Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11299
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dc.contributor.authorTsipouras, M.G.,en
dc.contributor.authorGoletsis, Y.,en
dc.contributor.authorFotiadis, D.I.en
dc.date.accessioned2015-11-24T17:05:19Z-
dc.date.available2015-11-24T17:05:19Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11299-
dc.rightsDefault Licence-
dc.titleA method for arrhythmic episode classification in ECGs using fuzzy logic and Markov modelsen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Οικονομικών και Κοινωνικών Επιστημών. Τμήμα Οικονομικών Επιστημώνel
heal.publicationDate2004-
heal.abstractA merhod for arrhythmic episode classification using only the RR-interval signal is presented. The merhod is based on f u m logic and Markov models, while Classification is performed for nine categories of cardiac rhythms. A two-stage classifier is applied. In the first stage, a fuuy system clussiQies the episode using the mean value and standard deviaiion of the KR-intervals. In the second, the RR-interval signal is transformed to character sequences, which are classified by Markov models. Two representation techniques are used for the extraction of the characrer sequences: symbolic dynamics and one bused on the ER-interval length. The classification of an episode is achieved combining the outcomes of the two stages. The MIT-BIH arrhythmia database is used for the evaluation of the proposed method. The obtained results indicate high perlformance (accuracy 73%) in arrhythmic episode classijicafion.en
heal.journalNameComputers in Cardiologyen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΟΕ

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