1. Repository of OAI
  2. ΑΠΟΘΕΤΗΡΙΟ "ΟΛΥΜΠΙΑΣ"
  3. Σχολή Θετικών Επιστημών
  4. Τμήμα Μηχανικών Επιστήμης Υλικών
  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)
Ελληνικά   English  
Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/13850
Full metadata record
DC FieldValueLanguage
dc.contributor.authorExarchos, T. P.en
dc.contributor.authorTzallas, A. T.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorKonitsiotis, S.en
dc.contributor.authorGiannopoulos, S.en
dc.date.accessioned2015-11-24T17:33:21Z-
dc.date.available2015-11-24T17:33:21Z-
dc.identifier.issn1089-7771-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/13850-
dc.rightsDefault Licence-
dc.subjectassociation rulesen
dc.subjectclusteringen
dc.subjectelectroencephalographic (eeg)en
dc.subjectepilepsyen
dc.subjectspike detectionen
dc.subjecttransient eventsen
dc.subjectartificial neural-networken
dc.subjectepileptiform dischargesen
dc.subjectinterictal spikesen
dc.subjectwave-formen
dc.subjectselectionen
dc.subjectsystemen
dc.titleEEG transient event detection and classification using association rulesen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1109/Titb.2006.872067-
heal.identifier.secondary<Go to ISI>://000239033000004-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.publicationDate2006-
heal.abstractIn this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.en
heal.journalNameIeee Transactions on Information Technology in Biomedicineen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

Show simple item record
Files in This Item:
File Description SizeFormat 
Fotiadis-2006-EEG Transient event.pdf245.78 kBAdobe PDFView/Open    Request a copy
Show simple item record  


This item is licensed under a Creative Commons License Creative Commons