Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal
Article
Article Title | Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal |
---|---|
ERA Journal ID | 13572 |
Article Category | Article |
Authors | Zhu, Guohun (Author), Li, Yan (Author) and Wen, Peng (Paul) (Author) |
Journal Title | IEEE Journal of Biomedical and Health Informatics |
Journal Citation | 18 (6), pp. 1813-1821 |
Number of Pages | 9 |
Year | 2014 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1089-7771 |
1558-0032 | |
2168-2194 | |
2168-2208 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/JBHI.2014.2303991 |
Web Address (URL) | https://ieeexplore.ieee.org/document/6733276 |
Abstract | The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P(k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of $P$ $(k)$ from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse. |
Keywords | classification; degree distribution (DD); difference visibility graph (DVG); electroencephalogram (EEG); single channel |
ANZSRC Field of Research 2020 | 400308. Medical devices |
400607. Signal processing | |
460103. Applications in life sciences | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Southern Queensland |
https://research.usq.edu.au/item/q2y82/analysis-and-classification-of-sleep-stages-based-on-difference-visibility-graphs-from-a-single-channel-eeg-signal
1858
total views7
total downloads4
views this month0
downloads this month