Seizure Detection in Clinical EEG Based on Entropies and EMD

  • Qingfang Meng
  • Shanshan Chen
  • Weidong Zhou
  • Xinghai Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7952)


Considering the EEG signals are nonlinear and nonstationary, the nonlinear dynamical methods have been widely applied to analyze the EEG signals. Directly extracted the approximate entropy and sample entropy as features are efficient methods to analysis the EEG signals of epileptic parents. To detect the epilepsy seizure signals from epileptic EEG, choose an appropriate threshold value as the discrimination criteria is simplest. The experiment indicated the approximate entropy provide a higher accuracy in distinguishing the epileptic seizure signals from the EEG than sample entropy. To improve the accuracy of sample entropy, empirical mode decomposition (EMD) is used to decompose EEG into multiple frequency subbands, and then calculate sample entropy for each component. The results show that the accuracy is up to 91%, which could be used to discriminate epileptic seizure signals from epileptic EEG.


epileptic EEG approximate entropy sample entropy empirical mode decomposition (EMD) 


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  1. 1.
    Wang, Y., Zhou, W.D., Li, S.F., Yuan, Q., Geng, S.J.: Fractal Intercept Analysis of EEG and Application for Seizure Detection. Chinese Journal of Biomedical Engineering 30, 562–566 (2011)Google Scholar
  2. 2.
    Kai, C.H., Sung, N.Y.: Detection of Seizures in EEG using Subband Nonlinear Parameters and Genetic Algorithm. Computers in Biology and Medicine 40, 823–830 (2010)CrossRefGoogle Scholar
  3. 3.
    Kuhlmann, L., Burkitt, A.N., Cook, M.J., Fuller, K., Grayden, D.B., Seiderer, L., Maieels, I.M.: Seizure Detection Using Seizure Probability Estimation: Comparison of Features Used to Detect Seizures. Ann. Biomed. Eng. 37, 2129–2145 (2009)CrossRefGoogle Scholar
  4. 4.
    Ocak, H.: Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications 36, 2027–2036 (2009)CrossRefGoogle Scholar
  5. 5.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88, 2297–2301 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Kannatha, N., Choob, M.L., Acharya, U.R., Raudys, P.K.: Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 80, 187–194 (2005)CrossRefGoogle Scholar
  7. 7.
    Sabeti, M., Katebi, S., Boostani, R.: Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artificial Intelligence in Medicine 47, 263–274 (2009)CrossRefGoogle Scholar
  8. 8.
    Achary, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomedical Signal Processing and Control 7, 401–408 (2012)CrossRefGoogle Scholar
  9. 9.
    Zhuang, J.J., Ning, X.B., Du, S.D., Wang, Z.Z., Huo, C.Y., Yang, X., Fan, A.H.: Short-term nonlinear heart rate variability forecast of ventricular tachycardia. Chinese Science Bulletin 53, 520–527 (2008)CrossRefGoogle Scholar
  10. 10.
    Huang, X.L., Cui, S.Z., Ning, X.B., Bian, C.H.: Multiscale Base-scale Entropy Analysis of Heart Rate Variability. Phys. Sin. 58, 8160–8165 (2009)Google Scholar
  11. 11.
    Cullen, J., Saleem, A., Swindell, R., Burt, P., Moore, C.: Measurement of cardiac synchrony using Approximate Entropy applied to nuclear medicine scans. Biomedical Signal Processing and Control 5, 32–36 (2010)CrossRefGoogle Scholar
  12. 12.
    Sabeti, M., Katebi, S., Boostani, R.: Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artificial Intelligence in Medicine 47, 263–274 (2009)CrossRefGoogle Scholar
  13. 13.
    Pan, Y.H., Wang, Y.H., Liang, S.F., Lee, K.T.: Fast computation of sample entropy and approximate entropy in biomedicine. Comput. Methods Programs Biomed. 104, 382–396 (2011)CrossRefGoogle Scholar
  14. 14.
    Huang, N., Shen, Z., Long, S.R., Wu, M.C., Shin, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis. Proc. R. Soc. London A 454, 903–995 (1998)zbMATHCrossRefGoogle Scholar
  15. 15.
    Li, S.F., Zhou, W.D., Cai, D.M., Liu, K., Zhao, J.L.: EEG Signal Classification Based on EMD and SVM. Journal of Biomedical Engineering 28, 891–894 (2011)Google Scholar
  16. 16.
    Pachori, R.B., Bajaj, V.: Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Computer Methods and Programs in Biomedicine 104, 373–381 (2011)CrossRefGoogle Scholar
  17. 17.
    Tafreshi, A.K., Nasrabadi, A.M., Omidvarnia, A.H.: Epileptic Seizure Detection Using Empirical Mode Decomposition. In: 8th IEEE International Symposium on Signal Processing and Information Technology, Sarajevo, Bosnia, pp. 238–242 (2008)Google Scholar
  18. 18.
    Braun, S., Feldman, M.: Decomposition of nonstationary signals into varying time scales: Some aspects of the EMD and HVD methods. Mechanical Systems and Signal Processing 25, 2608–2630 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qingfang Meng
    • 1
    • 2
  • Shanshan Chen
    • 1
    • 2
  • Weidong Zhou
    • 3
  • Xinghai Yang
    • 1
    • 2
  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key laboratory of Network Based Intelligent ComputingJinanChina
  3. 3.School of Information Science and EngineeringShandong UniversityJinanChina

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