期刊
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
卷 27, 期 6, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218126618500883
关键词
Compressive sensing; cardiac arrhythmia; direct classification; semi-supervised learning
资金
- Science and Technology Research Project of Jiangxi Province [GJJ150424, 20161BBE50090, 20171BAB201013]
- National Natural Science Foundation of China [61273282]
- College Science and Technology Ground Plan Project of Jiangxi Province [KJLD13002]
- Key Research and Development Plan of Jiangxi Province [20161BBE50090]
Due to the capacity of processing signal with low energy consumption, compressive sensing (CS) has been widely used in wearable health monitoring system for arrhythmia classification of electrocardiogram (ECG) signals. However, most existing works focus on compressive sensing reconstruction, in other words, the ECG signals must be reconstructed before use. Hence, these methods have high computational complexity. In this paper, the authors propose a cardiac arrhythmia classification scheme that performs classification task directly in the compressed domain, skipping the reconstruction stage. The proposed scheme first employs the Pan-Tompkins algorithm to preprocess the ECG signals, including denoising and QRS detection, and then compresses the ECG signals by CS to obtain the compressive measurements. The features are extracted directly from these measurements based on principal component analysis (PCA), and are used to classify the ECG signals into different types by the proposed semi-supervised learning algorithm based on support vector machine (SVM). Extensive simulations have been performed to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of 98.0531% at a sensing rate of 0.7, compared to an accuracy of 98.5841% for noncompressive ECG data.
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