FASE: Feature-Based Similarity Search on ECG Data

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...

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Bibliographic Details
Published in2019 IEEE International Conference on Big Knowledge (ICBK) pp. 273 - 280
Main Authors Wu, Meng, Li, Lei, Li, Hongyan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
Binary codes
Diseases
ECG
Electrocardiography
Laplace equations
Machine learning
Morphology
Similarity Search
Training
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Summary: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.
DOI:10.1109/ICBK.2019.00044