Automatic Identification of an Epileptic Spike Pattern in an EEG Signals Using ANN

  • Mohd. Zuhair
  • Sonia Thomas
  • Anup Kumar Keshri
  • Rakesh Kumar Sinha
  • Kirat Pal
  • Dhrubes Biswas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)

Abstract

This work emphasizes to automatically detect the pattern called ‘epileptic spike’ from electroencephalogram (EEG) signal using multilayer perceptron (MLP). The analysis work is carried out through using the receiver operating characteristics (ROC). Electroencephalograph is used to record the electrical activity of the brain. Classification of the (EEG) signal plays a vital role in the diagnosis of epilepsy. The verification of epileptic seizure requires long-term EEG monitoring of 24 h or more. The signal is a huge collection of data and unfortunately, medicos uses the traditional method of visually interpreting EEG signal through personal experience to identify the transient event of epilepsy. This method of visual interpretation is tedious and time-consuming and, may result in an erroneous judgment. Hence, efficient EEG signal analysis is required for the diagnosis of epilepsy. This model is evaluated on the basis of sensitivity and selectivity and experimental result highlights the good precision of the model. The overall accuracy of the model is computed as 99.11 %.

Keywords

EEG ROC MLP 

References

  1. 1.
    World Health Organization Media centre factsheet. http://who.int/mediacentre/factsheets/fs999/en/index.html (2012)
  2. 2.
    Srinivasan, V., Eswaran, C., Sriram, N.: Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29, 647–660 (2005). doi: 10.1007/s10916-005-6133-1 CrossRefGoogle Scholar
  3. 3.
    Kutlu, K., Kuntalp, M., Kuntalp, D.: Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes. Expert Syst. Appl. 36, 7567–7575 (2009). doi: 10.1016/j.eswa.2008.09.052 CrossRefGoogle Scholar
  4. 4.
    Sezer, E., Isik, H., Saracoglu, E.: Employment and comparison of different artificial neural networks for epilepsy diagnosis from EEG signals. J. Med. Syst. 36, 347–362 (2010). doi: 10.1007/s10916-010-9480-5 CrossRefGoogle Scholar
  5. 5.
    Subasi, A., Ercelebi, E.: Classification of eeg signal using neural network and logistic regression. Comput. Methods Programs Biomed. 78, 87–99 (2005). doi: 10.1016/j.cmpb.2004.10.009 CrossRefGoogle Scholar
  6. 6.
    Sahin, C., Ogulata, N.S., Aslan, K., Bozdemir, H.: The application of neural networks in classification of epilepsy using EEG signals. In: Mele, F., et al. (eds.) BVAI 2007. LNCS, vol. 4729, pp. 499–508. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Pradhan, N., Sadasivan, P.K., Arunodaya, G.R.: Detection of seizure activity in EEG by an artificial neural network: a preliminary study. Comput. Biomed. Res. 29, 303–313 (1996)CrossRefGoogle Scholar
  8. 8.
    Jahankani, P., Kodogiannis, V., Lygouras, J.: Adaptive fuzzy inference neural network system for EEG signal classification. In: Jain, L.C., Lim, C. P. (eds.) Handbook on Decision Making. ISRL vol. 4, pp. 453–471. Springer, Heidelberg (2010)Google Scholar
  9. 9.
    Kumar, Y., Dewal, M.L., Anand, R.S.: Epileptic seizures detection in EEG using dwt-based ApEn and artificial neural network. SIViP (2012). doi: 10.1007/s11760-012-0362-9 Google Scholar
  10. 10.
    Geva, A. B.: Forecasting generalized epileptic seizure from the eeg signal by wavelet analysis and dynamic unsupervized fuzzy clustering. IEEE Trans. Biomed. Eng. 45, 1205–1216 (1998)Google Scholar
  11. 11.
    Keshri, A.K., Sinha, R.K., Hatwal, R., Das, B.N.: Epileptic spike recognition in electroencephalogram using deterministic automata. J. Med. Syst. 33, 173–179 (2009). doi: 10.1007/s10916-008-9177-1 CrossRefGoogle Scholar
  12. 12.
    Keshri, A.K., Sinha, R.K., Mallik, D.K., Das, B.N.: Parallel algorithm to analyze the brain signals: application on epileptic spike. J. Med. Syst. 35, 93–104 (2011). doi: 10.1007/s10916-009-9345-y CrossRefGoogle Scholar
  13. 13.
    Lopes, H.S.: Genetic programming for epileptic pattern recognition in electroencephalographic signals. Appl. Soft Comput. 7, 343–352 (2007). doi: 10.1016/j.asoc.2005.07.004 CrossRefGoogle Scholar
  14. 14.
    Abdullah, M. A., Abdullah, J. M., Abdullah, M. Z.: Seizure detection by means of Hidden Markov model and stationary wavelet transform of electroencephalogram signal. In: Proceedings of the IEEE-EMBS International Conference on Biomedicaland health (BHI (2012)), Hong Kong and Shenzhen (2012)Google Scholar
  15. 15.
    Gotman, J., Flanagan, D., Zhang, J., Rosenblatt, B.: Automatic seizure detection in the newborn: methods and initial evaluation. Electroencephalogr. Clin. Neurophysiol. 103, 356–362 (1997)CrossRefGoogle Scholar
  16. 16.
    Zoubir, M., Boashash, B.: Seizure detection of newborn EEG using a model-based approach. IEEE Trans. Biomed. Eng. 45, 673–685 (1998)CrossRefGoogle Scholar
  17. 17.
    Shaker, M.M.: EEG wave classifier using wavelet transform and fourier transform. Int. J. Biol. med. Sci. 12, 85–90 (2006)Google Scholar
  18. 18.
    Qu, H., Gotman, J.: A patient specific algorithm for the detection of seizure onset in long-term EEG monitoring: possible use of warning system. IEEE Trans. Biomed. Eng. 44(2), 115–122 (1997)CrossRefGoogle Scholar
  19. 19.
    Litt, B., Echauz, J.: Prediction of epileptic seizures. Lancet Neurol. 1, 22–30 (2002)Google Scholar
  20. 20.
    Othman, M.Z., Shaker, M.M., Abdullah, M.F.: EEG spike detection, sorting and localization. In: Proceedings of World Academy Science, Engineering and Technology, vol. 9, ISSN 1307-6884 (2005)Google Scholar
  21. 21.
    Indiradevi, K.P., Elias, E., Shathidevi, P.S., Nayak, D.S., Radhakrishnan, K.: A multi-level wavelet approach for automatic detection of epileptic spike in electroencephalogram. Comput. Biol. Med. 38, 805–816 (2008)CrossRefGoogle Scholar
  22. 22.
    Tzallas, A.T., Oikonomou, V.P., Fotiadis, D.I.: Epileptic spike detection using a Kalman filter based approach. In: Proceedings of the 28th Conference of the IEEE Engineering in Medicine and Biological Society, New York City, Aug 30–Sept 3 2006Google Scholar
  23. 23.
    Sarbadhikari, S.N.: A neural network confirms that physical exercise reverses EEG changes in depressed rats. Med. Eng. Phys. 17, 579–582 (1995)CrossRefGoogle Scholar
  24. 24.
    Sinha, R.K.: Electro-encephalogram disturbances in different sleep-awake states following exposure of high environmental heat. Med. Biol. Eng. Comput. 42, 282–287 (2004)CrossRefGoogle Scholar
  25. 25.
    Sinha, R.K.: Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-awake states in an animal model of heat stress. Med. Biol. Eng. Comput. 41, 595–600 (2003)CrossRefGoogle Scholar
  26. 26.
    Haykin, S.: Neural Networks. McMaster University, Ontario (1999)MATHGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Mohd. Zuhair
    • 1
  • Sonia Thomas
    • 2
  • Anup Kumar Keshri
    • 3
  • Rakesh Kumar Sinha
    • 4
  • Kirat Pal
    • 2
  • Dhrubes Biswas
    • 1
  1. 1.Rajendra Mishra School of Engineering EntrepreneurshipIndian Institute of TechnologyKharagpurIndia
  2. 2.Earthquake EngineeringIndian Institute of TechnologyRoorkeeIndia
  3. 3.Department of Information TechnologyBirla Institute of TechnlogyRanchiIndia
  4. 4.Department of Biomedical InstrumentationBirla Institute of TechnlogyRanchiIndia

Personalised recommendations