Slow and fast EEG sleep spindle component extraction using independent component analysis

dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Κτώνας, Περικλής Υ.	el
dc.contributor.author	Τσέκου, Χαρά	el
dc.contributor.author	Παπαρρηγόπουλος, Θωμάς	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-01-26T09:28:03Z
dc.date.issued	2015-01-26
dc.identifier.uri	http://hdl.handle.net/11400/4739
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	Feature extraction
dc.subject	Ηλεκτροεγκεφαλογραφία
dc.subject	Εξαγωγή χαρακτηριστικών
dc.title	Slow and fast EEG sleep spindle component extraction using independent component analysis	el
heal.type	conferenceItem
heal.generalDescription	Proceedings of the 8th IEEE International Conference on BioInformatics and BioEngineering	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/sh85042138
heal.contributorName	Σολδάτος, Κωνσταντίνος Ρ.	el
heal.identifier.secondary	DOI: 10.1109/BIBE.2008.4696821
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	2008
heal.bibliographicCitation	Ventouras, E., Ktonas, P., Tsekou, H., Paparrigopoulos, T., Kalatzis, I., et al. (2008). Slow and fast EEG sleep spindle component extraction using independent component analysis. In the 8th IEEE International Conference on BioInformatics and BioEngineering. pp. 1-6. Athens, 8th-10th October 2008.	el
heal.abstract	Sleep spindles are groups of rhythmic activity, with a waxing-waning morphology, and are considered a hallmark of stage 2 of the sleep electroencephalogram (EEG). They are present predominantly in stages 2, 3 and 4 of the sleep EEG. Spatial analysis of sleep spindle scalp EEG and EEG inverse problem solutions have provided evidence for the existence of two distinct sleep spindle types, ldquoslowrdquo and ldquofastrdquo spindles at approximately 12 and 14 Hz, respectively. The present study aimed at processing sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting spindle ldquocomponentsrdquo corresponding to separate EEG activity patterns. The EEG activity underlying the components was also investigated, using the Low-Resolution Brain Electromagnetic Tomography (LORETA) technique, inverting the 21-channel EEG recordings to cortical current sources. Results indicate separability and stability of current sources related to sleep spindle ldquocomponentsrdquo reconstructed from separate groups of Independent Components (ICs).	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on BioInformatics and BioEngineering	en
heal.conferenceItemType	full paper