- Title
- K-complex detection using a hybrid-synergic machine learning method
- Creator
- Vu, Huy Quan; Li, Gang; Sukhorukova, Nadezda; Beliakov, Gleb; Liu, Shaowu; Philippe, Carole; Amiel, Hélène; Ugon, Adrien
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61234
- Identifier
- vital:4879
- Identifier
-
https://doi.org/10.1109/TSMCC.2012.2191775
- Identifier
- ISSN:1094-6977
- Abstract
- 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. © 1998-2012 IEEE.
- Relation
- IEEE Transactions on Systems, Man and Cybernetics Part C : Applications and Reviews Vol. 42, no. 6 (2012), p. 1478-1490
- Rights
- Copyright 1998-2012 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; 17 Psychology and Cognitive Sciences; 09 Engineering; EEG; K-complex; Multi-instance learning (MIL); Sleep disorder
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