to support similar researches, and will also be upgraded through future works.
KeywordsCardiovascular disease Electrocardiograms Distributed annotation
This work is supported by The National Key Research and Development Program of China (2017YFB1401804) and The Medicine-Engineering Innovation Support Program of Tsinghua University (IDS-MSP-2019003).
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