The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models
Abstract
In this paper, we propose an automatic sleep-scoring method combining multiscale entropy (MSE) and autoregressive (AR) models for single-channel EEG and to assess the performance of the method comparatively with manual scoring based on full polysomnograms. This is the first time that MSE has ever been applied to sleep scoring. All-night polysomnograms from 20 healthy individuals were scored using the Rechtschaffen and Kales rules. The developed method analyzed the EEG signals of C3-A2 for sleep staging. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. A total of 8480 30-s sleep EEG epochs were measured and used for performance evaluation. The epoch-by-epoch comparison was made by classifying the EEG epochs into five states (Wake/REM/S1/S2/SWS) by the proposed method and manual scoring. The overall sensitivity and kappa coefficient of MSE alone are 76.9% and 0.65, respectively. Moreover, the overall sensitivity and kappa coefficient of our proposed method of integrating MSE, AR models, and a smoothing process can reach the sensitivity level of 88.1% and 0.81, respectively. Our results show that MSE is a useful and representative feature for sleep staging. It has high accuracy and good home-care applicability because a single EEG channel is used for sleep staging.
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Cites
Practical Statistics for Medical Research
Altman, D. G. (1990) · doi:10.1201/9780429258589 · Cited by 10,668
Epidemiology of insomnia: what we know and what we still need to learn
Ohayon, M. M. (2002) · Sleep Medicine Reviews · doi:10.1053/smrv.2002.0186 · Cited by 3,743 · cited 1×
“The prevalence of insomnia symptoms without restrictive criteria is approximately 33% in the general population [1].”
BackgroundMultiscale Entropy Analysis of Complex Physiologic Time Series
Costa, M., Goldberger, A. L., & Peng, C.-K. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.068102 · Cited by 3,207
Insights from studying human sleep disorders
Mahowald, M. W., & Schenck, C. H. (2005) · Nature · doi:10.1038/nature04287 · Cited by 330
Cited by
Zhu, G., Li, Y., & Wen, P. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2303991 · Cited by 384
A comparative review on sleep stage classification methods in patients and healthy individuals
Boostani, R., Karimzadeh, F., & Nami, M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.004 · Cited by 316
Ensemble SVM Method for Automatic Sleep Stage Classification
Alickovic, E., & Subasi, A. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2799059 · Cited by 313
An ensemble system for automatic sleep stage classification using single channel EEG signal
Koley, B., & Dey, D. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.09.012 · Cited by 291
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
Metadata
- Authors
- Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., & Wang, Y.-H.
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2012-06
- Volume
- 61
- Issue
- 6
- Pages
- 1649-1657
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2102376611
- Cited by
- 303
- References
- 4
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., & Wang, Y.-H. (2012). Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2012.2187242
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