Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Maali, Y; Al-Jumaily, A

Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Maali, Y Al-Jumaily, A

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Publisher:: IACSIT Press
Publication Type:: Journal Article
Citation:: International Journal of Machine Learning and Computing, 2012, 2 (5), pp. 685 - 688
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	International Journal of Machine Learning and Computing, 2012, 2 (5), pp. 685 - 688	en_US
dc.identifier.issn	2010-3700	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22895
dc.description.abstract	Sleep Apnea (SA) is one of the most common andimportant part of sleep disorders. Unfortunately, sleep apneamay be going undiagnosed for years, because of the personsunawareness. The common diagnose procedure usuallyrequired an overnight sleep test. During the test, a recording ofmany biosignals, which related to breath, are obtained bypolysomnography machine to detect this syndrome. Themanual process for detecting the sleep Apnea by analysis therecording data is highly cost and time consuming. So, severalworks tried to develop systems that achieve this automatically.This paper proposes a genetic fuzzy approach for detectingApnea/Hypopnea events by using Air flow, thoracic andabdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.	en_US
dc.publisher	IACSIT Press	en_US
dc.relation.ispartof	International Journal of Machine Learning and Computing	en_US
dc.title	Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	2	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	5	en_US
pubs.volume	2	en_US

Abstract:

Sleep Apnea (SA) is one of the most common andimportant part of sleep disorders. Unfortunately, sleep apneamay be going undiagnosed for years, because of the personsunawareness. The common diagnose procedure usuallyrequired an overnight sleep test. During the test, a recording ofmany biosignals, which related to breath, are obtained bypolysomnography machine to detect this syndrome. Themanual process for detecting the sleep Apnea by analysis therecording data is highly cost and time consuming. So, severalworks tried to develop systems that achieve this automatically.This paper proposes a genetic fuzzy approach for detectingApnea/Hypopnea events by using Air flow, thoracic andabdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.

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http://hdl.handle.net/10453/22895