Probabilistic neural network versus cubic least-squares minimum-distance in classifying eeg signals

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Κανδαράκης, Ιωάννης Σ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.date.accessioned	2015-01-28T12:20:16Z
dc.date.available	2015-01-28T12:20:16Z
dc.date.issued	2015-01-28
dc.identifier.uri	http://hdl.handle.net/11400/4929
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	Classification--Archives
dc.subject	Neural computers
dc.subject	Σύστημα ταξινόμησης
dc.subject	Νευρωνικό δίκτυο
dc.title	Probabilistic neural network versus cubic least-squares minimum-distance in classifying eeg signals	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the International Conference of Computational Methods in Sciences and Enginnering 2003	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://id.loc.gov/authorities/subjects/sh85026720
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh87008041
heal.contributorName	Ραμπαβίλας, Ανδρέας Ν.	el
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.contributorName	Σίμος, Θεόδωρος (επιμ.)	el
heal.identifier.secondary	ISBN: 981-238-595-9
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2003
heal.bibliographicCitation	Kalatzis, I., Piliouras, N., Ventouras, E., kandarakis, I., Papageorgiou, C., et al. (2003). Probabilistic neural network versus cubic least-squares minimum-distance in classifying eeg signals. In the International Conference of Computational Methods in Sciences and Enginnering. pp. 268-271. Kastoria, Greece, 2003.	en
heal.abstract	The purpose of the present study is the implementation of a classification system for differentiating healthy subjects from patients with depression. Twenty-five depressive patients and an equal number of gender and aged-matched normal controls were evaluated using a computerized version of the digit span Wechsler test. Morphological waveform features were extracted from the digitized Event-Related Potential (ERP) signals, recorded from 15 scalp electrodes. The feature extraction process focused on the P600 component of the ERPs. The designed system comprised two classifiers, the probabilistic neural network (PNN) and the cubic least-squares (CLS) minimum-distance, two routines for feature reduction and feature selection, and an overall system evaluation routine, consisting of the exhaustive search and the leave-one-out methods. Highest classification accuracies achieved were 96% for the PNN and 94% for the CLS, using the ‘latency/amplitude ratio’ and ‘peak-to-peak slope’ two-feature combination. In conclusion, employing computer-based pattern recognition techniques with features not easily evaluated by the clinician, patients with depression could be distinguished from healthy subjects with high accuracy.	en
heal.publisher	[χ.ό.]	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference of Computational Methods in Sciences and Enginnering	en
heal.conferenceItemType	full paper