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ECG Classification Based on Non-cardiology Feature

  • Kai Huang
  • Liqing Zhang
  • Yang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

As for ECG auto-diagnosis, Classification accuracy is a vital factor for providing diagnosis decision support in remote ECG diagnosis. The final accuracy depends on ECG preprocessing process, feature extraction, feature selection and classification. However, different heart diseases are with different ECG wave shapes, in addition, there is large numbers of heart diseases, so it is hard to accurately extract cardiology features from diverse ECG wave forms. Also the extracted cardiology features are always with large error which to some extent influence the classification accuracy. To deal with these problems, we propose a feature extraction method of PCA and ICA approach. We calculate a adaptive basis with ICA and PCA for the given disease type ECG and extract the coefficients in the respect of trained basis which will be used as the classification features combined with cardiology features. To prevent the dimension disaster problem brought by the additional ICA and PCA feature, a minimal redundancy maximal relevance feature selection method is adapted to reduce the dimension of feature vector. Experiment shows that our method can effectively exclude the influence of not accurate cardiology features and greatly improve the classification accuracy for heart diseases.

Keywords

Non-Cardiology Feature PCA Feature Extraction ICA Feature Fxtraction Support Vector Machine 

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References

  1. 1.
    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
  2. 2.
    Soria, L.M., Martínez, J.P.: Analysis of Multidomain Features for ECG Classification. Computers in Cardiology, 561–564 (2009)Google Scholar
  3. 3.
    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, IEE Conf. Publ. No.476, pp. 41–47 (2000)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Gacek, A.: Preprocessing and analysis of ECG signals - A self-organizing maps approach. Expert Systems with Applications 38(7), 9008–9013 (2011)CrossRefGoogle Scholar
  7. 7.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Inter-science (2001)Google Scholar
  8. 8.
    Del Aguila, C.: Electromedicina. Hasa, Buenos Aires (1994)Google Scholar
  9. 9.
    Netter, F.: Coleccin de ilustraciones mdicas: Corazn. Salvat, Barcelona (1976)Google Scholar
  10. 10.
    Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3(2), 185–205 (2005)MathSciNetCrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Wu, Z., Huang, N.E.: A study of the characteristics of white noise using the empirical mode decomposition method. Proc. Roy. Soc. London A 460, 1597–1611 (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    Dotsinsky, I.A., Daskalov, I.K.: Accuracy of 50 Hz interference subtraction from an electrocardiogram. Med. & Bio. Eng. & Compu. 34, 489–494 (1996)CrossRefGoogle Scholar
  14. 14.
    Mark, R., Moody, G.: MIT-BIH Arrhythmia Database, http://ecg.mit.edu/dbinfo.html
  15. 15.
    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), 215–220 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kai Huang
    • 1
  • Liqing Zhang
    • 1
  • Yang Wu
    • 1
  1. 1.MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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