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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/13881
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dc.contributor.authorExarchos, T. P.en
dc.contributor.authorTsipouras, M. G.en
dc.contributor.authorExarchos, C. P.en
dc.contributor.authorPapaloukas, C.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorMichalis, L. K.en
dc.date.accessioned2015-11-24T17:33:37Z-
dc.date.available2015-11-24T17:33:37Z-
dc.identifier.issn0933-3657-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/13881-
dc.rightsDefault Licence-
dc.subjectfuzzy expert systemen
dc.subjectdata miningen
dc.subjectoptimizationen
dc.subjectischaemiaen
dc.subjectarrhythmiaen
dc.subjectneural-networksen
dc.subjectheart-rateen
dc.subjectt-waveen
dc.subjectdatabaseen
dc.subjectoptimizationen
dc.subjectrecognitionen
dc.subjectframeworken
dc.subjectepisodesen
dc.subjectecgsen
dc.titleA methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision treeen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/j.artmed.2007.04.001-
heal.identifier.secondary<Go to ISI>://000248725700003-
heal.identifier.secondaryhttp://ac.els-cdn.com/S0933365707000504/1-s2.0-S0933365707000504-main.pdf?_tid=3799f42b9b7e79de02ae1781face61fb&acdnat=1339756637_387f0db6fd684becb0ae90c719c9e522-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικώνel
heal.publicationDate2007-
heal.abstractObjective: In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. Methods: The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. Material: The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. Results: The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. Conclusion: The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis. (C) 2007 Elsevier B.V. All rights reserved.en
heal.publisherElsevieren
heal.journalNameArtif Intell Meden
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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