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Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

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Abstract

Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular diseases. This paper presents a new method for nonlinear feature extraction of ECG signals by combining principal component analysis (PCA) and kernel independent component analysis (KICA). The proposed method first uses PCA to decrease the dimensions of the ECG signal training set and then employs KICA to calculate the feature space for extracting the nonlinear features. Support vector machine (SVM) is utilized to determine the nonlinear features of the ECG signal classification. Genetic algorithm is also used to optimize the SVM parameters. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. Computer simulations reveal that the proposed method yields more satisfactory classification results on the MIT–BIH arrhythmia database, reaching an overall accuracy of 97.78 %.

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Acknowledgments

This paper was supported by the National Natural Science Foundation of China (Nos. 61177078, 61307094, 31271871), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20101201120001), and Tianjin Research Program of Application Foundation and Advanced Technology (No. 13JCYBJC16800).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tianjin Polytechnic University, No. 399 Binshuixi Road, Xiqing District, Tianjin, 300387, China

    Hongqiang Li, Huan Liang, Xiuli Feng & Chunxiao Tang

  2. School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Chunjiao Miao

  3. Tianjin Chest Hospital, Tianjin, 300000, China

    Lu Cao

  4. School of Physics, Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, Australia

    Enbang Li

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  1. Hongqiang Li
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  2. Huan Liang
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  3. Chunjiao Miao
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  4. Lu Cao
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  5. Xiuli Feng
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  6. Chunxiao Tang
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  7. Enbang Li
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Correspondence to Hongqiang Li.

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Li, H., Liang, H., Miao, C. et al. Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction. Circuits Syst Signal Process 35, 1187–1197 (2016). https://doi.org/10.1007/s00034-015-0108-3

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  • DOI: https://doi.org/10.1007/s00034-015-0108-3

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