Analyzing EEG Signal Data for Detection of Epileptic Seizure: Introducing Weight on Visibility Graph with Complex Network Feature

  • Supriya
  • Siuly
  • Hua Wang
  • Guangping Zhuo
  • Yanchun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)


In the medical community, automatic epileptic seizure detection through electroencephalogram (EEG) signals is still a very challenging issue for medical professionals and also for the researchers. When measuring an EEG, huge amount of data are obtained with different categories. Therefore, EEG recording can be characterized as big data due to its high volume. Traditional methods are facing challenges to handle such Big Data as it exhibits non-stationarity, chaotic, voluminous, and volatile in nature. Motivated by this, we introduce a new idea for epilepsy detection using complex network statistical property by measuring different strengths of the edges in the natural visibility graph theory. We conducted 10-fold cross validation for evaluating the performance of our proposed methodology with support vector machine (SVM) and Discriminant Analysis (DA) families of classifiers. This study aims to investigate the effect of segmentation and non-segmentation of EEG signals in the detection of epilepsy disorder.


EEG Epilepsy Complex network Visibility graph Average weighted degree SVM and LDA 


  1. 1.
    Siuly, S., Li, Y.: Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Comput. Methods Programs Biomed. 119, 29–42 (2015)CrossRefGoogle Scholar
  2. 2.
    Supriya, S., Siuly, S., Zhang, Y.: Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron. Lett. (2016)Google Scholar
  3. 3.
    Donner, R., Small, M., Donges, J., Marwan, N., Zou, Y., Xiang, R., Kurths, J.: Recurrence-based time series analysis by means of complex network methods. Int. J. Bifurcat. Chaos 21, 1019–1046 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Campanharo, A., Sirer, M., Malmgren, R., Ramos, F., Amaral, L.: Duality between Time Series and Networks. PLoS ONE 6, e23378 (2011)CrossRefGoogle Scholar
  5. 5.
    van Stam, C., Straaten, E.: The organization of physiological brain networks. Clin. Neurophysiol. 123, 1067–1087 (2012)CrossRefGoogle Scholar
  6. 6.
    Ahmadlou, M., Adeli, H., Adeli, A.: New diagnostic EEG markers of the Alzheimer’s disease using visibility graph. J. Neural Transm. 117, 1099–1109 (2010)CrossRefGoogle Scholar
  7. 7.
    Tang, X., Xia, L., Liao, Y., Liu, W., Peng, Y., Gao, T., Zeng, Y.: New approach to epileptic diagnosis using visibility graph of high-frequency signal. Clin. EEG Neurosci. 44, 150–156 (2013)CrossRefGoogle Scholar
  8. 8.
    Ni, Y., Wang, Y., Yu, T., Li, X.: Analysis of epileptic seizures with complex network. Comput. Math. Methods Med. 2014, 1–6 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.: From time series to complex networks: The visibility graph. Proc. Nat. Acad. Sci. 105, 4972–4975 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Antoniou, I., Tsompa, E.: Statistical analysis of weighted networks. Discrete Dyn. Nat. Soci. 2008, 1–16 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Andrew, A.: An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods (2001)Google Scholar
  12. 12.
    Mikat, S., Fitscht, G., Weston, J., Scholkopft, B., Muller, K.-R.: Fisher discriminant analysis with kernels. Neural Net. Signal Proc. IX, 41–48 (1999)Google Scholar
  13. 13.
    Andrzejak, R., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phys. Rev. E 64, 61907 (2001)CrossRefGoogle Scholar
  14. 14.
    Siuly, L.: Y., Wen, P.: Clustering technique-based least square support vector machine for EEG signal classification. Comput. Methods Programs Biomed. 104, 358–372 (2011)CrossRefGoogle Scholar
  15. 15.
    Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on Permutation Entropy and support vector machines. Expert Syst. Appl. 39, 202–209 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhu, G., Li, Y., Wen, P.: Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Comput. Methods Programs Biomed. 115, 64–75 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Supriya
    • 1
  • Siuly
    • 1
  • Hua Wang
    • 1
  • Guangping Zhuo
    • 2
  • Yanchun Zhang
    • 1
  1. 1.Centre for Applied Informatics, College of Engineering and ScienceVictoria UniversityMelbourneAustralia
  2. 2.Department of Computer ScienceTaiyuan Normal UniversityTaiyuanChina

Personalised recommendations