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Detecting K-complexes for sleep stage identification using nonsmooth optimisation

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journal contribution
posted on 2011-04-01, 00:00 authored by D Moloney, N Sukhorukova, P Vamplew, Julien UgonJulien Ugon, Gang LiGang Li, Gleb BeliakovGleb Beliakov, C Philippe, H Amiel, A Ugon
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.

History

Journal

ANZIAM Journal

Volume

52

Pagination

319 - 332

Location

Cambridge, England

Open access

  • Yes

ISSN

1446-1811

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2011, Cambridge University Press

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