Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/91658
Title: EEG feature extraction and selection methods using wavelets and ARMA model for classifying mild epileptic signal patterns
Authors: Sakkalis, V.
Zervakis, M.
Bigan, C.
Camilleri, Tracey A.
Camilleri, Kenneth P.
Fabri, Simon G.
Micheloyannis, S.
Keywords: Electrooculography
Electroencephalography -- Data processing
Computer simulation
Signal processing
Epilepsy
Issue Date: 2006
Publisher: Neoventor Medicinsk Innovation AB
Citation: Sakkalis, V., Zervakis, M., Bigan, C., Cassar, T., Camilleri, K. P., Fabri, S. G., & Micheloyannis, S. (2006). EEG feature extraction and selection methods using wavelets and ARMA model for classifying mild epileptic signal patterns. BIOPATTERN Brain, 21.
Abstract: Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood and evaluation as well as treatment of these children is of importance. Most of the cases in childhood are not accommodated by brain damage and many drugs produce no brain dysfunction. Thus we decided to examine children with seizures and compare them with controls to evaluate cognitive function in mild epilepsy cases i.e. children with epileptic seizures, without brain damage and under drug control.
URI: https://www.um.edu.mt/library/oar/handle/123456789/91658
Appears in Collections:Scholarly Works - FacEngESE



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