Abstract
Electrocardiography (ECG) signal classification is a challenging task since the characteristics of ECG signals vary significantly for different patients. In this paper, we propose a new method for ECG signal classification based on the separation of common and specific components of a signal. The common components are obtained via Canonical Correlation Analysis (CCA). After removing the common components from the signal, we map the specific components to a lower dimensional feature space for classification. We first establish a basic model in the binary classification setting and then extend it to a more general version. Numerical experiments results on the MIT-BIH Arrhythmia Database are presented and discussed.
This work is supported by National Natural Science Foundation of China (Nos. 11601532, 11501377, 11431015, 11601346) and Interdisciplinary Innovation Team of Shenzhen University.
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Huang, J., Huang, C., Yang, L., Zhang, Q. (2020). A Novel ECG Signal Classification Algorithm Based on Common and Specific Components Separation. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_51
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DOI: https://doi.org/10.1007/978-3-030-59830-3_51
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