FASE: Feature-Based Similarity Search on ECG Data

Abstract

Electrocardiogram (ECG) data is commonly used in clinic to reveal instant status of cardiac electrophysiology, and is related to numerous heart diseases. Efficient similarity search on ECG data can assist diagnosis. However, similarity search on ECG data is different from similarity search on images in that ECG data is a kind of physiological wave data, and that there are no established robust feature extraction methods for these physiological wave data. Thus, we adopt a supervised framework to preserve locality based on label information, while extracting effective features automatically. Experiments on real-life data show the effectiveness and efficiency of the proposed approach FASE.