Detecting Epileptic Seizures Using Abe Entropy, Line Length and SVM Classifier

  • Aya NaserEmail author
  • Manal Tantawi
  • Howida Shedeed
  • Mohamed F. Tolba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Epilepsy is a 4th prevalent neurological disorder which affects the individuals in all ages around the world. Epilepsy disorder is characterized by the abnormal movements of human muscles, called seizure, as a result of the abnormality in the brain electrical activity. The electroencephalogram (EEG) can serve as a powerful tool for detecting Epilepsy. In this paper, the most commonly used Andrzejak database is utilized for building an automated system for epilepsy detection. Digital Wavelet Transform (DWT) is applied on the segmented EEG signals to extract the five EEG sub-bands (delta, theta, alpha, beta, and gamma). Approximation and Abe entropies along with line length are calculated for the extracted sub-bands. Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel function is used to distinguish between three classes: (1) normal, (2) interictal (seizure free interval), and (3) ictal (during seizure). The best accuracies achieved are 93.75%, 98.75% and 98.125% for normal, interictal and ictal classes respectively. These accuracies are achieved using the combination of both Abe entropy and line length features together.


Electroencephalogram (EEG) Epilepsy Seizure Entropies Line length Digital Wavelet Transform (DWT) Support Vector Machine (SVM) 


  1. 1.
  2. 2.
    Naser, A., Tantawi, M., Shedeed, H.A., Tolba, M.F.: EEG based epilepsy detection using approximation entropy and different classification strategies. In: 8th International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 92–97. IEEE, Egypt (2017)Google Scholar
  3. 3.
    Shen, C.P., Chan, C.M., Lin, F.S., Chiu, M.J., Lin, J.W., Kao, J.H., Chen, C.P., Lai, F.: Epileptic seizure detection for multichannel EEG signals with support vector machines. In: 11th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 39–43. IEEE (2011)Google Scholar
  4. 4.
    Kiymik, M.K., Subasi, A., Ozcalık, H.R.: Neural networks with periodogram and autoregressive spectral analysis methods in detection of epileptic seizure. J. Med. Syst. 28(6), 511–522 (2004)CrossRefGoogle Scholar
  5. 5.
    Srinivasan, V., Eswaran, C., Sriraam, N.: Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans. Inf. Technol. Biomed. 11(3), 288–295 (2007)CrossRefGoogle Scholar
  6. 6.
    Guo, L., Rivero, D., Dorado, J., Rabunal, J.R., Pazos, A.: Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. J. Neurosci. Methods 191(1), 101–109 (2010)CrossRefGoogle Scholar
  7. 7.
    Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193(1), 156–163 (2010)CrossRefGoogle Scholar
  8. 8.
    Ibrahim, S.W., Majzoub, S.: EEG-based epileptic seizures detection with adaptive learning capability. Int. J. Electr. Eng. Inf. 9(4), 813–824 (2017)Google Scholar
  9. 9.
    Gajic, D., Djurovic, Z., Di Gennaro, S., Gustafsson, F.: Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomed. Eng.: Appl. Basis Commun. 26(02), 1450021 (2014)Google Scholar
  10. 10.
    Husain, S.J., Rao, K.S.: Epileptic seizures classification from EEG signals using neural networks. In: 2012 International Conference on Information and Network Technology (ICINT 2012), vol. 37, pp. 269–273, April 2012Google Scholar
  11. 11.
    Juarez-Guerra, E., Alarcon-Aquino, V., Gomez-Gil, P.: Epilepsy seizure detection in EEG signals using wavelet transforms and neural networks. In: New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering, pp. 261–269. Springer, Cham (2015)Google Scholar
  12. 12.
    Kannathal, N., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 2(81), 193 (2006). Comput. Methods Programs Biomed. 80, 187–194 (2005)CrossRefGoogle Scholar
  13. 13.
    Subasi, A.: Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Syst. Appl. 31(2), 320–328 (2006)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, D., Miao, D., Xie, C.: Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Syst. Appl. 38(11), 14314–14320 (2011)Google Scholar
  15. 15.
    Nigam, V.P., Graupe, D.: A neural-network-based detection of epilepsy. Neurol. Res. 26(1), 55–60 (2004)CrossRefGoogle Scholar
  16. 16.
    Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: 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)CrossRefGoogle Scholar
  17. 17.
  18. 18.
    Beck, C.: Generalised information and entropy measures in physics. Contemp. Phys. 50(4), 495–510 (2009)CrossRefGoogle Scholar
  19. 19.
    Koolen, N., Jansen, K., Vervisch, J., Matic, V., De Vos, M., Naulaers, G., Van Huffel, S.: Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings. Clin. Neurophysiol. 125(10), 1985–1994 (2014)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Aya Naser
    • 1
    Email author
  • Manal Tantawi
    • 1
  • Howida Shedeed
    • 1
  • Mohamed F. Tolba
    • 1
  1. 1.Scientific Computing DepartmentFCIS-Ain Shames UniversityCairoEgypt

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