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An Efficient Classification of EEG Signals for Epilepsy based on Discrete Wavelet Transform and Approximate Entropy using Constrained Neyman-Pearson Criteria

S.A. Chavan, M.A. Potey


EEG (Electroencephalogram) signal is summation of electrical activities that stores information about states of human brain. In order to improve the performance of EEG, de-noising is required which is done by using Discrete Wavelet Transform (DWT). In this paper, a new technique is presented for detecting epileptic seizures from 16-channel EEG data recorded from two groups of patients: normal and epileptic. This technique is based on approximate entropy (ApEn) and DWT analysis of EEG signals. Seizure detection is accomplished in two stages. In the first stage, EEG signals on each channel are decomposed into four levels using multi-resolution wavelet analysis to get approximate and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are computed. For separating two groups, student t-test statistical analysis is used which estimates confidence interval (CI) and segregation probability (p-value). Past work in the EEG classification area has been done using ApEn with single threshold Neyman-Pearson criteria, but through experimental analysis here we have used Upper and Lower threshold values for the efficient EEG classification based on Constrained Neyman-Pearson criteria. Signals with ApEn less than the threshold are classified as epileptic EEG and signals with ApEn greater than or equal to the threshold are classified as normal EEG. Experiments show that best detection rate is obtained at D2 sub-band, and it assures a higher detection rate with a lower false detection rate.  



Electroencephalogram (EEG), Discrete Wavelet Transform (DWT), Approximate Entropy (ApEn), Student T-Test, Noise Rejection, Neyman-Pearson Criteria.

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Jeremy Thomas Neyhart, “Automated Segmentation of radiodense tissue in digitized mammograms using a constrained Neyman-Pearson classifier,” M.S. thesis, Dept. of Electrical and Computer Engineering, Rowan University Glassboro New Jersey, 2002.

J. T. Neyhart, R. E. Eckert, R. Polikar, S. Mandayam, M. Tseng, “A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms,” Proceedings of the Second Joint EMBS/BMES Conference Houston, TX, USA, pp. 23-26, Oct 2002.

Chunmei Wang, Junzhong Zou, Jian Zhang, and Zhisuo Zhang, “Automatic detection of epileptic sharp-slow by wavelet and approximate entropy,” IEEE International Conference on Information and Automation China, pp. 22-25, June 2009.

Hasan Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Elsevier Journal of Expert Systems with Applications, vol. 36, pp. 2027-2036, 2009.

Mallat S. G, “Theory for multi-resolution signal decomposition: The wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674-693, 1989.

Pincus S. M, “Assessing serial irregularity and its implications for health,” Ann N Y Acad Sci, vol. 954, pp. 245-267, 2001.

Pincus S. M, “Approximate entropy as a measure of system complexity,” Proceedings National Academy Sciences USA, vol. 88, pp. 2297-2301, 1991.

Hamed Vavadi, Ahmad Ayatollahi, Ahmad Mirzaei, “A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands,” J. Biomedical Science and Engineering, vol. 3, pp. 1182-1189, Dec 2010.

Lanlan Yu, “EEG de-noising based on wavelet transformation,” School of Electric, Shandong University of Technology Zibo China, IEEE, 2009.


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