Detection of EEG sleep spindles using artificial and probabilistic neural networks

dc.contributor.author	Κοψαυτής, Ν.	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	2015-01-29T12:54:21Z
dc.date.issued	2015-01-29
dc.identifier.uri	http://hdl.handle.net/11400/5061
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	Artificial neural network
dc.subject	Sleep spindles
dc.subject	Τεχνητό νευρωνικό δίκτυο
dc.subject	Άτρακτοι ύπνου
dc.title	Detection of EEG sleep spindles using artificial and probabilistic neural networks	en
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 5th European Symposium on Biomedical Engineering	en
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.contributorName	Σολδάτος, Κωνσταντίνος Ρ.	el
heal.contributorName	Νικηφορίδης, Γεώργιος Π.	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	2006
heal.bibliographicCitation	Kopsaftis, N., Ventouras, E., Ktonas, P., Paparrigopoulos, T., Dikeos, D., et al. (2006). Detection of EEG sleep spindles using artificial and probabilistic neural networks. In the 5th European Symposium on Biomedical Engineering. University of Patras: Patras, 2006.	en
heal.abstract	In the present work an Artificial Neural Network (ANN) and a Probabilistic Neural Network (PNN) were used in detecting EEG sleep spindles. The networks were trained separately using the same spindles. The networks were trained separately using the same spindles. False positives (FPs) were characterized in two categories: FP1 included FP indications by the system, when no spindles were visually detected and FP2 concerned the case when a visually detected spindle corresponded to two distinct spindle indications by the system. ANN had higher sensitivity than PNN, when the classifiers functioned separately (88.5% vs 75.8%). All spindles detected by PNN were also detected by ANN. If only FP1s were taken into account, then FP rate was 16.6% and 0% respectively. The combination of a two-stage detector, with the first level being the ANN and the second being the PNN resulted in a system retaining the sensitivity of the ANN, and the ability to characterize the spindles indicated by the system into two categories, those indicated by both networks, with a probability of 3.5% to be FPs, and the remaining ones, which need a second round of visual inspection, because they have a high probability (56.7%) to be FP indications.	en
heal.publisher	[χ.ό.]	el
heal.fullTextAvailability	true
heal.conferenceName	European Symposium on Biomedical Engineering	en
heal.conferenceItemType	full paper