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Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

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Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  12. Kiranyaz, S., Ince, T., Zabihi, M., Ince, D.: Automated patient-specific classification of long-term electroencephalography. J. Biomed. Inform. 49, 16–31 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011)

    Article  MathSciNet  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. Takens, Floris, et al.: Detecting strange attractors in turbulence. Lect. Notes Math. 898(1), 366–381 (1981)

    Article  MathSciNet  Google Scholar 

  25. Witte, H., Iasemidis, L.D., Litt, B.: Special issue on epileptic seizure prediction. IEEE Trans. Biomed. Eng. 50(5), 537–539 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

    Abhijit Bhattacharyya, Lokesh Singh & Ram Bilas Pachori

Authors
  1. Abhijit Bhattacharyya
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  2. Lokesh Singh
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  3. Ram Bilas Pachori
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Corresponding author

Correspondence to Abhijit Bhattacharyya .

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Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Bhattacharyya, A., Singh, L., Pachori, R.B. (2019). Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_18

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