A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

Maali, Y; Al-Jumaily, A

A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection

Maali, Y Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
Issue Date:: 2012-10-04

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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	2012-10-04	en_US
dc.identifier.citation	2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012	en_US
dc.identifier.isbn	9781467315098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22940
dc.description.abstract	Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Congress on Evolutionary Computation, CEC 2012	en_US
dc.relation.isbasedon	10.1109/CEC.2012.6256138	en_US
dc.title	A novel partially connected cooperative parallel PSO-SVM algorithm: Study based on sleep apnea detection	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Brisbane, Australia	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.publication-status	Published	en_US

Abstract:

Sleep disorders are common in a general population. It effect one in 5 adults and has several short term and long term bad side effects on health. Sleep apnea (SA) is the most important and common component of sleep disorders. This paper presents an automatic approach for detecting apnea events by using few bio-singles that are related to breathe defect. This work uses only air flow, thoracic and abdominal respiratory movement as input. The proposed algorithm consists of three main parts which are signal segmentation, feature generation and classification. A new proposed segmentation method intelligently segments the input signals for further classification, then features are generated for each segment by wavelet packet coefficients and also original signals. In classification phase a unique parallel PSO-SVM algorithm is investigated. PSO used to tune SVM parameters, and also data reduction. Proposed parallel structure used to help PSO to search space more efficiently, also avoiding fast convergence and local optimal results that are common problem in similar parallel algorithms. Obtained results demonstrate that the proposed method is effective and robust in sleep apnea detection and statistical tests on the results shown superiority of it versus previous methods even with more input signals, and also versus single PSO-SVM. Using fewer signals means more comfortable to subject and also, reduction of cost during recording the data. © 2012 IEEE.

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http://hdl.handle.net/10453/22940