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Statements

Subject Item
n2:RIV%2F61988987%3A17450%2F11%3AA12011Z9%21RIV12-MSM-17450___
rdf:type
n6:Vysledek skos:Concept
dcterms:description
This paper presents application of an automatic classification system on 53 animal polysomnographic recordings. A two-step automatic system is used to score the recordings into three traditional stages: wake, NREM sleep and REM sleep. In the first step of the analysis, monitored signals are analyzed using artifact identification strategy and artifact-free signals are selected. Then, 30sec epochs are classified according to relevant features extracted from available signals using artificial neural networks. The overall classification accuracy reached by the presented classification system exceeded 95%, when analyzed 53 polysomnographic recordings. This paper presents application of an automatic classification system on 53 animal polysomnographic recordings. A two-step automatic system is used to score the recordings into three traditional stages: wake, NREM sleep and REM sleep. In the first step of the analysis, monitored signals are analyzed using artifact identification strategy and artifact-free signals are selected. Then, 30sec epochs are classified according to relevant features extracted from available signals using artificial neural networks. The overall classification accuracy reached by the presented classification system exceeded 95%, when analyzed 53 polysomnographic recordings.
dcterms:title
Automatic Classification of Sleep/Wake Stages Using Two-Step System Automatic Classification of Sleep/Wake Stages Using Two-Step System
skos:prefLabel
Automatic Classification of Sleep/Wake Stages Using Two-Step System Automatic Classification of Sleep/Wake Stages Using Two-Step System
skos:notation
RIV/61988987:17450/11:A12011Z9!RIV12-MSM-17450___
n6:predkladatel
n7:orjk%3A17450
n3:aktivita
n15:V
n3:aktivity
V
n3:dodaniDat
n17:2012
n3:domaciTvurceVysledku
n10:5490529
n3:druhVysledku
n5:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n20:predkladatel
n3:idSjednocenehoVysledku
187558
n3:idVysledku
RIV/61988987:17450/11:A12011Z9
n3:jazykVysledku
n4:eng
n3:klicovaSlova
decision making; diagnosis; medical applications; pattern recognition; signal processing.
n3:klicoveSlovo
n12:medical%20applications n12:signal%20processing. n12:diagnosis n12:pattern%20recognition n12:decision%20making
n3:kontrolniKodProRIV
[F1E494C40BC3]
n3:mistoKonaniAkce
Ostrava
n3:nazevZdroje
Communications in Computer and Information Science
n3:obor
n18:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n17:2011
n3:tvurceVysledku
Zoubek, Lukáš Chapotot, Florian
n3:typAkce
n9:WRD
n3:zahajeniAkce
2011-07-07+02:00
s:numberOfPages
12
n16:hasPublisher
Springer-Verlag
n21:isbn
978-3-642-22388-4
n13:organizacniJednotka
17450