EEG sleep stages classification based on time domain features and structural graph similarity
Article
Article Title | EEG sleep stages classification based on time domain features and structural graph similarity |
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ERA Journal ID | 5044 |
Article Category | Article |
Authors | Diykh, Mohammed (Author), Li, Yan (Author) and Wen, Peng (Author) |
Journal Title | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Journal Citation | 24 (11), pp. 1159-1168 |
Number of Pages | 11 |
Year | 2016 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1534-4320 |
1558-0210 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TNSRE.2016.2552539 |
Web Address (URL) | https://ieeexplore.ieee.org/document/7452628 |
Abstract | Abstract-The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high accuracy of classification. In this paper, a novel method is proposed, which uses the statistical features in time domain and the structural graph similarity combined with k-means (SGSKM) to identify six sleep stages using a single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted and different sets of features are forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relation between sleep stages and the time domain features of the EEG data. The experimental results show that the proposed method yields better classification results compared to other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by the proposed method. |
Keywords | EEG signal; structural graph similarity; time domain features; sleep stages |
ANZSRC Field of Research 2020 | 460103. Applications in life sciences |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q37zw/eeg-sleep-stages-classification-based-on-time-domain-features-and-structural-graph-similarity
2013
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