Detection of Epileptic Seizure Using Wavelet Transform and Neural Network Classifier

  • S. M. WaniEmail author
  • S. Sabut
  • S. L. Nalbalwar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


The electroencephalograph (EEG) signals are most widely used for identification of neurological diseases like epilepsy, Alzheimer’s, and other brain diseases. Detection of epileptic activity requires a detailed analysis of the entire length of the EEG data. In this paper, we proposed an automated detection of epileptic seizure using energy distribution of wavelet coefficient in each sub-band frequencies of the EEG signals. The performance of the proposed method is investigated using signals obtained from public EEG database at the University Hospital Bonn, Germany. Initially, the EEG signals are de-noised and decomposed into sub-bands using discrete wavelet transform (DWT), Then wavelet energy distribution in each sub-band is calculated and used as a feature set. Finally, artificial neural network (ANN) used to classify the feature set with ANN. The method was tested on EEG data sets obtained from that belongs to three subject groups: (a) healthy, (b) seizure-free interval, and (c) epileptic syndrome during a seizure. The test result shows that the proposed method for detecting epileptic seizure can achieve an overall classification accuracy of 95%. The proposed method can be used efficiently for recognition of epileptic seizures.


Epilepsy EEG signal Discrete wavelet transform Energy distribution Neural network classifier 


  1. 1.
    Thakor, N.V., Tong, S.: Quantitative EEG Analysis Methods and Clinical Applications, Artech House, Boston/London, pp. 193–224 (2009)Google Scholar
  2. 2.
    Alotaiby, T., El-Samie, F.A., Alshebeili, S.A., Ahmad, I.: A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Sig. Process. 66 (2015)Google Scholar
  3. 3.
    Shaker, M.M.: EEG wave classifier using wavelet transform and Fourier transform. Int. J. Biol. Life Sci. 1, 85–90 (2005)Google Scholar
  4. 4.
    Omerhodzic, I., Causevic, E., Dizdarevic, K., Avdakovic, S., Music, M., Kusljugic, M., Haj-darpasic, E., Kadic, N.: First neurosurgical experience with the wavelet based EEG in diagnostic of concussion. In: Proceedings of 11th Congress of Neurosurgeons of Serbia, Serbia (2008)Google Scholar
  5. 5.
    Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Automatic seizure detection based on time-frequency, analysis and artificial neural networks. Comput. Intell. Neurosci. 1–13 (2007)CrossRefGoogle Scholar
  6. 6.
    Sharma, M., Pachorib, R.B., Acharya, U.R.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn. Lett. 94, 172–179 (2017)CrossRefGoogle Scholar
  7. 7.
    Wang, L., Xue, W., Li, Y., Luo, M., Huang, J., Cui, W., Huang, C.: Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis. Entropy 19, 222 (2017)CrossRefGoogle Scholar
  8. 8.
    Gandhi, T., Panigrahi, B.K., Anand, S.: A comparative study of wavelet families for EEG signal classification. Neurocomput. 74, 3051–3057 (2011)CrossRefGoogle Scholar
  9. 9.
    Patnaik, L.M., Manyam, O.K.: Epileptic EEG detection using neural networks and post-classification. Comput. Methods Progr. Biomed. 91(2), 100–109 (2008)CrossRefGoogle Scholar
  10. 10.
    Subasi, A., Ercelebi, E.: Classification of EEG signals using neural network and logistic regression. Comput. Methods Progr. Biomed. 78(2), 87–99 (2005)CrossRefGoogle Scholar
  11. 11.
    Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C.E.: 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, 061907 (2001)CrossRefGoogle Scholar
  12. 12.
    Zandi, A.S., Dumont, G.A., Javidan, M., Tafreshi, R., MacLeod, B.A., Ries, C.R., Puil, E.A.: A novel wavelet-based index to detect epileptic seizures using scalp EEG signals. In: Proceedings of IEEE Engineering in Medicine and Biology Society, pp. 919–922 (2008)Google Scholar
  13. 13.
    Faust, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26, 56–64 (2015)CrossRefGoogle Scholar
  14. 14.
    Lancashire, L.J., Lemetre, C., Ball, G.R.: An introduction to artificial neural networks in bioinformatics-application to complex microarray and mass spectrometry datasets in cancer studies. Brief. Bioinform. 10, 315–329 (2009)CrossRefGoogle Scholar
  15. 15.
    Atoufi, B., Lucas, C., Zakerolhosseini, A.: A survey of multi-channel prediction of EEG signal in different EEG state: normal, pre-seizure, and seizure. In: Proceedings of the 7th International Conference on Computer Science and Information Technologies, Yerevan, Armenia (2009)Google Scholar
  16. 16.
    Lan, T., Erdogmus, D., Adami, A., Mathan, S., Pavel, M.: Channel selection and feature projection for cognitive load estimation using ambulatory EEG. Comput. Intell. Neurosci. 1–12 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics EngineeringRamrao Adik Institute of TechnologyNavi MumbaiIndia
  2. 2.Department of Electronics & Telecommunication EngineeringDr. Babasaheb Ambedkar Technological UniversityLonereIndia

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