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A New ECG Signal Classification Based on WPD and ApEn Feature Extraction

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Abstract

Electrocardiogram (ECG) signal classification is an important diagnosis tool wherein feature extraction plays a crucial function. This paper proposes a novel method for the nonlinear feature extraction of ECG signals by combining wavelet packet decomposition (WPD) and approximate entropy (ApEn). The proposed method first uses WPD to decompose ECG signals into different frequency bands and then calculates the ApEn of each wavelet packet coefficient as a feature vector. A support vector machine (SVM) classifier is used for the classification. The particle swarm optimization algorithm is used to optimize the SVM parameters. The proposed method does not require dimensionality reduction, has fast calculation speed, and requires simple computations. The classification of the signals into five beats yields an acceptable accuracy of 97.78 %.

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References

  1. U.R. Acharya, O. Faust, N.A. Kadri, J.S. Suri, W. Yu, Automated identification of normal and diabetes heart rate signals using nonlinear measures. Comput. Biol. Med. 43(10), 1523–1529 (2013)

    Article  Google Scholar 

  2. A.S. Alvarado, C. Lakshminarayan, J.C. Principe, Time-based compression and classification of heartbeats. IEEE Trans. Biomed. Eng. 59(6), 1641–1648 (2012)

    Article  Google Scholar 

  3. J.L. Camargo-Olivares, R. Martín-Clemente, S. Hornillo-Mellado, M.M. Elena, I. Román, The maternal abdominal ECG as input to MICA in the fetal ECG extraction problem. IEEE Signal Process. Lett. 18(3), 161–164 (2011)

    Article  Google Scholar 

  4. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines. ACM TIST 2(3), 27 (2011)

    Google Scholar 

  5. F. Ebrahimi, S.K. Setarehdan, J. Ayala-Moyeda, H. Nazeran, Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals. Comput. Methods Prog. Biomed. 112(1), 47–57 (2013)

    Article  Google Scholar 

  6. S. Fei, Diagnostic study on arrhythmia cordis based on particle swarm optimization-based support vector machine. Expert Syst. Appl. 37(10), 6748–6752 (2010)

    Article  Google Scholar 

  7. D. Giri, U.R. Acharya, R.J. Martis, S.V. Sree, T.C. Lim, T. Ahamed, J.S. Suri, Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl. Based Syst. 37, 274–282 (2013)

    Article  Google Scholar 

  8. R. Hornero, D. Abásolo, N. Jimeno, C.I. Sánchez, J. Poza, M. Aboy, Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects. IEEE Trans. Biomed. Eng. 53(2), 210–218 (2006)

    Article  Google Scholar 

  9. T. Ince, S. Kiranyaz, M. Gabbouj, A generic and robust system for automated patient-specific classification of ECG signals. IEEE Trans. Biomed. Eng. 56(5), 1415–1426 (2009)

    Article  Google Scholar 

  10. C. Kamath, ECG beat classification using features extracted from Teager energy functions in time and frequency domains. IET Signal Process. 5(6), 575–581 (2011)

    Article  MathSciNet  Google Scholar 

  11. J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks (1995), pp. 1942–1948

  12. Y. Kutlu, D. Kuntalp, Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Methods Prog. Biomed. 105(3), 257–267 (2012)

    Article  Google Scholar 

  13. H.Q. Li, X.F. Wang, Detection of electrocardiogram characteristic points using lifting wavelet transform and Hilbert transform. Trans. Inst. Meas. Control 35(5), 574–582 (2013)

    Article  Google Scholar 

  14. H.Q. Li, X.F. Wang, L. Chen, E.B. Li, Denoising and R-Peak detection of electrocardiogram signal based on EMD and improved approximate envelope. Circuits Syst. Signal Process. 33(4), 1261–1276 (2014)

    Article  Google Scholar 

  15. R.J. Martis, U.R. Acharya, K.M. Mandana, A.K. Ray, C. Chakraborty, Cardiac decision making using higher order spectra. Biomed. Signal Process. 8(2), 193–203 (2013)

    Article  Google Scholar 

  16. S.M. Pincus, Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88(6), 2297–2301 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  17. V.N. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, 1995)

    Book  MATH  Google Scholar 

  18. C. Ye, B.V.K.V. Kumar, M.T. Coimbra, Heartbeat classification using morphological and dynamic features of ECG signals. IEEE Trans. Biomed. Eng. 59(10), 2930–2941 (2012)

    Article  Google Scholar 

  19. A.E. Zadeh, A. Khazaee, V. Ranaee, Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput. Methods Prog. Biomed. 99(2), 179–194 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

This paper is 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, Xiuli Feng, Huan Liang & Xuelong Chen

  2. Tianjin Chest Hospital, Tianjin, 300000, China

    Lu Cao

  3. 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. Xiuli Feng
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  3. Lu Cao
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  4. Enbang Li
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  5. Huan Liang
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  6. Xuelong Chen
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Correspondence to Hongqiang Li.

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Li, H., Feng, X., Cao, L. et al. A New ECG Signal Classification Based on WPD and ApEn Feature Extraction. Circuits Syst Signal Process 35, 339–352 (2016). https://doi.org/10.1007/s00034-015-0068-7

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  • DOI: https://doi.org/10.1007/s00034-015-0068-7

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