Probability-Based Approach for Epileptic Seizure Detection Using Hidden Markov Model

  • Deba Prasad DashEmail author
  • Maheshkumar H. Kolekar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


Seizure is defined as a sudden synchronous activity of a group of neurons resulting in an electric surge in the brain. Epilepsy is a brain disorder indicated by repeated seizures. Around 10 million people in India are suffering from epilepsy. Electroencephalogram (EEG) signal being low cost and non-invasive in nature can be used effectively for seizure detection. The present work focuses on developing an efficient epileptic seizure detection system using intracranial EEG signals. Dual tree complex wavelet transform is used to decompose the signal into various sub-frequency bands. Probability features are used to extract efficient indicators for seizure and healthy classes. Discriminant correlation analysis is used to increase the difference between different classes as well as reduce the difference between same classes. The fused feature set is clustered using fuzzy c means clustering algorithm. Hidden Markov model discriminates the seizure class with healthy class with good efficiency. Maximum accuracy of 98.57% is achieved for seizure detection with very low execution time.


EEG Epilepsy Seizure Dual tree complex wavelet transform Discriminant correlation analysis Hidden Markov Model 


  1. 1.
    Adeli, H., Ghosh-Dastidar, S., Dadmehr, N.: A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans. Biomed. Eng. 54, 205–211 (2007)CrossRefGoogle Scholar
  2. 2.
    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, 061907 (2001)CrossRefGoogle Scholar
  3. 3.
    Baldominos, A., Ramón-Lozano, C.: Optimizing EEG energy-based seizure detection using genetic algorithms. In: IEEE Congress on Evolutionary Computation, pp. 2338–2345 (2017)Google Scholar
  4. 4.
    Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1984)CrossRefGoogle Scholar
  5. 5.
    Damaševičius, R., Maskeliūnas, R., Woźniak, M., Połap, D.: Visualization of physiologic signals based on Hjorth parameters and Gramian angular fields. In: IEEE World Symposium on Applied Machine Intelligence and Informatics, pp. 000091–000096 (2018)Google Scholar
  6. 6.
    Dash, D.P., Kolekar, M.H.: Epileptic seizure detection based on EEG signal analysis using hierarchy based hidden Markov model. In: International Conference on Advances in Computing, Communications and Informatics, pp. 1114–1120 (2017)Google Scholar
  7. 7.
    Dash, D.P., Kolekar, M.H.: EEG based epileptic seizure detection using empirical mode decomposition and hidden Markov model. Indian J. Public Health Res. Dev. 8(4), 897 (2017)CrossRefGoogle Scholar
  8. 8.
    Deshmukh, P., Ingle, R., Kehri, V., Awale, R.: Epileptic seizure detection using discrete wavelet transform based support vector machine. In: International Conference on communication and Signal Processing, pp. 1933–1937 (2017)Google Scholar
  9. 9.
    Dheepa, N.: Automatic seizure detection using higher order moments & ANN. In: IEEE International Conference on Advances in Engineering, Science and Management, pp. 601–605 (2012)Google Scholar
  10. 10.
    Dhif, I., Hachicha, K., Pinna, A., Hochberg, S., Mhedhbi, I., Garda, P.: Epileptic seizure detection based on expected activity measurement and neural network classification. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2814–2817 (2017)Google Scholar
  11. 11.
    Elger, C.E., Hoppe, C.: Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 17, 279–288 (2018)CrossRefGoogle Scholar
  12. 12.
    Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans. Inf. Forensics Secur. 11(9), 1984–1996 (2016)CrossRefGoogle Scholar
  13. 13.
    Inan, Z., Kuntalp, M.: A study on fuzzy c-means clustering-based systems in automatic spike detection. Comput. Biol. Med. 37(8), 1160 (2007)CrossRefGoogle Scholar
  14. 14.
    Jha, C.K., Kolekar, M.H.: ECG data compression algorithm for tele-monitoring of cardiac patients. Int. J. Telemed. Clin. Pract. 2, 31–41 (2017)CrossRefGoogle Scholar
  15. 15.
    Kolekar, M.H., Sengupta, S.: Bayesian network-based customized highlight generation for broadcast soccer videos. IEEE Trans. Broadcasting 61(2), 195–209 (2015)CrossRefGoogle Scholar
  16. 16.
    Kolekar, M.H., Dash, D.P.: A nonlinear feature based epileptic seizure detection using least square support vector machine classifier. In: IEEE TENCON Region 10 Conference, pp. 1–6 (2015)Google Scholar
  17. 17.
    Kumar, A., Kolekar, M.H.: Machine learning approach for epileptic seizure detection using wavelet analysis of EEG signals. In: International Conference on Medical Imaging, m-Health and Emerging Communication Systems, pp. 412–416 (2014)Google Scholar
  18. 18.
    Liu, X., Jiang, A., Xu, N.: Automated epileptic seizure detection in EEGs using increment entropy. In: Canadian Conference on Electrical and Computer Engineering, pp. 1–4 (2017)Google Scholar
  19. 19.
    Malladi, R., Johnson, D.H., Kalamangalam, G.P., Tandon, N., Aazhang, B.: Data-driven estimation of mutual information using frequency domain and its application to epilepsy. In: Asilomar Conference on Signals, Systems, and Computers, pp. 2015–2019 (2017)Google Scholar
  20. 20.
    Ouyang, G., Li, X., Guan, X.P.: Use of fuzzy similarity index for epileptic seizure prediction. IEEE World Congress Intell. Control Autom. 6, 5351–5355 (2004)CrossRefGoogle Scholar
  21. 21.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22, 123–151 (2005)CrossRefGoogle Scholar
  22. 22.
    Shahid, A., Kamel, N., Malik, A.S., Jatoi, M.A.: Epileptic seizure detection using the singular values of EEG signals. In: International Conference on Complex Medical Engineering, pp. 652–655 (2013)Google Scholar
  23. 23.
    Sharma, N., Kolekar, M.H.: Diagnosis of vascular cognitive impairment using EEG. Indian J. Public Health Res. Dev. 8(4) (2017)Google Scholar
  24. 24.
    Sharma, N., Kolekar, M.H., Chandra, S.: The role of EEG signal processing in detection of neurocognitive disorders. Int. J. Behav. Healthcare Res. 6, 15–27 (2016)CrossRefGoogle Scholar
  25. 25.
    Swami, P., Gandhi, T.K., Panigrahi, B.K., Bhatia, M., Anand, S.: Detection of ictal patterns in electroencephalogram signals using 3D phase trajectories. In: IEEE India Conference, pp. 1–6 (2015)Google Scholar
  26. 26.
    Ye, X., Tian, T., Xu, T., Wang, J.: Analysis of beta wave epileptic EEG signals based on symbolic transfer entropy. In: International Conference on Artificial Intelligence and Industrial Engineering (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology PatnaPatnaIndia

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