Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

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


In this work, we propose a method for epileptic seizure detection from scalp electroencephalogram (EEG) signals. The proposed method is based on the application of tunable-Q wavelet transform (TQWT). The long duration scalp EEG signals have been segmented into one-second duration segments using a moving window-based scheme. After that, TQWT has been applied in order to decompose scalp EEG signals segments into multiple sub-band signals of different oscillatory levels. We have generated two-dimensional (2D) reconstructed phase space (RPS) plot of each of the sub-band signals. Further, the central tendency measure (CTM) has been applied in order to measure the area of the 2D-RPS plots. These computed area measures have been used as features for distinguishing seizure and seizure-free EEG signal segments. Finally, we have used a feature-processing technique which clearly discriminates epileptic seizures in the scalp EEG signals. The proposed method may also find application in the online detection of epileptic seizures from intracranial EEG signals.


TQWT RPS CTM Scalp EEG signal Epileptic seizure detection 


  1. 1.
    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), 17–21 (2013)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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, London, United Kingdom (UK) (2017)Google Scholar
  4. 4.
    Bhattacharyya, A., Pachori, R.B., Rajendra Acharya, U.: Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG Signal Analysis. Entropy 19(99) (2017)Google Scholar
  5. 5.
    Bhattacharyya, A., Pachori, R.B., Upadhyay, A., Acharya, U.R.: Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(385) (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. pp. 1–11 (2017)Google Scholar
  7. 7.
    Gabor, A.J.: Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies. Electroencephalogr. Clin. Neurophysiol. 107(1), 27–32 (1998)CrossRefGoogle Scholar
  8. 8.
    Gabor, A.J., Leach, R.R., Dowla, F.U.: Automated seizure detection using a self-organizing neural network. Electroencephalogr. Clin. Neurophysiol. 99(3), 257–266 (1996)CrossRefGoogle Scholar
  9. 9.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C., Stanley, H.E.: Physiobank, physiotoolkit, and physionet. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  10. 10.
    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, London, United Kingdom (UK) (2017)Google Scholar
  11. 11.
    Hassan, A.R., Siuly, S., Zhang, Y.: Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput. Methods Programs Biomed. 137, 247–259 (2016)CrossRefGoogle Scholar
  12. 12.
    Kiranyaz, S., Ince, T., Zabihi, M., Ince, D.: Automated patient-specific classification of long-term electroencephalography. J. Biomed. Inform. 49, 16–31 (2014)CrossRefGoogle Scholar
  13. 13.
    O’Neill, N.S., Koles, Z.J., Javidan, M.: Identification of the temporal components of seizure onset in the scalp EEG. Can. J. Neurol. Sci. 28(3), 245–253 (2001)CrossRefGoogle Scholar
  14. 14.
    Osorio, I., Frei, M.G., Wilkinson, S.B.: Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia 39(6), 615–627 (1998)CrossRefGoogle Scholar
  15. 15.
    Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput. Methods Programs Biomed. 104(3), 373–381 (2011)CrossRefGoogle Scholar
  16. 16.
    Samiee, K., Kiranyaz, S., Gabbouj, M., Saramäki, T.: Long-term epileptic EEG classification via 2D mapping and textural features. Expert Syst. Appl. 42(20), 7175–7185 (2015)CrossRefGoogle Scholar
  17. 17.
    Saxena, M.K., Raju, S.D.V.S.J., Arya, R., Pachori, R.B., Kher, S.: Instantaneous area based online detection of bend generated error in a Raman optical fiber distributed temperature sensor. IEEE Sens. Lett. 1(4), 1–4 (2017)CrossRefGoogle Scholar
  18. 18.
    Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shah, M., Saurav, S., Sharma, R., Pachori, R.B.: Analysis of epileptic seizure EEG signals using reconstructed phase space of intrinsic mode functions. In: 9th International Conference on Industrial and Information Systems (ICIIS) 2014, pp. 1–6 (2014)Google Scholar
  20. 20.
    Sharma, R., Pachori, R.B.: Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst. Appl. 42(03), 1106–1117 (2015)CrossRefGoogle Scholar
  21. 21.
    Sharma, R.R., Pachori, R.B.: Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci. Meas. Technol. (2017)Google Scholar
  22. 22.
    Sheb, A., Guttag, J.: Application of machine learning to epileptic seizure detection. In: 27th International Conference on Machine Learning, Haifa, Israel (2010)Google Scholar
  23. 23.
    Shoeb, A.H.: Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. thesis, Ph.D dissertation, Massachusetts Institute of Technology, Cambridge, MA, USA (2009)Google Scholar
  24. 24.
    Takens, Floris, et al.: Detecting strange attractors in turbulence. Lect. Notes Math. 898(1), 366–381 (1981)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Witte, H., Iasemidis, L.D., Litt, B.: Special issue on epileptic seizure prediction. IEEE Trans. Biomed. Eng. 50(5), 537–539 (2003)CrossRefGoogle Scholar
  26. 26.
    Zabihi, M., Kiranyaz, S., Rad, A.B., Katsaggelos, A.K., Gabbouj, M., Ince, T.: Analysis of high-dimensional phase space via poincaré section for patient-specific seizure detection. IEEE Trans. Neural Syst. Rehabil. Eng. 24(3), 386–398 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhijit Bhattacharyya
    • 1
    Email author
  • Lokesh Singh
    • 1
  • Ram Bilas Pachori
    • 1
  1. 1.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

Personalised recommendations