Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography

Li, X; Al-Ani, A; Ling, SH

Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography

Li, X

Al-Ani, A

Ling, SH

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 1444 - 1447
Issue Date:: 2018-10-26

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Field	Value	Language
dc.contributor.author	Li, X https://orcid.org/0000-0002-8719-0227	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.available	2020-08-31T19:05:38Z
dc.date.issued	2018-10-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 1444 - 1447	en_US
dc.identifier.isbn	9781538636466	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128602
dc.description.abstract	© 2018 IEEE. Patients with sleep apnea (SA) are at increased risk of stroke and cardiovascular disease. Diagnosis of sleep apnea depends on the standard overnight polysomnography (PSG). In this study, the DREAM Apnea Database was used to evaluate the importance of the various features proposed in the literature for the analysis of sleep apnea. Various timeand frequency- domain features that include wavelet and power spectral density were extracted from ECG, EMG, EEG, airflow, SaO2, abdominal and thoracic recordings. Evaluation measures of one-way analysis of variance (ANOVA) and Rank-Sum test were used to test the performance of different features. The selected feature subset indicated that frequency-domain features outperform time-domain ones. This study will help in enhancing the detection accuracy of sleep apnea for the various polysomnography signals.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2018.8512585	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Sleep Apnea Syndromes	en_US
dc.subject.mesh	Polysomnography	en_US
dc.title	Feature Selection for the Detection of Sleep Apnea using Multi-Bio Signals from Overnight Polysomnography	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0903 Biomedical Engineering	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Patients with sleep apnea (SA) are at increased risk of stroke and cardiovascular disease. Diagnosis of sleep apnea depends on the standard overnight polysomnography (PSG). In this study, the DREAM Apnea Database was used to evaluate the importance of the various features proposed in the literature for the analysis of sleep apnea. Various timeand frequency- domain features that include wavelet and power spectral density were extracted from ECG, EMG, EEG, airflow, SaO2, abdominal and thoracic recordings. Evaluation measures of one-way analysis of variance (ANOVA) and Rank-Sum test were used to test the performance of different features. The selected feature subset indicated that frequency-domain features outperform time-domain ones. This study will help in enhancing the detection accuracy of sleep apnea for the various polysomnography signals.

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http://hdl.handle.net/10453/128602