A Novel Feature Extraction Method for Epileptic Seizure Detection Based on the Degree Centrality of Complex Network and SVM

  • Haihong Liu
  • Qingfang MengEmail author
  • Qiang Zhang
  • Zaiguo Zhang
  • Dong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9772)


Epilepsy is a kind of ancient disease, which is affecting the life of patients. With the increasing of incidence of epilepsy, automatic epileptic seizure detection with high performance is of great clinical significance. In order to improve the efficiency of epilepsy diagnosis, a novel feature extraction method for epileptic EEG signal based on the statistical property of the complex network and an epileptic seizure detection algorithm, which is composed of the extracted feature and support vector machine (SVM) is proposed. The EEG signal is converted to complex network by horizontal visibility graph firstly. Then the degree centrality of complex network as a novel feature is calculated. At last, the extracted feature and SVM construct automatic epileptic seizure detection. A classification experiment of the epileptic EEG dataset is performed to evaluate the performance of the proposed detection algorithm. Experimental results show the novel feature we extracted can distinguish ictal EEG from interictal EEG clearly and the proposed detection algorithm achieves high classification accuracy which can be up to 93.92 %.


Epileptic seizure detection Feature extraction method Epileptic electroencephalograph (EEG) Degree centrality Support vector machine (SVM) Horizontal visibility graph (HVG) 



This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302128), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003, BS2014DX015).


  1. 1.
    Song, Y., Crowcroft, J., Zhang, J.: Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J. Neurosci. Methods 210, 132–146 (2012)CrossRefGoogle Scholar
  2. 2.
    Kannathal, N., Choo, M.L., Rajendra Acharya, U., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80, 187–194 (2005)CrossRefGoogle Scholar
  3. 3.
    Nurujjaman, M., Narayanan, R., Sekar Iyengar, A.N.: Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients. Nonlin. Biomed. Phys. 3, 6 (2009)CrossRefGoogle Scholar
  4. 4.
    Zhang, J., Sun, J., Luo, X., Zhang, K., Nakamurad, T., Small, M.: Characterizing pseudoperiodic time series through the complex network approach. Physica D 237, 2856–2865 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Zhang, J., Small, M.: Complex network from pseudoperiodic time series: topology versus dynamics. Phys. Rev. Lett. 96, 238701 (2006)CrossRefGoogle Scholar
  6. 6.
    Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002)CrossRefGoogle Scholar
  7. 7.
    Small, M., Zhang, J., Xu, X.: Transforming time series into complex networks. In: Zhou, J. (ed.) Complex 2009. LNICST, vol. 5, pp. 2078–2089. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Xiang, R., Zhang, J., Xu, X.K., Small, M.: Multiscale characterization of recurrence-based phase space networks constructed from time series. Chaos 22, 013107 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gao, Z., Jin, N.: Complex network from time series based on phase space reconstruction. Chaos 19, 033137 (2009)CrossRefGoogle Scholar
  10. 10.
    Marwan, N., Donges, J.F., Zou, Y., Donner, R.V., Kurths, J.: Complex network approach for recurrence analysis of time series. Phys. Lett. A 373, 4246–4254 (2009)CrossRefzbMATHGoogle Scholar
  11. 11.
    Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Ambiguities in recurrence-based complex network representations of time series. Phys. Rev. E 81, 015101(R) (2010)CrossRefGoogle Scholar
  12. 12.
    Gao, Z.K., Jin, N.D.: A directed weighted complex network for characterizing chaotic dynamics from time series. Nonlinear Anal. Real World Appl. 13, 947–952 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proc. Natl. Acad. Sci. USA 105, 4972–4975 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Lacasa, L., Toral, R.: Description of stochastic and chaotic series using visibility graphs. Phys. Rev. E 82, 036120 (2010)CrossRefGoogle Scholar
  15. 15.
    Yang, Y., Wang, J., Yang, H., Mang, J.: Visibility graph approach to exchange rate series. Phys. A: Stat. Mech. Appl. 388, 4431–4437 (2009)CrossRefGoogle Scholar
  16. 16.
    Ni, X.H., Jiang, Z.Q., Zhou, W.X.: Degree distributions of the visibility graphs mapped from fractional Brownian motions and multifractal random walks. Phys. Lett. A 373, 3822–3826 (2009)CrossRefzbMATHGoogle Scholar
  17. 17.
    Lacasa, L., Luque, B., Luque, J., Nuño, J.C.: The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion. EPL 86, 30001 (2009)CrossRefGoogle Scholar
  18. 18.
    Donges, J.F., Donner, R.V., Kurths, J.: Testing time series irreversibility using complex network methods. EPL 102, 10004 (2013)CrossRefGoogle Scholar
  19. 19.
    Qian, M.C., Jiang, Z.Q., Zhou, W.X.: Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices. J. Phys. A Math. Theor. 43, 33 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Elsner, J.B., Jagger, T.H., Fogarty, E.A.: Visibility network of United States hurricanes. Geophys. Res. Lett. 36, L16702 (2009)CrossRefGoogle Scholar
  21. 21.
    Liu, C., Zhou, W.X., Yuan, W.K.: Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence. Phys. A Stat. Mech. Appl. 389, 2675–2681 (2010)CrossRefGoogle Scholar
  22. 22.
    Tang, Q., Liu, J., Liu, H.: Comparison of different daily streamflow series in US and China, under a viewpoint of complex networks. Mod. Phys. Lett. B 24, 1541–1547 (2010)CrossRefzbMATHGoogle Scholar
  23. 23.
    Wang, N., Li, D., Wang, Q.: Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory. Phys. A 391, 6543–6555 (2012)CrossRefGoogle Scholar
  24. 24.
    Moguerza, J., Muñoz, A.: Support vector machines with applications. Stat. Sci. 21, 322–336 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, London (2000)CrossRefzbMATHGoogle Scholar
  26. 26.
    Luque, B., Lacasa, L., Ballesteros, F., Liuque, J.: Horizontal visibility graphs: exact results for random time series. Phys. Rev. E 80, 046103 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Haihong Liu
    • 1
    • 2
  • Qingfang Meng
    • 1
    • 2
    Email author
  • Qiang Zhang
    • 3
  • Zaiguo Zhang
    • 4
  • Dong Wang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina
  3. 3.Institute of Jinan Semiconductor Elements ExperimentationJinanChina
  4. 4.CET Shandong Electronics Co., Ltd.JinanChina

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