Self-advising SVM for sleep apnea classification

Maali, Y; Al-Jumaily, A; Laks, L

Self-advising SVM for sleep apnea classification

Maali, Y Al-Jumaily, A

Laks, L

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Publication Type:: Conference Proceeding
Citation:: CEUR Workshop Proceedings, 2012, 944 pp. 24 - 33
Issue Date:: 2012-12-01

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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.contributor.author	Laks, L	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	CEUR Workshop Proceedings, 2012, 944 pp. 24 - 33	en_US
dc.identifier.issn	1613-0073	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31683
dc.description.abstract	In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to trans-fer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, ob-structive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification.	en_US
dc.relation.ispartof	CEUR Workshop Proceedings	en_US
dc.title	Self-advising SVM for sleep apnea classification	en_US
dc.type	Conference Proceeding
utslib.citation.volume	944	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Sydney, Australia
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	944	en_US

Abstract:

In this paper Self-Advising SVM, a new proposed version of SVM, is investigated for sleep apnea classification. Self-Advising SVM tries to trans-fer more information from training phase to the test phase in compare to the traditional SVM. In this paper Sleep apnea events are classified to central, ob-structive or mixed, by using just three signals, airflow, abdominal and thoracic movement, as inputs. Statistical tests show that self-advising SVM performs better than traditional SVM in sleep apnea classification.

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http://hdl.handle.net/10453/31683