Orthogonal locality sensitive fuzzy discriminant analysis in sleep-stage scoring

Khushaba, RN; Elliott, R; AlSukker, A; Al-Ani, A; McKinley, S

Orthogonal locality sensitive fuzzy discriminant analysis in sleep-stage scoring

Khushaba, RN

Elliott, R

AlSukker, A Al-Ani, A

McKinley, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2010, pp. 165 - 168
Issue Date:: 2010-11-18

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Elliott, R https://orcid.org/0000-0002-9239-7126	en_US
dc.contributor.author	AlSukker, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	McKinley, S	en_US
dc.date.issued	2010-11-18	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2010, pp. 165 - 168	en_US
dc.identifier.isbn	9780769541099	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16152
dc.description.abstract	Sleep-stage scoring plays an important role in analyzing the sleep patterns of people. Studies have revealed that Intensive Care Unit (ICU) patients do not usually get enough quality sleep, and hence, analyzing their sleep patterns is of increased importance. Due to the fact that sleep data are usually collected from a number of Electroencephalogram (EEG), Electromyogram (EMG) and Electrooculography (EOG) channels, the feature set size can become large, which may affect the development of on-line scoring systems. Hence, a dimensionality reduction step is needed. One of the powerful dimensionality reduction approaches is based on the concept of Linear Discriminant Analysis (LDA). Unlike existing variants of LDA, this paper presents a new method that considers the fuzzy nature of input measurements while preserving their local structure. Practical results indicate the significance of preserving the local structure of sleep data, which is achieved by the proposed method, and hence attaining superior results to other dimensionality reduction methods. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/ICPR.2010.49	en_US
dc.title	Orthogonal locality sensitive fuzzy discriminant analysis in sleep-stage scoring	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Istanbul, Turkey	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/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/Nursing
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Sleep-stage scoring plays an important role in analyzing the sleep patterns of people. Studies have revealed that Intensive Care Unit (ICU) patients do not usually get enough quality sleep, and hence, analyzing their sleep patterns is of increased importance. Due to the fact that sleep data are usually collected from a number of Electroencephalogram (EEG), Electromyogram (EMG) and Electrooculography (EOG) channels, the feature set size can become large, which may affect the development of on-line scoring systems. Hence, a dimensionality reduction step is needed. One of the powerful dimensionality reduction approaches is based on the concept of Linear Discriminant Analysis (LDA). Unlike existing variants of LDA, this paper presents a new method that considers the fuzzy nature of input measurements while preserving their local structure. Practical results indicate the significance of preserving the local structure of sleep data, which is achieved by the proposed method, and hence attaining superior results to other dimensionality reduction methods. © 2010 IEEE.

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http://hdl.handle.net/10453/16152