A Kalman filter based methodology for EEG spike enhancement

dc.contributor.authorOikonomou, V. P.en
dc.contributor.authorTzallas, A. T.en
dc.contributor.authorFotiadis, D. I.en
dc.date.accessioned2015-11-24T17:32:28Z
dc.date.available2015-11-24T17:32:28Z
dc.identifier.issn0169-2607-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/13736
dc.rightsDefault Licence-
dc.subjecteegen
dc.subjectepilepsyen
dc.subjectspike enhancementen
dc.subjectnon-stationary signalen
dc.subjectkalman filteren
dc.subjectartificial neural-networksen
dc.subjectepileptiform activityen
dc.subjectautomatic recognitionen
dc.subjectinterictal spikesen
dc.subjectraw eegen
dc.subjectsystemen
dc.subjectdischargesen
dc.subjectalgorithmsen
dc.subjectmodelsen
dc.titleA Kalman filter based methodology for EEG spike enhancementen
heal.abstractIn this work, we present a methodology for spike enhancement in electroencephalographic (EEG) recordings. Our approach takes advantage of the non-stationarity nature of the EEG signal using a time-varying autoregressive model. The time-varying coefficients of autoregressive model are estimated using the Kalman filter. The results show considerable improvement in signal-to-noise ratio and significant reduction of the number of false positives. (c) 2006 Elsevier Ireland Ltd. All rights reserved.en
heal.accesscampus-
heal.fullTextAvailabilityTRUE-
heal.identifier.primaryDOI 10.1016/j.cmpb.2006.10.003-
heal.identifier.secondary<Go to ISI>://000244167100002-
heal.identifier.secondaryhttp://ac.els-cdn.com/S0169260706002422/1-s2.0-S0169260706002422-main.pdf?_tid=614e6609e4af2932248f3378f4d2f698&acdnat=1339758291_68653f35983137452030915329a4eb32-
heal.journalNameComput Methods Programs Biomeden
heal.journalTypepeer reviewed-
heal.languageen-
heal.publicationDate2007-
heal.publisherElsevieren
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.typejournalArticle-
heal.type.elΆρθρο Περιοδικούel
heal.type.enJournal articleen

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