K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Vu, Huy Quan, Li, Gang, Sukhorukova, Nadezda S., Beliakov, Gleb, Liu, Shaowu, Philippe, Carole, Amiel, Helene and Ugon, Adrien 2012, K-Complex Detection Using a Hybrid-Synergic Machine Learning Method, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 42, no. 6, pp. 1478-1490, doi: 10.1109/TSMCC.2012.2191775.

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Title K-Complex Detection Using a Hybrid-Synergic Machine Learning Method
Author(s) Vu, Huy Quan
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Sukhorukova, Nadezda S.
Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
Liu, Shaowu
Philippe, Carole
Amiel, Helene
Ugon, Adrien
Journal name IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Volume number 42
Issue number 6
Start page 1478
End page 1490
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, NJ
Publication date 2012-04-26
ISSN 1094-6977
Keyword(s) EEG
Multi-instance learning (MIL)
Sleep disorder
Summary Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.
Language eng
DOI 10.1109/TSMCC.2012.2191775
Field of Research 080109 Pattern Recognition and Data Mining
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30046979

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