ECG Classification Using ICA Features and Support Vector Machines

  • Yang Wu
  • Liqing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7062)


Classification accuracy is vital in practical application of automatic ECG diagnostics. This paper aims at enhancing accuracy of ECG signals classification. We propose a statistical method to segment heartbeats from ECG signal as precisely as possible, and use the combination of independent component analysis (ICA) features and temporal feature to describe multi-lead ECG signals. To obtain the most discriminant features of different class, we introduce the minimal-redundancy-maximal-relevance feature selection method. Finally, we designed a vote strategy to make the decision from different classifiers. We test our method on the MIT-BIT Arrhythmia Database, achieving a high accuracy.


ECG segmentation ICA feature extraction SVM 


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  1. 1.
    Tan, K.F., Chan, K.L., Choi, K.: Detection of the QRS complex, P wave and T wave in electrocardiogram. In: First International Conference on Advances in Medical Signal and Information Processing, IEEE Conf. Publ. No. 476, pp. 41–47 (2000)Google Scholar
  2. 2.
    Pal, S., Mitra, M.: Detection of ECG characteristic points using Multiresolution Wavelet Analysis based Selective Coefficient Method. Measurement 43(2), 255–261 (2010)CrossRefGoogle Scholar
  3. 3.
    Zhao, Q.B., Zhang, L.Q.: ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. In: International Conference on Neural Networks and Brain, pp. 1089–1092 (2005)Google Scholar
  4. 4.
    Pasolli, E., Melgani, F.: Active learning methods for electrocardiographic signal classification. IEEE Transactions on Information Technology in Biomedicine 14(6), 1405–1416 (2010)CrossRefGoogle Scholar
  5. 5.
    Gacek, A.: Preprocessing and analysis of ECG signals - A self-organizing maps approach. Expert Systems with Applications 38(7), 9008–9013 (2011)CrossRefGoogle Scholar
  6. 6.
    Karpagachelvi, S., Arthanari, M., Sivakumar, M.: ECG Feature Extraction Techniques - A Survey Approach. International Journal of Computer Science and Information Security 8(1), 76–80 (2010)Google Scholar
  7. 7.
    Soria, L.M., Martínez, J.P.: Analysis of Multidomain Features for ECG Classification. Computers in Cardiology, 561–564 (2009)Google Scholar
  8. 8.
    Zhang, L.Q., Cichocki, A., Amari, S.I.: Self-adaptive blind source separation based on activation functions adaptation. IEEE Trans. Neural Networks 15(2), 233–244 (2004)CrossRefGoogle Scholar
  9. 9.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Inter-science (2001)Google Scholar
  10. 10.
    Hyvärinen, A., Oja, E.: A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Computation 9, 1483–1492 (1997)CrossRefGoogle Scholar
  11. 11.
    Dash, M., Liu, H.: Feature Selection for Classification. In: Intelligent Data Analysis, vol. 1, pp. 131–156 (1997)Google Scholar
  12. 12.
    Peng, H., Long, F., Ding, C.: Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(8) (2005)Google Scholar
  13. 13.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, Heidelberg (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kressel, U.H.-G.: Pairwise classification and support vector machines. In: Advances in Kernel Methods, pp. 255–268. MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Cheong, S., Oh, S.H., Lee, S.Y.: Support Vector Machines with Binary Tree Architecture for Multi-Class Classification. Neural Information Processing 2(3), 47–51 (2004)Google Scholar
  16. 16.
    Hsu, C., Chang, C., Lin, C.-J.: A practical guide to support vector classification, Technical report, Department of Computer Science. National Taiwan University (2003)Google Scholar
  17. 17.
    Wu, T.F., Lin, C.J., Weng, R.C.: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research 5, 975–1005 (2004)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Mark, R., Moody, G.: MIT-BIH Arrhythmia Database,
  19. 19.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215–e220 (2000)Google Scholar
  20. 20.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar
  21. 21.
    Jiang, X., Zhang, L.Q., Zhao, Q.B., Albayrak, S.: ECG Arrhythmias Recognition System Based on Independent Component Analysis Feature Extraction. In: Conference Proceedings of IEEE Region 10 Conference (TENCON), pp. 464–471 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yang Wu
    • 1
  • Liqing Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringShanghai Jiaotong UniversityShanghaiChina

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