Signal analysis methods to discriminate between obsessive-compulsive disorder (OCD) patients and healthy controls

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.date.accessioned	2015-01-29T11:22:00Z
dc.date.issued	2015-01-29
dc.identifier.uri	http://hdl.handle.net/11400/5048
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Pattern recognition
dc.subject	Support vector machines
dc.subject	Αναγνώριση προτύπων
dc.subject	Μηχανές διανυσμάτων υποστήριξης
dc.title	Signal analysis methods to discriminate between obsessive-compulsive disorder (OCD) patients and healthy controls	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 1st International Conference on Experiments/Process/System Modelling/Simulation/Optimization (1st IC-EpsMso) (CD-ROM)	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρικά όργανα και εξοπλισμός
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://skos.um.es/unescothes/C02465
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρικά όργανα και εξοπλισμός
heal.keywordURI	http://skos.um.es/unescothes/C02924
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008009003
heal.contributorName	Ραμπαβίλας, Ανδρέας Ν.	el
heal.contributorName	Σολδάτος, Κωνσταντίνος Ρ.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Glotsos, D., Ventouras, E., Papageorgiou, C., et al. (2005). Signal analysis methods to discriminate between obsessive-compulsive disorder (OCD) patients and healthy controls. In the 1st International Conference on Experiments/Process/System Modelling/Simulation/Optimization. University of Patras: Athens, 6th-9th July 2005.	en
heal.abstract	A series of Event-Related Potential (ERP) signals were analyzed with pattern recognition methods using new morphological features and powerful classifiers, in an attempt to develop a computer-aided discrimination system of OCD patients from controls. Eighteen OCD patients and twenty controls were examined. All subjects were evaluated by a computerized version of the digit span subtest of the Wechsler Adult Intelligence Scale. EEGs were recorded from 15 scalp leads. From the P600 component of each signal nineteen waveform-features were calculated. The 3rd-degree Least Squares - Minimum Distance classifier (LSMD3C) and the Support Vector Machines classifier (SVMC) were developed. The LSMD3C was fed with features from all leads and the best feature combinations were inputted into the SVMC to improve the classification results. Highest overall accuracy (89.5%) was found at the C6 lead indicating that OCD patients may present deficits related to working memory mechanisms corresponding to the right temporocentral region.	en
heal.publisher	University of Patras	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Experiments/Process/System Modelling/Simulation/Optimization	en
heal.conferenceItemType	full paper