Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/18813
Title: Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy
Authors: Sakkalis, Vangelis
Cassar, Tracey A.
Zervakis, Michalis
Camilleri, Kenneth P.
Fabri, Simon G.
Bigan, Cristin
Karakonstantaki, Eleni
Micheloyannis, Sifis
Keywords: Electroencephalography
Electroencephalography -- Data processing
Epilepsy in children
Issue Date: 2008
Publisher: Hindawi Publishing Corporation
Citation: Sakkalis, V., Cassar, T., Zervakis, M., Camilleri, K. P., Fabri, S. G., Bigan, C., ... & Micheloyannis, S. (2008). Parametric and nonparametric EEG analysis for the evaluation of EEG activity in young children with controlled epilepsy. Computational Intelligence and Neuroscience, 2008, 1.
Abstract: There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform) and a parametric, signal modeling technique (ARMA) are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest) task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.
Description: This work was supported in part by the EC-IST project Biopattern, Contract no. 508803, and by the internal research grant of the University of Malta LBA-73-967.
URI: https://www.um.edu.mt/library/oar//handle/123456789/18813
Appears in Collections:Scholarly Works - FacEngSCE



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