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Circuits, Systems, and Signal Processing

, Volume 35, Issue 4, pp 1187–1197 | Cite as

Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

  • Hongqiang LiEmail author
  • Huan Liang
  • Chunjiao Miao
  • Lu Cao
  • Xiuli Feng
  • Chunxiao Tang
  • Enbang Li
Article

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 %.

Keywords

ECG signal Feature extraction Principal component analysis  Kernel independent component analysis Classification Support vector machine 

Notes

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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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Hongqiang Li
    • 1
    Email author
  • Huan Liang
    • 1
  • Chunjiao Miao
    • 2
  • Lu Cao
    • 3
  • Xiuli Feng
    • 1
  • Chunxiao Tang
    • 1
  • Enbang Li
    • 4
  1. 1.School of Electronics and Information EngineeringTianjin Polytechnic UniversityTianjinChina
  2. 2.School of Electronics and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  3. 3.Tianjin Chest HospitalTianjinChina
  4. 4.School of Physics, Faculty of Engineering and Information SciencesUniversity of WollongongWollongongAustralia

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