Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome

Maali, Y; Al-Jumaily, AA

Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome

Maali, Y Al-Jumaily, AA

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Publisher:: IEEE
Publication Type:: Conference
Citation:: Maali Yashar and Al-Jumaily Adel 2011, 'Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome', , IEEE, Singapore, , pp. V2-337-V2-340.
Issue Date:: 2011

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Field	Value	Language
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Al-Jumaily, AA	en_US
dc.contributor.editor	Dr. Steve Thatcher	en_US
dc.date	20110226	en_US
dc.date.accessioned	2012-10-12T03:36:21Z
dc.date.available	2012-10-12T03:36:21Z
dc.date.issued	2011	en_US
dc.identifier	2010004912	en_US
dc.identifier.citation	Maali Yashar and Al-Jumaily Adel 2011, 'Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome', , IEEE, Singapore, , pp. V2-337-V2-340.	en_US
dc.identifier.issn	978-1-4244-9252-7	en_US
dc.identifier.other	E1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19197
dc.description.abstract	This paper proposes a genetic fuzzy approach for detecting Apnea/Hypopnea events by using Air flow, thoracic and abdominal respiratory movement signals and Oxygen desaturation as inputs. Results show efficiency of this approach.	en_US
dc.language	English	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	3rd International Conference on Machine Learning and Computing (ICMLC 2011)	en_US
dc.relation.ispartof	Verified OK	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	Apnea, Hypopnea, sleep disorder, detection	en_US
dc.subject.classification	Neural, Evolutionary and Fuzzy Computation	en_US
dc.title	Genetic Fuzzy Approach for detecting Sleep Apnea/Hypopnea Syndrome	en_US
dc.type	Conference
utslib.location	Singapore	en_US
utslib.citation.startpage	V2-337	en_US
utslib.citation.endpage	V2-340	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080108	en_US
utslib.percentage	100	en_US
dc.location.activity	Singapore	en_US
utslib.copyright.status	closed_access

Abstract:

This paper proposes a genetic fuzzy approach for detecting Apnea/Hypopnea events by using Air flow, thoracic and abdominal respiratory movement signals and Oxygen desaturation as inputs. Results show efficiency of this approach.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/19197