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

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Dattaprasad Torse, Veena Desai, Rajashri Khanai

Index Terms

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


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


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