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Publikační činnost

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Typ záznamu:	stať ve sborníku (D)
Domácí pracoviště:	Katedra informačních a komunikačních technologií (45080)
Název:	Automatic Classification of Sleep/Wake Stages Using Two-Step System
Citace	Zoubek, L. a Chapotot, F. Automatic Classification of Sleep/Wake Stages Using Two-Step System. In: Communications in Computer and Information Science. Springer, 2011. s. 106-117. ISBN 978-3-642-22388-4.

Podnázev
Rok vydání:	2011
Obor:	Informatika
Počet stran:	12
Strana od:	106
Strana do:	117
Forma vydání:
Kód ISBN:	978-3-642-22388-4
Kód ISSN:
Název sborníku:	Communications in Computer and Information Science
Sborník:	Mezinárodní
Název nakladatele:	Springer
Místo vydání:	Neuveden
Stát vydání:	Sborník vydaný v zahraničí
Název konference:	International conference on Digital Information Processing and Communications
Místo konání konference:	Ostrava
Datum zahájení konference:
Typ akce podle státní příslušnosti účastníků akce:	Celosvětová akce
Kód UT WoS:
EID:

Klíčová slova anglicky:	decision making, diagnosis, medical applications, pattern recognition, signal processing.
Popis v původním jazyce:	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.
Popis v anglickém jazyce:	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.

Seznam ohlasů

Ohlas

R01:	RIV/61988987:17450/11:A12011Z9

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