A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands
Hamed Vavadi, Ahmad Ayatollahi, Ahmad Mirzaei
DOI: 10.4236/jbise.2010.312154   PDF    HTML     9,736 Downloads   19,655 Views   Citations


Epilepsy is a common brain disorder that about 1% of world's population suffers from this disorder. EEG signal is summation of brain electrical activities and has a lot of information about brain states and also used in several epilepsy detection methods. In this study, a wavelet-approximate entropy method is ap-plied for epilepsy detection from EEG signal. First wavelet analysis is applied for decomposing the EEG signal to delta, theta, alpha, beta and gamma sub- ands. Then approximate entropy that is a chaotic measure and can be used in estimation complexity of time series applied to EEG and its sub-bands. We used this method for separating 5 group EEG signals (healthy with opened eye, healthy with closed eye, interictal in none focal zone, interictal in focal zone and seizure onset signals). For evaluating separation ability of this method we used t-student statistical analysis. For all pair of groups we have 99.99% separation probability in at least 2 bands of these 6 bands (EEG and its 5 sub-bands). In comparing some groups we have over 99.98% for EEG and all its sub-bands.

Share and Cite:

Vavadi, H. , Ayatollahi, A. and Mirzaei, A. (2010) A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands. Journal of Biomedical Science and Engineering, 3, 1182-1189. doi: 10.4236/jbise.2010.312154.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Subasi, A. and Ercelebi, E. (2005) Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78, 87-99.
[2] Sakkalis, V. and Doru Giurc?aneanu, C. (2009) Assessment of linear and nonlinear synchronization measures for analyzing EEG in a mild epileptic paradigm. IEEE Transaction on Information Technology in Biomedicine, 13, 433-441.
[3] Adeli, H., Ghosh-Dastidar, S. and Dadmehr, N. (2007) A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54, 205-211.
[4] Firpi, H., Goodman, E.D. and Echauz, J. (2007) Epileptic seizure detection using genetically programmed artificial features. IEEE Transactions on Biomedical Engineering, 54, 212-224.
[5] Babloyantz, A. and Destexhe, A. (1986) Low-dimensional chaos in an instance of epilepsy. Proceedings of the National Academy of Science, USA, 83, 3513-3517.
[6] Lai, Y.-C., Osorio, I., Harrison, M.A.F. and Frei M.G. (2002) Correlation-dimension and autocorrelation fluc-tuations in epileptic seizure dynamics. Journal of Ameri-can Physical Society, 65, 1-5.
[7] Natarajan, K., Acharya, R., Alias, F., Tiboleng, T. and Puthusserypady, S.K. (2004) Nonlinear analysis of EEG signals at different mental states. BioMedical Engineer-ing, 3, 1-11.
[8] Ghosh-Dastidar, S., Adeli, H. and Dadmehr, N. (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans-actions on Biomedical Engineering, 54, 1545-1551.
[9] Bibian, S., ZlKov, T., Dumont, G.A. Ries, C.R. Puil, E., Ahmad, H., Huzmeza’N, M. and Macleod, B.A. (2001) Estimation the Anesthetic depth using wavelet analysis of electroencephalogeram. Proceedings of the 23rd Annual EMBS International Conference. Istanbul, Turkey, Octo-ber 2001, 951-955.
[10] Kumar, S.P., Sriraam, N. and Benakop, P.G. (2008) Auto-mated detection of epileptic seizures using wavelet en-tropy feature with recurrent neural network classifier. IEEE Region 10 Conference, Heyderabad, 1-5.
[11] Kannathalab, N., Choob, M.L., Acharyab, U.R. and Sa-dasivana, P.K. (2005) Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedi-cine, 80, 187-194.
[12] Bruhn, J., Lehmann, L.E., Ropcke, H., Bouillon T.W. and Hoeft A. (2001) Shannon entropy applied to the meas-urement of the lector encephalographic effects of desflu-rane. American Society of Anesthesiologists Jurnal, 95, 30-35.
[13] Cao, Y.H., Tung, W.W., Gao, J.B., Protopopescu, V.A. and Hively, L.M. (2004) Detecting dynamical changes in time series using the permutation entropy. Journal of American Physical Society, 70, 1-7.
[14] Vukkadala, S., Vijayalakshmi, S. and Vijayapriya, S. (2009) Automated detection of epileptic EEG using ap-proximate entropy in elman networks. International Journal of Recent Trends in Engineering, 1, 307-312.
[15] Filho, A.P., Cukiert, A. and Diambra, L. (2006) Peri-ictal complexity loss as determined by approximate entropy analysis in the electrocorticogram obtained from chronic subdural recordings in patients with refractory temporal lobe epilepsy. Journal of Epilepsy and Clinical Neuro-physiology, 12, 191-199.
[16] Ocak, H. (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approxi-mate entropy. Elsevier Journal of Expert Systems with Applications, 36, 2027-2036.
[17] Pincus, S.M. (1991) Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Science (USA), 88, 2297-2301.
[18] Wang, L., Xu, G.Z., Wang, J. Yang, S. and Yan, W.L. (2007) Feature extraction of mental task in BCI based on the method of approximate entropy. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, August 2007, 1941-1944.
[19] Wang, Y.R., Wang, W., Liu, Y.L., Wang, D., Liu, B.W. Shi, Y.J. and Gao, P. (2009) Feature Extracting of Weak Signal in Real-Time Sleeping EEG with Approximate Entropy and Bispectrum Analysis. ICBBE 2009. 3rd In-ternational Conference on Bioinformatics and Biomedi-cal Engineering, Beijing, June 2009, 1-4.
[20] Srinivasan, V., Eswaran, C. and Sriraam N. (2007) Ap-proximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Transaction on Informa-tion Technology in Biomedicine, 11, 288-295.
[21] Abásolo, D., James, C.J. and Hornero, R. (2007) Non- linear analysis of intracranial electroencephalogram re-cordings with approximate entropy and lempel-ziv com-plexity for epileptic seizure detection. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France, Aug 2007, 1953-1956.
[22] Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P. and Elger, C.E. (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on re-cording region and brain state. Journal of American Physical Society, 64, 1-8.
[23] Hosseini, P.T., Shalbaf, R. and Nasrabadi, A.M. (2010) Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis. Journal of Biomedical Science and Engineering, 3, 253-261.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.