Advertisement

A New Method for Classification of Focal and Non-focal EEG Signals

  • Vipin GuptaEmail author
  • Ram Bilas Pachori
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

In this paper, we have proposed a new methodology based on the empirical mode decomposition (EMD) for classification of focal electroencephalogram (FE) and non-focal electroencephalogram (NFE) signals. The proposed methodology uses EMD along with Sharma–Mittal entropy feature computed on Euclidean distance values from K-nearest neighbors (KNN) of FE and NFE signals. The EMD method is used to decompose these electroencephalogram (EEG) signals into amplitude modulation and frequency modulation (AM–FM) components, which are also known as intrinsic mode functions (IMFs) then the KNN approach-based Sharma–Mittal entropy feature has been computed on these IMFs. These extracted features play significant role for the classification of FE and NFE signals with the help of least squares support vector machine (LS-SVM) classifier. The classification step includes radial basis function (RBF) kernel along with tenfold cross-validation process. The proposed methodology has achieved classification accuracy of 83.18% on entire Bern-Barcelona database of FE and NFE signals. The proposed method can be beneficial for the neurosurgeons to identify focal epileptic areas of the patient brain.

Keywords

EEG EMD KNN IMF LS-SVM 

Notes

Acknowledgements

This work was supported by the Council of Scientific and Industrial Research (CSIR) funded Research Project, Government of India, Grant No. 22/687/15/EMR-II.

References

  1. 1.
    Altunay, S., Telatar, Z., Erogul, O.: Epileptic EEG detection using the linear prediction error energy. Expert Syst. Appl. 37(8), 5661–5665 (2010)CrossRefGoogle Scholar
  2. 2.
    Andrzejak, R.G., Schindler, K., Rummel, C.: Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E 86, 046206 (2012)CrossRefGoogle Scholar
  3. 3.
    Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)CrossRefGoogle Scholar
  4. 4.
    Bajaj, V., Pachori, R.B.: Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed. Eng. Lett. 3(1)(1), 17–21 (2013)Google Scholar
  5. 5.
    Bhattacharyya, A., Pachori, R.B.: A multivariate approach for patient specific EEG seizure detection using empirical wavelet transform. IEEE Trans. Biomed. Eng. 64(9), 2003–2015 (2017)Google Scholar
  6. 6.
    Bhattacharyya, A., Sharma, M., Pachori, R.B., Sircar, P., Acharya, U.R.: A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Comput. Appl. 29(8), 47–57 (2018)Google Scholar
  7. 7.
    Bhattacharyya, A., Gupta, V., Pachori, R.B.: Automated identification of epileptic seizure EEG signals using empirical wavelet transform based Hilbert marginal spectrum. In: 22nd International Conference on Digital Signal Processing August 23-25, London, United Kingdom. IEEE, 1–5 (2017)Google Scholar
  8. 8.
    Bhattacharyya, A., Pachori, R.B., Acharya, U.R.: Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy 19(3) (2017)Google Scholar
  9. 9.
    Das, A.B., Bhuiyan, M.I.H.: Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Signal Process. Control 29, 11–21 (2016)Google Scholar
  10. 10.
    Fisher, R.S., Boas, W.E., Blume, W., Elger, C., Genton, P., Lee, P., Engel, J.: Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 46(4), 470–472 (2005)CrossRefGoogle Scholar
  11. 11.
    Gloor, P., Fariello, R.G.: Generalized epilepsy: some of its cellular mechanisms differ from those of focal epilepsy. Trends Neurosci. 11(2), 63–68 (1988)CrossRefGoogle Scholar
  12. 12.
    Gupta, V., Bhattacharyya, A., Pachori, R.B.: Classification of seizure and non-seizure EEG signals based on EMD-TQWT method. In: 22nd International Conference on Digital Signal Processing August 23-25, London, United Kingdom. IEEE, 1–5 (2017)Google Scholar
  13. 13.
    Gupta, V., Priya, T., Yadav, A.K., Pachori, R.B., Acharya, U.R.: Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recogn. Lett. 94, 180–188 (2017)CrossRefGoogle Scholar
  14. 14.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995. The Royal Society (1998)Google Scholar
  15. 15.
    Khandoker, A.H., Lai, D.T.H., Begg, R.K., Palaniswami, M.: Wavelet-Based feature extraction for support vector machines for screening balance impairments in the elderly. IEEE Trans. Neural Syst. Rehabil. Eng. 15(4), 587–597 (2007)Google Scholar
  16. 16.
    Martis, R.J., Acharya, U.R., Tan, J.H., Petznick, A., Yanti, R., Chua, C.K., Ng, E.Y.K., Tong, L.: Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals. Int. J. Neural Syst. 22(6), 1250027, 1–16 (2012)Google Scholar
  17. 17.
    McKight, P.E., Najab, J.: Kruskal-Wallis test. Corsini Encyclopedia of Psychology. Wiley, Hoboken (2010)Google Scholar
  18. 18.
    Pati, S., Alexopoulos, A.V.: Pharmacoresistant epilepsy: from pathogenesis to current and emerging therapies. Clevel. Clin. J. Med. 77, 457–467 (2010)CrossRefGoogle Scholar
  19. 19.
    Sharma, B.D., Mittal, D.P.: New non-additive measures of entropy for discrete probability distributions. J. Math. Sci 10, 28–40 (1975)MathSciNetGoogle Scholar
  20. 20.
    Sharma, R., Pachori, R.B., Gautam, S.: Empirical mode decomposition based classification of focal and non-focal EEG signals. In: International Conference on Medical Biometrics, pp. 135–140, Shenzhen (2014)Google Scholar
  21. 21.
    Sharma, R., Pachori, R.B., Acharya, U.R.: Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2), 669–691 (2015)Google Scholar
  22. 22.
    Sharma, R., Pachori, R.B., Acharya, U.R.: An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 17, 5218–5240 (2015)CrossRefGoogle Scholar
  23. 23.
    Sharma, M., Dhere, A., Pachori, R.B., Acharya, U.R.: An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks. Knowl.-Based Syst. 118, 217–227 (2017)CrossRefGoogle Scholar
  24. 24.
    Sharma, R., Kumar, M., Pachori, R.B., Acharya, U.R.: Decision support system for focal EEG signals using tunable-Q wavelet transform. J. Comput. Sci. 20, 52–60 (2017)Google Scholar
  25. 25.
    Singh, P., Pachori, R.B.: Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms. J. Mech. Med. Biol. 17(04), 1740002, 1–16 (2017)Google Scholar
  26. 26.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  27. 27.
    Szabó, Z.: Information theoretical estimators toolbox. J. Mach. Learn. Res. 15(1), 283–287 (2014)zbMATHGoogle Scholar
  28. 28.
    The Bern-Barcelona EEG database (2013). http://ntsa.upf.edu/downloads

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

Personalised recommendations