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Detection of Epileptic EEG Signal Using Improved Local Pattern Transformation Methods

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Abstract

This paper aims to introduce efficient computer-aided techniques for automated epileptic diagnosis using the features based on improved local pattern transformation methods (LPT). To analyze electroencephalographic (EEG) signal, three techniques, namely one-dimensional local neighbor descriptive count, one-dimensional local gradient count and one-dimensional local binary count, are proposed in this work. Further, a signature point-based improved LPT approach is introduced for effectual classification of EEG signals. The features are computed at the signature points of the EEG signals, which are detected by using the difference of Gaussian pyramid. The features extracted from the signature points of the EEG signals are fed into artificial neural network (ANN) classifier for the discrimination of EEG signals. In this paper, seventeen different classification cases based on six different experimental cases are evaluated using the University of Bonn EEG database. Experimental results show that high classification accuracy for all the cases is achieved using the proposed approach and it also compares favorably to other state-of-the-art methods.

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References

  1. U.R. Acharya, S.V. Sree, A.P. Alvin, R. Yanti, J.S. Suri, Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals. Int. J. Neural Syst. 22(2), 1250002 (2012)

    Article  Google Scholar 

  2. H. Adeli, Z. Zhou, N. Dadmehr, Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123, 69–87 (2003)

    Article  Google Scholar 

  3. R.G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, C.E. Elger, 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(6), 061907 (2001)

    Article  Google Scholar 

  4. V. Bajaj, R.B. Pachori, Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16, 1135–1142 (2012)

    Article  Google Scholar 

  5. M. Crouse, R. Nowak, R. Baraniuk, Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans. Signal Process. 46, 886–902 (1998)

    Article  MathSciNet  Google Scholar 

  6. I. Gûler, E.D. Ubeyli, Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 148(2), 113–121 (2005)

    Article  Google Scholar 

  7. A.R. Hassan, S. Siuly, Y. Zhang, 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 

  8. A.R. Hassan, A. Subasi, Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput. Methods Programs Biomed. 136, 65–77 (2016)

    Article  Google Scholar 

  9. A. Hyvärinen, P. Ramkumar, L. Parkkonen, R. Hari, Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage 49, 257–271 (2010)

    Article  Google Scholar 

  10. A.K. Jaiswal, H. Banka, Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals. Biomed. Signal Process. Control 34, 81–92 (2017)

    Article  Google Scholar 

  11. H. Kalbkhani, M.G. Shayesteh, Stockwell transform for epileptic seizure detection from EEG signals. Biomed. Signal Process. Control 38, 108–118 (2017)

    Article  Google Scholar 

  12. Y. Kaya, M. Uyar, R. Tekin, S. Yıldırım, 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl. Math. Comput. 243, 209–219 (2014)

    MathSciNet  MATH  Google Scholar 

  13. T.S. Kumar, V. Kanhangad, R.B. Pachori, Classification of seizure and seizure-free EEG signals using local binary patterns. Biomed. Signal Process. Control 15, 33–40 (2015)

    Article  Google Scholar 

  14. Y. Kumar, M.L. Dewal, R.S. Anand, Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 133, 271–279 (2014)

    Article  Google Scholar 

  15. D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)

    Article  Google Scholar 

  16. R.B. Pachori, Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process. 2008, 1–5 (2008)

    Article  Google Scholar 

  17. R.B. Pachori, P. Sircar, EEG signal analysis using FB expansion and second-order linear TVAR process. Sig. Process. 88, 415–420 (2008)

    Article  Google Scholar 

  18. K. Polat, S. Günes, Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl. Math. Comput. 187(2), 1017–1026 (2007)

    MathSciNet  MATH  Google Scholar 

  19. F. Riaz, A. Hassan, S. Rehman, I.K. Niazi, K. Dremstrup, EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 28–35 (2016)

    Article  Google Scholar 

  20. N. Robinson, A.P. Vinod, K.K. Ang, K.P. Tee, C.T. Guan, EEG-based classification of fast and slow hand movements using wavelet-CSP algorithm. IEEE Trans. Biomed. Eng. 60, 2123–2132 (2013)

    Article  Google Scholar 

  21. M. Sharma, R.B. Pachori, U.R. Acharya, A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn. Lett. 94, 172–179 (2017)

    Article  Google Scholar 

  22. A. Sharmila, P. Geethanjali, DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access. 4, 7716–7727 (2016)

    Article  Google Scholar 

  23. V. Srinivasan, C. Eswaran, N. Sriraam, Artificial neural network based epileptic detection using time-domain and frequency-domain features. J. Med. Syst. 29(6), 647–660 (2005)

    Article  Google Scholar 

  24. P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, S. Anand, A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56, 116–130 (2016)

    Article  Google Scholar 

  25. N.S. Tawfik, S.M. Youssef, M. Kholief, A hybrid automated detection of epileptic seizures in EEG records. Comput. Electr. Eng. 53, 177–190 (2016)

    Article  Google Scholar 

  26. A.K. Tiwari, R.B. Pachori, V. Kanhangad, B.K. Panigrahi, Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE J. Biomed. Health Inf. 21(4), 888–896 (2017)

    Article  Google Scholar 

  27. A. Tzallas, M. Tsipouras, D. Fotiadis, Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)

    Article  Google Scholar 

  28. T. Zhang, W. Chen, M. Li, Fuzzy distribution entropy and its application in automated seizure detection technique. Biomed. Signal Process. Control 39, 360–377 (2018)

    Article  Google Scholar 

  29. T. Zhang, W. Chen, LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans. Neural Syst. Rehabil. Eng. 25(8), 1100–1108 (2017)

    Article  Google Scholar 

  30. Y. Zhang, G. Pan, K. Jia, M. Lu, Y. Wang, Z. Wu, Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Transactions on Cybernetics 45, 1864–1875 (2015)

    Article  Google Scholar 

  31. Y. Zhao, D.S. Huang, W. Jia, Completed local binary count for rotation invariant texture classification. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This study was funded by the Department of Science and Technology (TSDP), Ministry of Science and Technology, Government of India [Grant Nos. DST/TSG/ICT/2015/54-G, 2015].

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Authors and Affiliations

  1. Department of Electrical Sciences, Karunya Institute of Technology and Sciences, Coimbatore, India

    N. J. Sairamya, S. Thomas George, D. Narain Ponraj & M. S. P. Subathra

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  1. N. J. Sairamya
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  2. S. Thomas George
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  3. D. Narain Ponraj
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  4. M. S. P. Subathra
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Correspondence to S. Thomas George.

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Sairamya, N.J., George, S.T., Ponraj, D.N. et al. Detection of Epileptic EEG Signal Using Improved Local Pattern Transformation Methods. Circuits Syst Signal Process 37, 5554–5575 (2018). https://doi.org/10.1007/s00034-018-0829-1

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  • DOI: https://doi.org/10.1007/s00034-018-0829-1

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