Statistical Wavelet Features, PCA, and SVM Based Approach for EEG Signals Classification

Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

Abstract:

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.

Keywords: Discrete Wavelet Transform, Electroencephalogram, Pattern Recognition, Principal Component Analysis, Support Vector Machine.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099414

References:

[1] SaeidSanei and J.A. Chambers.EEG Signal Processing. John Wiley & Sons, 2007.
[2] A. Massimo, “In Memoriam Pierre Gloor (1923–2003): an appreciation”.Epilepsia, vol.-45(7), July 2004, page-882.
[3] M. A. B. Brazier. “A History of the Electrical Activity of the Brain”.The First Half-Century, Macmillan,New York, 1961.
[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.
[5] B. P. Marchant. “Time–frequency analysis for biosystem engineering”.Biosystems Engineering-2003. vol.-85 (3), pp.-261–281.
[6] A. Subasi. “EEG signal classification using wavelet feature extraction and a mixture of expert model”. ExpertSystems with Applications-2007, vol-32, pp.-1084–1093.
[7] A. Subasi, and M. Ismail Gursoy. “EEG signal classification using PCA, ICA, LDA and support vector machines”.ExpertSystemswithApplications-2010. vol. 37,pp.-8659–8666.
[8] A. Subasi. “Epileptic seizure detection using dynamic wavelet network”.Expert Systems with Applications-2005. vol.-29, pp.-343–355.
[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.
[10] R.G. Andrzejak, K. Lehnertz, F. Mormann, et al. “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state”. Phys. Rev. Ed- 64 (6)-061907.2001.
[11] H. Adeli, Z. Zhou, and N. Dadmehr. “Analysis of EEG records in an epileptic patient using wavelet transform”. 2003. Journal of Neuroscience Methods, vol.-123, pp.-69–87.
[12] R. O. Duda, P. E. Hart, and D.G. Stork. Pattern Classification.2nd Ed., John Wiley & Sons, 2001.
[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.