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Window size analysis in ARMA-modeled spectral biomarkers for epileptic children

Μιχελογιάννης Σήφης, Zervakis Michail, Cassar Tracey, Camilleri Kenneth P. , Fabri Simon G.

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URIhttp://purl.tuc.gr/dl/dias/78885245-09B9-49D0-B307-D8DBC5783072-
Identifierhttps://dl.acm.org/doi/10.5555/1713360.1713373-
Languageen-
Extent6 pagesen
TitleWindow size analysis in ARMA-modeled spectral biomarkers for epileptic childrenen
CreatorΜιχελογιάννης Σήφηςel
CreatorMicheloyannis Sifisen
CreatorMichelogiannis Sifisen
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
CreatorCassar Traceyen
CreatorCamilleri Kenneth P. en
CreatorFabri Simon G. en
PublisherACTA Pressen
Content SummaryAutoregressive Moving Average (ARMA) models are suitable for modeling processes whose frequency spectrum exhibits both sharp peaks and deep nulls. In this analysis an ARMA model was used to model EEG data and estimate its time-frequency spectrum. Spectral features were then extracted and their suitability as biomarkers for epileptic children whose EEG is clinically diagnosed as normal is analyzed. Furthermore, variations in classification are investigated when features are extracted from different window sizes. Results show that through windowing, a maximum classification score of 97.9% is achieved with an improvement of up to 18.3% over the non-windowed case.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-26-
Date of Publication2008-
SubjectAutoregressive Moving Average (ARMA)en
SubjectEEGen
SubjectElectroencephalographyen
Bibliographic CitationT. A. Cassar, K. P. Camilleri, S. G. Fabri, M. Zervakis and S. Micheloyannis,"Window size analysis in ARMA-modeled spectral biomarkers for epileptic children,"in Sixth IASTED International Conference on Biomedical Engineering, 2008, pp. 58-63.en

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