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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29381

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Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification
The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the supportvectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.
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[1] SaeidSanei and J.A. Chambers.EEG Signal Processing. John Wiley & Sons, 2007.
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[4] M. D. Alessandro,R.Esteller,G. Vachtsevanos,A. Hinson, A. Echauz, and B.Litt."Epileptic seizure prediction using hybrid featureselection over multiple intracranial EEG electrode contacts: A report of four patients".IEEE Transactionson Biomedical Engineering-2003.vol.-50 (5), pp.-603–615.
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[9] K. Fu, J. Qu, Y. Chai, and Y. Dong, “Classification of seizure based on the time-frequency image of EEGsignals using HHT and SVM”. Biomedical Signal Processing and Control-2014, vol.-13, pp.-15–22.
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[13] S. Theodoridis, and K. Koutroumbas. Pattern Recognition. 4th Ed., Elsevier - Academic Press, 2009.
[14] P. S.Sastry. “An introduction to Support Vector Machines”. Chapter in J.C. Misra (Ed), computing and information sciences: Recent Trends. Narosa Publishing House, New Delhi 2003.
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