Statistical analysis of Epileptic activities based on Histogram and Wavelet-Spectral entropy
Ahmad Mirzaei, Ahmad Ayatollahi, Hamed Vavadi
DOI: 10.4236/jbise.2011.43029   PDF    HTML     7,124 Downloads   12,584 Views   Citations


Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analysed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub-bands. Applying histogram and Spectral entropy approaches to the EEG sub-bands, normal and abnormal states of brain can be distinguished with more than 99% probability.

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Mirzaei, A. , Ayatollahi, A. and Vavadi, H. (2011) Statistical analysis of Epileptic activities based on Histogram and Wavelet-Spectral entropy. Journal of Biomedical Science and Engineering, 4, 207-213. doi: 10.4236/jbise.2011.43029.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Bethesda, M.D. (2004) Seizures and Epilepsy: Hope through Research National Institute of Neurological Disorders and Stroke (NINDS). Internet Available: htm
[2] Duke, D., and Pritchard, W. (1991) Measuring Chaos in the Human Brain. World Scientific, Singapore.
[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 Transaction on Biomedical Engineering, 54, 205-211. doi:10.1109/TBME.2006.886855
[4] Gao, W. and Li B.L. (1993) Wavelet analysis of coherent structures at the atmosphere-forest interface. J. Appl. Meteorology, 32, 1717-1725. doi:10.1175/1520-0450(1993)032<1717:WAOCSA>2.0.CO;2
[5] Meyer, Y. (1993) Wavelets: Algorithms and Applications. SIAM, Philadelphia, PA.
[6] Iasemidis L.D. and Sackellares J.C. (1991) The temporal evolution of the largest Lyapunov exponent on the human epileptic cortex. World Scientific, 49-82.
[7] Iasemidis, L.D., Shiau, D.S., Sackellares, J.C., and Pardalos, P.M. (2000) Transition to epileptic seizures: Optimization. In: DIMACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, Providence, RI, 55, 55-74.
[8] Iasemidis, L.D., Shiau, D.S., Chaovalitwongse, W., Sackellares, J.C., Pardalos, P.M., Principe, J.C., Carney, P.R., Prasad, A., Veeramini, B., and Tsakalis, K. (2003) Adaptive epileptic seizure prediction system. IEEE Transaction on Biomedical Engineering, 50 , 616-627. doi:10.1109/TBME.2003.810689
[9] Elger, C.E. and Lehnertz, K. (1994) Ictogenesis and chaos. In: Wolf, P., Ed., Epileptic Seizures and Syndromes. Libbey, London, U.K., 547-552.
[10] Elger, C.E. and Lehnertz, K. (1998) Seizure prediction by non-linear time series analysis of brain electrical activity. Eur. J. Neurosci., 10, 786-789. doi:10.1046/j.1460-9568.1998.00090.x
[11] Tayaranian, P., Shalbaf, R., and Nasrabadi, A.M. (2010) Extracting a seizure intensity index from one-channel EEG signal using bispectral and detrended fluctuation analysis. J. Biomedical Science and Engineering, 3, 253-261. doi:10.4236/jbise.2010.33034
[12] Kumar, S.P., Sriraam, N. and Benakop, P.G. (2008) Automated detection of epileptic seizures using wavelet entropy feature with recurrent neural network classifier. TENCON IEEE, 1-5. doi:10.1109/CICSyN.2010.84
[13] Mirzaei, A., Ayatollahi, A., Gifani, P., and Salehi, L. (2010) Spectral Entropy for Epileptic Seizures Detection. Second International Conference on Computational Intelligence, Communication Systems and Networks, Liverpool.
[14] Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., and Elger, C.E. (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64, 061907. doi:10.1103/PhysRevE.64.061907
[15] Shannon, C.E. (1948) A mathematical theory of communication. Bell Syst. Tech. J., 27, 379-423.
[16] Qian, S., and Chen, D. Joint time – frequency analysis methods and applications. Prentice – Hall, Chapter 2, 21-22.
[17] Mendenhall, W., Beaver, R.J., and Beaver B.M. Introduction to probability and statistics. 12th edition. Chapter 10, 404-406.

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