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

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

Published By: MECS Press

IJIGSP Vol.11, No.11, Nov. 2019

Fast and Accurate Classification F and NF EEG by Using SODP and EWT

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Author(s)

Hesam Akbari, Sedigheh Ghofrani

Index Terms

Focal EEG signal;empirical wavelet transform (EWT);second order difference plot (SODP);central tendency measure (CTM);support vector machine (SVM);K nearest neighbor (KNN)

Abstract

Removing the brain part, as the epilepsy source attack, is a surgery solution for those patients who have drug resistant epilepsy. So, the epilepsy localization area is an essential step before brain surgery. The Electroencephalogram (EEG) signals of these areas are different and called as focal (F) whereas the EEG signals of other normal areas are known as non-focal (NF). Visual inspection of multi-channels for F EEG detection is time-consuming along with human error. In this paper, an automatic and adaptive method is proposed based on second order difference plot (SODP) of EEG rhythms in empirical wavelet transform (EWT) domain as an adaptive signal decomposition. SODP provides the data variability rate or gives a 2D projection for rhythms. The feature vector is obtained using the central tendency measure (CTM). Finally, significant features, chosen by Kruskal–Wallis statistical test, are fed to K nearest neighbor (KNN) and support vector machine (SVM) classifiers. The achieved results of the proposed method in terms of three objective criteria are compared with state-of-the-art papers demonstrating an outstanding algorithm here in.

Cite This Paper

Hesam Akbari, Sedigheh Ghofrani, " Fast and Accurate Classification F and NF EEG by Using SODP and EWT", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.11, pp. 29-35, 2019.DOI: 10.5815/ijigsp.2019.11.04

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