Λάφκα, Θεοδώρα - Ιωάννα; Σινάνογλου, Βασιλεία; Λάζος, Ευάγγελοςd with filtered time-domain EEG

dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Μονογυιού, Ευστρατία Α.	el
dc.contributor.author	Κτώνας, Περικλής Υ.	el
dc.contributor.author	Παπαρρηγόπουλος, Θωμάς	el
dc.contributor.author	Δίκαιος, Δημήτρης Γ.	el
dc.date.accessioned	2015-02-02T18:17:09Z
dc.date.available	2015-02-02T18:17:09Z
dc.date.issued	2015-02-02
dc.identifier.uri	http://hdl.handle.net/11400/5512
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.elsevier.com	en
dc.subject	Pattern recognition
dc.subject	Sleep spindles
dc.subject	Αναγνώριση προτύπων
dc.subject	Άτρακτοι ύπνου
dc.title	Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG	en
heal.type	journalArticle
heal.secondaryTitle	a feasibility study	en
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρική τεχνολογία
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-Ιατρική τεχνολογία
heal.keywordURI	http://skos.um.es/unescothes/C02924
heal.contributorName	Ουζούνογλου, Νικόλαος Κ.	el
heal.contributorName	Σολδάτος, Κωνσταντίνος Ρ.	el
heal.identifier.secondary	doi:10.1016/j.cmpb.2005.02.006
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Ventouras, E., Monoyiou, E., Ktonas, P., Paparrigopoulos, T., Dikeos, D., et al. (June 2005). Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study. Computer Methods and Programs in Biomedicine. 78(3). pp. 191–207. Elsevier Science Ltd. Available from: http://www.sciencedirect.com [Accessed 25/04/2005]	en
heal.abstract	An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.	en
heal.publisher	Elsevier Science Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true