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Detecting K-complexes for sleep stage identification using nonsmooth optimisation

Moloney, D., Sukhorukova, N., Vamplew, P., Ugon, J., Li, G., Beliakov, G., Philippe, C., Amiel, H. and Ugon, A. 2011, Detecting K-complexes for sleep stage identification using nonsmooth optimisation, ANZIAM Journal, vol. 52, no. 4, pp. 319-332.

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Title Detecting K-complexes for sleep stage identification using nonsmooth optimisation
Formatted title Detecting K-complexes for sleep stage identification using nonsmooth optimisation
Author(s) Moloney, D.
Sukhorukova, N.
Vamplew, P.
Ugon, J.
Li, G.
Beliakov, G.
Philippe, C.
Amiel, H.
Ugon, A.
Journal name ANZIAM Journal
Volume number 52
Issue number 4
Start page 319
End page 332
Total pages 14
Publisher Cambridge University Press
Place of publication Cambridge, England
Publication date 2011-04
ISSN 1446-1811
Keyword(s) K-complexes
nonsmooth optimization
classification
Summary The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2012
Copyright notice ©2011, Cambridge University Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044331

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.