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Validation of time-frequency and ARMA feature extraction methods in classification of mild epileptic signal patterns

Zervakis Michalis, Μιχελογιάννης Σήφης, Sakkalis, Vangelis, Camilleri Kenneth P., Fabri Simon G., Bigan Cristin, Cassar Tracey

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/AEA3E4D0-C99C-43C4-8D4E-D0ACB0F504AA-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςValidation of time-frequency and ARMA feature extraction methods in classification of mild epileptic signal patternsen
ΔημιουργόςZervakis Michalisen
ΔημιουργόςΖερβακης Μιχαληςel
ΔημιουργόςΜιχελογιάννης Σήφηςel
ΔημιουργόςMichelogiannis Sifisel
ΔημιουργόςMicheloyannis Sifisen
ΔημιουργόςSakkalis, Vangelisen
ΔημιουργόςCamilleri Kenneth P.en
ΔημιουργόςFabri Simon G.en
ΔημιουργόςBigan Cristinen
ΔημιουργόςCassar Traceyen
ΠερίληψηEpilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and many drugs produce no brain dysfunction. In this study cognitive function in mild epilepsy cases is evaluated where children with seizures are compared to controls i.e., children with epileptic seizures, without brain damage and under drug control. Two different cognitive tasks were designed and performed by both the epileptic and healthy children: i) a relatively difficult math task and ii) Fractal observation. Under this context, we propose two frameworks: the first is based on time-frequency analysis using the continuous wavelet transform (WT) and the Compressed Spectral Array (CSA), and the second is based on Auto-Regressive Moving Average (ARMA) modeling to evaluate the EEG signals at rest and during cognitive tasks in both groups. Initially, the analytical capabilities of the proposed feature extraction techniques were assessed in a simulated environment, and finally classification of the actual data was performed. The results suggest that time-frequency analysis methods were able to capture non-stationary activity, whereas ARMA modeling performs better on stationary signals. Classification of the actual data was successful and both approaches reached the same level of accuracy (73.4%). Higher frequency bands (beta and gamma) were apparent on frontal-parietal lobes on both math and fractal tests, while alpha band was diffused across a wider frontal network, only during the math task.en
ΤύποςΣύντομη Δημοσίευση σε Συνέδριοel
ΤύποςConference Short Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-10-25-
Ημερομηνία Δημοσίευσης2006-
Θεματική ΚατηγορίαBasic medical sciencesen
Θεματική ΚατηγορίαBasic sciences, Medicalen
Θεματική ΚατηγορίαBiomedical sciencesen
Θεματική ΚατηγορίαHealth sciencesen
Θεματική ΚατηγορίαPreclinical sciencesen
Θεματική ΚατηγορίαSciences, Medicalen
Θεματική Κατηγορίαmedical sciencesen
Θεματική Κατηγορίαbasic medical sciencesen
Θεματική Κατηγορίαbasic sciences medicalen
Θεματική Κατηγορίαbiomedical sciencesen
Θεματική Κατηγορίαhealth sciencesen
Θεματική Κατηγορίαpreclinical sciencesen
Θεματική Κατηγορίαsciences medicalen
Βιβλιογραφική ΑναφοράV. Sakkalis, M. Zervakis, C. Bigan, T. Cassar, K.P. Camilleri, S.G. Fabri and S. Micheloyannis, "Validation of time-frequency and ARMA feature extraction methods in classification of mild epileptic signal patterns," presented at International Special Topic Conference on Information Technology in Biomedicine, 2006.el

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