The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal
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.
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Cited by
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
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Automated sleep scoring: A review of the latest approaches
Fiorillo, L., Puiatti, A., Papandrea, M., Ratti, P.-L., Favaro, P., Roth, C., Bargiotas, P., Bassetti, C. L., & Faraci, F. D. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.07.007 · Cited by 284
Hassan, A. R., & Bhuiyan, M. I. H. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.09.002 · Cited by 273
Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor
Nakamura, T., Alqurashi, Y. D., Morrell, M. J., & Mandic, D. P. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2911423 · Cited by 147
Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis
Shahin, M., Ahmed, B., Hamida, S. T.-B., Mulaffer, F. L., Glos, M., & Penzel, T. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2650199 · Cited by 96
Metadata
- Authors
- Zhu, G., Li, Y., & Wen, P.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2014-11
- Volume
- 18
- Issue
- 6
- Pages
- 1813-1821
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2088478798
- Cited by
- 384
- References
- 35
- FWCI
- 10.21
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhu, G., Li, Y., & Wen, P. (2014). Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2014.2303991
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