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  5. Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) - ΙΑΤ
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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/23575
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dc.contributor.authorTsipouras, M. G.en
dc.contributor.authorTzallas, A. T.en
dc.contributor.authorRigas, G.en
dc.contributor.authorBougia, P.en
dc.contributor.authorFotiadis, D. I.en
dc.contributor.authorKonitsiotis, S.en
dc.date.accessioned2015-11-24T19:33:50Z-
dc.date.available2015-11-24T19:33:50Z-
dc.identifier.issn1557-170X-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/23575-
dc.rightsDefault Licence-
dc.subjectAccelerationen
dc.subjectAlgorithmsen
dc.subjectAntiparkinson Agents/pharmacologyen
dc.subjectAutomationen
dc.subjectBiosensing Techniquesen
dc.subjectDyskinesia, Drug-Induced/*physiopathologyen
dc.subjectEquipment Designen
dc.subjectHumansen
dc.subjectLevodopa/*pharmacologyen
dc.subjectModels, Statisticalen
dc.subjectMonitoring, Ambulatory/*instrumentation/methodsen
dc.subjectParkinson Disease/diagnosisen
dc.subjectProgramming Languagesen
dc.subjectReproducibility of Resultsen
dc.subjectSignal Processing, Computer-Assisteden
dc.titleAutomated Levodopa-induced dyskinesia assessmenten
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primary10.1109/IEMBS.2010.5626130-
heal.identifier.secondaryhttp://www.ncbi.nlm.nih.gov/pubmed/21095695-
heal.identifier.secondaryhttp://ieeexplore.ieee.org/ielx5/5608545/5625939/05626130.pdf?tp=&arnumber=5626130&isnumber=5625939-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικήςel
heal.publicationDate2010-
heal.abstractAn automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject's body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).en
heal.journalNameConf Proc IEEE Eng Med Biol Socen
heal.journalTypepeer-reviewed-
heal.fullTextAvailabilityTRUE-
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