Automated detecting sleep apnea syndrome: A novel system based on genetic SVM

Maali, Y; Al-Jumaily, A

Automated detecting sleep apnea syndrome: A novel system based on genetic SVM

Maali, Y Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 2011, pp. 590 - 594
Issue Date:: 2011-12-01

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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.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011, 2011, pp. 590 - 594	en_US
dc.identifier.isbn	9781457721502	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31313
dc.description.abstract	Sleep Apnea (SA) is one of the common symptoms and important part of sleep disorders. It has consequences that affect all daily life activities and present danger to the patient and/or others. The common diagnose procedure is based on an overnight sleep test. The test is usually including recording of several bio-signals that used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most of bio-signals to achieve reasonable accuracy. The manual process is highly cost and time-consuming. This paper presents a novel automatic system for detecting Apnea events by using just few of bio-signals that are related to breathe defect. This work use only Air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of three main parts which are signal segmentation, feature generation and classification based on genetic SVM. Results show efficiency of this system and its superiority versus previous methods with more bio-signals as input. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011	en_US
dc.relation.isbasedon	10.1109/HIS.2011.6122171	en_US
dc.title	Automated detecting sleep apnea syndrome: A novel system based on genetic SVM	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Malaysia
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.publication-status	Published	en_US

Abstract:

Sleep Apnea (SA) is one of the common symptoms and important part of sleep disorders. It has consequences that affect all daily life activities and present danger to the patient and/or others. The common diagnose procedure is based on an overnight sleep test. The test is usually including recording of several bio-signals that used to detect this syndrome. The conventional approach of detecting the sleep apnea uses a manual analysis of most of bio-signals to achieve reasonable accuracy. The manual process is highly cost and time-consuming. This paper presents a novel automatic system for detecting Apnea events by using just few of bio-signals that are related to breathe defect. This work use only Air flow, thoracic and abdominal respiratory movement as inputs for the system. The proposed technique consists of three main parts which are signal segmentation, feature generation and classification based on genetic SVM. Results show efficiency of this system and its superiority versus previous methods with more bio-signals as input. © 2011 IEEE.

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http://hdl.handle.net/10453/31313