Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study

Ventouras, EM; Monoyiou, EA; Ktonas, PY; Paparrigopoulos, T; Dikeos, DG; Uzunoglu, NK; Soldatos, CR

dc.contributor.author	Ventouras, EM	en
dc.contributor.author	Monoyiou, EA	en
dc.contributor.author	Ktonas, PY	en
dc.contributor.author	Paparrigopoulos, T	en
dc.contributor.author	Dikeos, DG	en
dc.contributor.author	Uzunoglu, NK	en
dc.contributor.author	Soldatos, CR	en
dc.date.accessioned	2014-03-01T01:23:05Z
dc.date.available	2014-03-01T01:23:05Z
dc.date.issued	2005	en
dc.identifier.issn	0169-2607	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16802
dc.subject	Artificial neural networks	en
dc.subject	EEG	en
dc.subject	Pattern recognition	en
dc.subject	Sleep spindles	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.classification	Medical Informatics	en
dc.subject.other	Biomedical engineering	en
dc.subject.other	Dynamics	en
dc.subject.other	Electroencephalography	en
dc.subject.other	Feature extraction	en
dc.subject.other	Pharmacodynamics	en
dc.subject.other	Time domain analysis	en
dc.subject.other	Multi-layer perceptron (MLP)	en
dc.subject.other	Sleep spindle detection	en
dc.subject.other	Visual assessment	en
dc.subject.other	Neural networks	en
dc.subject.other	accuracy	en
dc.subject.other	adult	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	controlled study	en
dc.subject.other	electroencephalogram	en
dc.subject.other	feasibility study	en
dc.subject.other	female	en
dc.subject.other	human	en
dc.subject.other	human experiment	en
dc.subject.other	mathematical computing	en
dc.subject.other	sleep spindle	en
dc.subject.other	time	en
dc.subject.other	vision	en
dc.subject.other	Alpha Rhythm	en
dc.subject.other	Beta Rhythm	en
dc.subject.other	Electroencephalography	en
dc.subject.other	Feasibility Studies	en
dc.subject.other	Greece	en
dc.subject.other	Humans	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Pattern Recognition, Automated	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Signal Processing, Computer-Assisted	en
dc.subject.other	Sleep Stages	en
dc.subject.other	Sleep, REM	en
dc.title	Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cmpb.2005.02.006	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cmpb.2005.02.006	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting steep 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 steep process, and for studies of pharmacological effects on steep structure, as revealed by the modification of total spindle duration. (c) 2005 Elsevier Ireland Ltd. All rights reserved.	en
heal.publisher	ELSEVIER IRELAND LTD	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
dc.identifier.doi	10.1016/j.cmpb.2005.02.006	en
dc.identifier.isi	ISI:000229655600002	en
dc.identifier.volume	78	en
dc.identifier.issue	3	en
dc.identifier.spage	191	en
dc.identifier.epage	207	en