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DETECTING K-COMPLEXES FOR SLEEP STAGE IDENTIFICATION USING NONSMOOTH OPTIMIZATION

Published online by Cambridge University Press:  20 March 2012

D. MOLONEY
Affiliation:
University of Ballarat, P. O. Box 663, Ballarat, Victoria 3353, Australia (email: d.moloney@ballarat.edu.au, n.sukhorukova@ballarat.edu.au, p.vamplew@ballarat.edu.au, j.ugon@ballarat.edu.au)
N. SUKHORUKOVA*
Affiliation:
University of Ballarat, P. O. Box 663, Ballarat, Victoria 3353, Australia (email: d.moloney@ballarat.edu.au, n.sukhorukova@ballarat.edu.au, p.vamplew@ballarat.edu.au, j.ugon@ballarat.edu.au)
P. VAMPLEW
Affiliation:
University of Ballarat, P. O. Box 663, Ballarat, Victoria 3353, Australia (email: d.moloney@ballarat.edu.au, n.sukhorukova@ballarat.edu.au, p.vamplew@ballarat.edu.au, j.ugon@ballarat.edu.au)
J. UGON
Affiliation:
University of Ballarat, P. O. Box 663, Ballarat, Victoria 3353, Australia (email: d.moloney@ballarat.edu.au, n.sukhorukova@ballarat.edu.au, p.vamplew@ballarat.edu.au, j.ugon@ballarat.edu.au)
G. LI
Affiliation:
Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia (email: gang.li@deakin.edu.au, gleb.beliakov@deakin.edu.au)
G. BELIAKOV
Affiliation:
Deakin University, 221 Burwood Highway, Burwood, Victoria 3125, Australia (email: gang.li@deakin.edu.au, gleb.beliakov@deakin.edu.au)
C. PHILIPPE
Affiliation:
Hôpital Tenon, 4 rue de la Chine, 75970 Paris Cedex 20, France (email: carole.philippe@tnn.aphp.fr, amielhelene@gmail.com, adrien.ugon@tnn.aphp.fr)
H. AMIEL
Affiliation:
Hôpital Tenon, 4 rue de la Chine, 75970 Paris Cedex 20, France (email: carole.philippe@tnn.aphp.fr, amielhelene@gmail.com, adrien.ugon@tnn.aphp.fr)
A. UGON
Affiliation:
Hôpital Tenon, 4 rue de la Chine, 75970 Paris Cedex 20, France (email: carole.philippe@tnn.aphp.fr, amielhelene@gmail.com, adrien.ugon@tnn.aphp.fr)
*
For correspondence; e-mail: n.sukhorukova@ballarat.edu.au
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Abstract

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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.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 2012

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