Complex networks approach for EEG signal sleep stages classification
Article
Article Title | Complex networks approach for EEG signal sleep stages classification |
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ERA Journal ID | 17852 |
Article Category | Article |
Authors | Diykh, Mohammed (Author) and Li, Yan (Author) |
Journal Title | Expert Systems with Applications |
Journal Citation | 63, pp. 241-248 |
Number of Pages | 8 |
Year | 30 Nov 2016 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0957-4174 |
1873-6793 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2016.07.004 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417416303463 |
Abstract | Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30 s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the com- plex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders. |
Keywords | Electroencephalography; Complex networks; Sleep stages; Statistical features |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
469999. Other information and computing sciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3y5q/complex-networks-approach-for-eeg-signal-sleep-stages-classification
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