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Cluster Computing

, Volume 22, Supplement 6, pp 13521–13531 | Cite as

Epileptic seizures classification in EEG signal based on semantic features and variational mode decomposition

  • M. Ravi KumarEmail author
  • Y. Srinivasa Rao
Article
  • 143 Downloads

Abstract

Electroencephalogram (EEG) is the most important monitoring methodology for the detection of epileptic seizure diseases. In this paper, EEG based epileptic seizure detection is assessed by employing Bern-Barcelona EEG and Bonn University EEG database. The proposed technique contains three major steps: decomposition, feature extraction and classification. Initially, decomposition using variational mode decomposition delivers an effective frequency localization. After decomposition, semantic feature extraction is carried-out by employing differential entropy and peak-magnitude of root mean square ratio for achieving optimal feature subsets and also for the rejection of irrelevant and redundant features. After finding the feature information, a superior classifier named as random forest is employed for classifying the normality and abnormality of seizure. The experimental result shows that the proposed approach distinguishes the normality and abnormality of seizure EEG signals in terms of sensitivity, specificity, accuracy, positive predictive value and negative predictive value with a superior recognition accuracy.

Keywords

Differential entropy Peak-magnitude to root mean square ratio Random forest Variational mode decomposition 

References

  1. 1.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non-focal EEG using entropies. Pattern Recogn. Lett. 94, 112–117 (2017)CrossRefGoogle Scholar
  2. 2.
    Biju, K.S., Hakkim, H.A., Jibukumar, M.G.: Ictal EEG classification based on amplitude and frequency contours of IMFs. Biocybern. Biomed. Eng. 37(1), 172–183 (2017)CrossRefGoogle Scholar
  3. 3.
    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(3), 1106–1117 (2015)CrossRefGoogle Scholar
  4. 4.
    Temko, A., Nadeu, C., Marnane, W., Boylan, G., Lightbody, G.: EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures. IEEE Trans. Inf. Technol. Biomed. 15(6), 839–847 (2011)CrossRefGoogle Scholar
  5. 5.
    Li, S., Zhou, W., Yuan, Q., Liu, Y.: Seizure prediction using spike rate of intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 21(6), 880–886 (2013)CrossRefGoogle Scholar
  6. 6.
    Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703–710 (2009)CrossRefGoogle Scholar
  7. 7.
    Wang, N., Lyu, M.R.: Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction. IEEE J. Biomed. Health Inf. 19(5), 1648–1659 (2015)CrossRefGoogle Scholar
  8. 8.
    Parvez, M.Z., Paul, M.: Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 158–168 (2016)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Markert, R.: Filter bank property of variational mode decomposition and its applications. Signal Process. 120, 509–521 (2016)CrossRefGoogle Scholar
  10. 10.
    Guo, L., Rivero, D., Dorado, J., Munteanu, C.R., Pazos, A.: Automatic feature extraction using genetic programming: an application to epileptic EEG classification. Expert Syst. Appl. 38(8), 10425–10436 (2011)CrossRefGoogle Scholar
  11. 11.
    Sharif, B., Jafari, A.H.: Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincaré plane. Comput. Methods Progr. Biomed. 145, 11–22 (2017)CrossRefGoogle Scholar
  12. 12.
    Chu, H., Chung, C.K., Jeong, W., Cho, K.H.: Predicting epileptic seizures from scalp EEG based on attractor state analysis. Comput. Methods Progr. Biomed. 143, 75–87 (2017)CrossRefGoogle Scholar
  13. 13.
    Hassan, A.R., Subasi, A.: Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput. Methods Progr. Biomed. 136, 65–77 (2016)CrossRefGoogle Scholar
  14. 14.
    Tawfik, N.S., Youssef, S.M., Kholief, M.: A hybrid automated detection of epileptic seizures in EEG records. Comput. Electr. Eng. 53, 177–190 (2016)CrossRefGoogle Scholar
  15. 15.
    Dhiman, R., Saini, J.S.: Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures. Appl. Soft Comput. 51, 116–129 (2017)CrossRefGoogle Scholar
  16. 16.
    Ahammad, N., Fathima, T., Joseph, P.: Detection of epileptic seizure event and onset using EEG. Biomed. Res. Int. (2014).  https://doi.org/10.1155/2014/450573 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics & Communication EngineeringSir C R Reddy College of EngineeringEluruIndia
  2. 2.Department of Instrument TechnologyAndhra University College of EngineeringVisakhapatnamIndia

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