About: Wards Clustering Method for Distinction Between Neonatal Sleep Stages

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward's minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels. A robust, automated classification system for polysomnographic (PSG) data targeted to the newborn sleep stage identification is presented. The problem of polysomnographic signal classification is very often difficult because of artifacts and noise. Furthermore, for each signal, a special classification method for each particular type of segment must be mostly used. This paper proposes fully unsupervised approach using adaptive segmentation, appropriate features extraction and hierarchical clustering (Ward's minimumvariance method is used). The mutual information concept was applied to results of hierarchical clustering. The proposed procedure was tested on real neonatal data. All sleep states were successfully separated by a combination of EEG, EMG, EOG, PNG and ECG channels. (en)
Title	Wards Clustering Method for Distinction Between Neonatal Sleep Stages Wards Clustering Method for Distinction Between Neonatal Sleep Stages (en)
skos:prefLabel	Wards Clustering Method for Distinction Between Neonatal Sleep Stages Wards Clustering Method for Distinction Between Neonatal Sleep Stages (en)
skos:notation	RIV/68407700:21460/09:00158660!RIV10-MSM-21460___
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(1ET101210512), Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Krajča, Vladimír
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta biomedicínského inženýrství
http://linked.open...dnocenehoVysledku	351704
http://linked.open...ai/riv/idVysledku	RIV/68407700:21460/09:00158660
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	PSG; EEG; neonatal; segmentation; clustering (en)
http://linked.open.../riv/klicoveSlovo	clustering segmentation EEG PSG neonatal
http://linked.open...ontrolniKodProRIV	[FDC5A9905D9A]
http://linked.open...v/mistoKonaniAkce	Mnichov
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	6 (xsd:int)
http://linked.open...vavai/riv/projekt	Intelligent methods for evaluation of long-term EEG recordings
http://linked.open...UplatneniVysledku	2009
http://linked.open...iv/tvurceVysledku	Krajča, Vladimír Gerla, Václav
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2009-09-07 (xsd:date)
http://linked.open...n/vavai/riv/zamer	Transdisciplinární výzkum v oblasti biomedicínského inženýrství II
issn	1680-0737
number of pages	4 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer Science+Business Media
https://schema.org/isbn	978-3-642-03897-6
http://localhost/t...ganizacniJednotka	21460

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