ECGLens, an interactive system featuring a novel A-glyph design, improved the efficiency of arrhythmia detection in a user study and expert interviews.
ECGLens, an interactive system utilizing CNNs and novel visual glyphs, improves the efficiency of detecting arrhythmias in large-scale ECG data.
The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.
Xu et al. (Fri,) conducted a other in Arrhythmia. ECGLens and A-glyph was evaluated on Efficiency in arrhythmia detection. ECGLens, an interactive system featuring a novel A-glyph design, improved the efficiency of arrhythmia detection in a user study and expert interviews.