Multi-layer perceptrons for the detection of sleep eeg transient waveforms

dc.contributor.author	Μονογυιού, Ευστρατία Α.	el
dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Κτώνας, Περικλής Υ.	el
dc.contributor.author	Παπαρρηγόπουλος, Θωμάς	el
dc.contributor.author	Δίκαιος, Δημήτρης Γ.	el
dc.date.accessioned	2015-01-28T10:36:05Z
dc.date.issued	2015-01-28
dc.identifier.uri	http://hdl.handle.net/11400/4915
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	Electroencephalography
dc.subject	Sleep staging
dc.subject	Ηλεκτροεγκεφαλογράφημα
dc.subject	Σταδιοποίηση ύπνου
dc.title	Multi-layer perceptrons for the detection of sleep eeg transient waveforms	en
heal.type	conferenceItem
heal.generalDescription	4th International Conference Neural Networks and Expert Systems in Medicine and Healthcare NNESMED 2001 20-22 June 2001, Milos Island, Greece	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://lod.nal.usda.gov/19230
heal.contributorName	Ουζούνογλου, Νικόλαος Κ.	el
heal.contributorName	Σολδάτος, Κωνσταντίνος Ρ.	el
heal.identifier.secondary	ISBN: 960-85316-5-9
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	2001
heal.bibliographicCitation	Monoyiou, E., Ventouras, E., Ktonas, P., Paparrigopoulos, T., Dikeos, D.G., et al. (2001). Multi-layer perceptrons for the detection of sleep eeg transient waveforms. In the 4th International Conference Neural Networks and Expert Systems in Medicine and Healthcare. Milos Island, 20th-22th June 2001.	en
heal.abstract	Spindles are rhythmic transients present in the sleep electroencephalogram (EEG). Automatic spindle detection techniques are actively sought in order to make sleep staging easier as well as to enable the study of the microstructure of sleep. In the present work an Artificial Neural Network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting spindles and quantifying their temporal characteristics. The EEG is band-pass filtered and fed to the ANN without feature extraction. The visual inspection of the original EEG was performed by two experienced polysomnographers, involving three levels of EEG segment characterization. This enabled a thorough assessment of the intra- and inter-scorer variability, to which the performance of the ANN was compared. The overall sensitivity of the network, ranged from 81.3% to 85.7% and false positives rate from 10.3 to 6.3%.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	International Conference Neural Networks and Expert Systems in Medicine and Healthcare	en
heal.conferenceItemType	full paper