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Statements

Subject Item
n2:RIV%2F68407700%3A21460%2F09%3A00158660%21RIV10-MSM-21460___
rdf:type
n4:Vysledek skos:Concept
dcterms: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.
dcterms:title
Wards Clustering Method for Distinction Between Neonatal Sleep Stages Wards Clustering Method for Distinction Between Neonatal Sleep Stages
skos:prefLabel
Wards Clustering Method for Distinction Between Neonatal Sleep Stages Wards Clustering Method for Distinction Between Neonatal Sleep Stages
skos:notation
RIV/68407700:21460/09:00158660!RIV10-MSM-21460___
n5:aktivita
n9:Z n9:P
n5:aktivity
P(1ET101210512), Z(MSM6840770012)
n5:dodaniDat
n16:2010
n5:domaciTvurceVysledku
n11:8891532
n5:druhVysledku
n14:D
n5:duvernostUdaju
n20:S
n5:entitaPredkladatele
n6:predkladatel
n5:idSjednocenehoVysledku
351704
n5:idVysledku
RIV/68407700:21460/09:00158660
n5:jazykVysledku
n13:eng
n5:klicovaSlova
PSG; EEG; neonatal; segmentation; clustering
n5:klicoveSlovo
n10:clustering n10:EEG n10:segmentation n10:PSG n10:neonatal
n5:kontrolniKodProRIV
[FDC5A9905D9A]
n5:mistoKonaniAkce
Mnichov
n5:mistoVydani
Berlin
n5:nazevZdroje
World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
n5:obor
n12:IN
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
6
n5:projekt
n22:1ET101210512
n5:rokUplatneniVysledku
n16:2009
n5:tvurceVysledku
Krajča, Vladimír Gerla, Václav
n5:typAkce
n15:WRD
n5:zahajeniAkce
2009-09-07+02:00
n5:zamer
n17:MSM6840770012
s:issn
1680-0737
s:numberOfPages
4
n21:hasPublisher
Springer Science+Business Media
n18:isbn
978-3-642-03897-6
n19:organizacniJednotka
21460