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Record type:	stať ve sborníku (D)
Home Department:	Katedra informačních a komunikačních technologií (45080)
Title:	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.

Subtitle
Publication year:	2011
Obor:	Informatika
Number of pages:	12
Page from:	106
Page to:	117
Form of publication:
ISBN code:	978-3-642-22388-4
ISSN code:
Proceedings title:	Communications in Computer and Information Science
Proceedings:	Mezinárodní
Publisher name:	Springer
Place of publishing:	Neuveden
Country of Publication:	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:	Celosvětová akce
WoS code:
EID:

Key words in English:	decision making, diagnosis, medical applications, pattern recognition, signal processing.
Annotation in original language:	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.
Annotation in english language:	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.

References

Reference

R01:	RIV/61988987:17450/11:A12011Z9

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