Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.contributor.author	Ραμπαβίλας, Ανδρέας Δ.	el
dc.date.accessioned	2015-04-29T10:59:52Z
dc.date.available	2015-04-29T10:59:52Z
dc.date.issued	2015-04-29
dc.identifier.uri	http://hdl.handle.net/11400/9243
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.source	http://www.sciencedirect.com/science/article/pii/S0169260703001305	en
dc.subject	Depression
dc.subject	Pattern recognition
dc.subject	Αναγνώριση προτύπων
dc.subject	Κατάθλιψη
dc.title	Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://zbw.eu/stw/descriptor/15410-6
heal.contributorName	Κάβουρας, Διονύσης Α.	en
heal.identifier.secondary	doi:10.1016/j.cmpb.2003.09.003
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2004
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Ventouras, E., Papageorgiou, C. and Rabavilas, A. (July 2004). Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals. Computer Methods and Programs in Biomedicine. 75(1). pp. 11-22. Elsevier Ireland Ltd: 2004.Available from: http://www.sciencedirect.com/science/article/pii/S0169260703001305 [Accessed 02/12/2003]	en
heal.abstract	A computer-based classification system has been designed capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised 25 patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEG activity was recorded and digitized from 15 scalp electrodes (leads). Seventeen features related to the shape of the waveform were generated and were employed in the design of an optimum support vector machine (SVM) classifier at each lead. The outcomes of those SVM classifiers were selected by a majority-vote engine (MVE), which assigned each subject to either the normal or depressive classes. MVE classification accuracy was 94% when using all leads and 92% or 82% when using only the right or left scalp leads, respectively. These findings support the hypothesis that depression is associated with dysfunction of right hemisphere mechanisms mediating the processing of information that assigns a specific response to a specific stimulus, as those mechanisms are reflected by the P600 component of ERPs. Our method may aid the further understanding of the neurophysiology underlying depression, due to its potentiality to integrate theories of depression and psychophysiology.	en
heal.publisher	Elsevier Ireland Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true