INFORMATION CHANGE THE WORLD

International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.10, No.1, Jan. 2018

Classification of EEG Signals in a Seizure Detection System Using Dual Tree Complex Wavelet Transform and Least Squares Support Vector Machine

Full Text (PDF, 863KB), PP.56-64


Views:110   Downloads:8

Author(s)

Dattaprasad Torse, Veena Desai, Rajashri Khanai

Index Terms

Dual Tree Complex Wavelet Transform;improved multiscale Permutation Entropy;Least Squares Support Vector Machine

Abstract

Epilepsy is a chronic brain disorder which affects normal neuronal activity of the brain. It results in sudden repeated episodes of higher electrical activity due to sensory disturbance. Electroencephalogram (EEG) plays an important role in the diagnosis of epilepsy. Currently, manual observation of EEG is done by experienced neurologist to diagnose epilepsy and related disorders. However, automated system is a promising method for seizure detection and diagnosis. The EEG signals recorded from the patient’s scalp are preprocessed, and classified as seizure and non-seizure based on the extracted signal features. The procedure significantly eliminates the error involved in manual observation. Due to non-linear nature of EEG, joint time-frequency methods are used to analyse the EEG signals. This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT. The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT. The performance of the Least Squares Support Vector Machine (LS-SVM) classifier is tested using these features and highest classification accuracy of  99.87 % is obtained on the real time EEG database.  

Cite This Paper

Dattaprasad Torse, Veena Desai, Rajashri Khanai," Classification of EEG Signals in a Seizure Detection System Using Dual Tree Complex Wavelet Transform and Least Squares Support Vector Machine", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.1, pp. 56-64, 2018.DOI: 10.5815/ijigsp.2018.01.07

Reference

[1]Gooch, Clifton L., Etienne Pracht, and Amy R. Borenstein, “The Burden of Neurological Disease in the United States: A Summary Report and Call to Action”, Annals of Neurology, 2017.

[2]S. A. Hosseini, M-R. Akbarzadeh-T, M-B. Naghibi-Sistani, “Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition”, International Journal of Intelligent Systems and Applications (IJISA), vol.5,no.6,pp.41-46,2013.DOI: 10.5815/ijisa.2013.06.05

[3]Vaughan, T. M. , Heetderks, W. J. , Trejo, L. J. , Rymer, W.Z., Weinrich, M., Moore, M.M., Kubler, A., Dobkin, B. H., Birbaumer, N., Donchin, E., Wolpaw, E. W. and Wolpaw, J. R., “Brain-computer interface technology: a review the second international meeting”, in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol.11, no. 2, pp. 94-109, 2003.

[4]Al-Fahoum, Amjed S., and Ausilah A. Al-Fraihat. “Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains”, ISRN neuroscience, pp. 1-7,  2014. 

[5]Faust, Oliver, et al., “Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis”, Seizure 26, 2015, pp. 56-64.

[6]Tzallas, Alexandros T., Markos G. Tsipouras, and Dimitrios I. Fotiadis, “Epileptic seizure detection in EEGs using time–frequency analysis”, IEEE transactions on information technology in biomedicine 13.5, 2009, pp. 703-710.

[7]Kumar, Yatindra, M. L. Dewal, and R. S. Anand., “Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network”, Signal, Image and Video Processing, 2014, pp.1-12. 

[8]Torse, Dattaprasad A., Veena Desai, and Rajashri Khanai, “Application of Intrinsic Mode Function Based Features and Artificial Neural Network for the Classification of Normal and Epileptic EEG Signals”, International Journal of Engineering vol.10 no.1,  2017.

[9]Goshvarpour, Ateke, Hossein Ebrahimnezhad, and Atefeh Goshvarpour. “Classification of epileptic EEG signals using time-delay neural networks and probabilistic neural networks”, International Journal of Information Engineering and Electronic Business vol.5 no.1 (2013), pp. 59-67.

[10]Wang, Deng, Duoqian Miao, and Chen Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection”, Expert Systems with Applications 38.11, 2011, pp. 14314-14320.

[11]Selesnick, Ivan W., Richard G. Baraniuk, and Nick C. Kingsbury, “The dual-tree complex wavelet transform”, IEEE signal processing magazine 22.6, 2005, pp.123-151.

[12]Suykens, Johan AK, Tony Van Gestel, and Jos De Brabanter. Least squares support vector machines. World Scientific, 2002.

[13]Singla, Rajesh, and B. A. Haseena, “Comparison of ssvep signal classification techniques using svm and ann models for bci applications”, International Journal of Information and Electronics Engineering 4.1, 2014, pp 1-6.

[14]Torse, Dattaprasad A., and Veena V. Desai. “Design of adaptive EEG preprocessing algorithm for neurofeedback system”, Communication and Signal Processing (ICCSP), 2016 International Conference on. IEEE, pp. 0392-0395, 2016.

[15]Wang, Shuihua, et al., “Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection”, Applied Sciences 6.6, 169, 2016, pp. 1-18.

[16]Chen, Guangyi, “Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features”, Expert Systems with Applications 41.5, 2014, pp. 2391-2394.

[17]Peker, Musa, Baha Sen, and Dursun Delen, “A novel method for automated diagnosis of epilepsy using complex-valued classifiers”, IEEE journal of biomedical and health informatics 20.1, 2016, pp. 108-118.

[18]Azami, Hamed, and Javier Escudero, “Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings”, Biomedical Signal Processing and Control 23, 2016, pp. 28-41.

[19]Costa, Madalena, Ary L. Goldberger, and C-K. Peng. “Multiscale entropy analysis of complex physiologic time series”, Physical review letters 89.6, 2002, pp. 068-102.

[20]Joshi, Varun, Ram Bilas Pachori, and Antony Vijesh, “Classification of ictal and seizure-free EEG signals using fractional linear prediction”, Biomedical Signal Processing and Control 9, 2014, pp. 1-5.