The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG
Abstract
Increasing depth of sleep corresponds to an increasing gain in the neuronal feedback loops that generate the low-frequency (slow-wave) electroencephalogram (EEG). We derived the maximum-likelihood estimator of the feedback gain and applied it to quantify sleep depth. The estimator computes the fraction (0%-100%) of the current slow wave which continues in the near-future (0.02 s later) EEG. Therefore, this percentage was dubbed slow-wave microcontinuity (SW%). It is not affected by anatomical parameters such as skull thickness, which can considerably bias the commonly used slow-wave power (SWP). In our study, both of the estimators SW% and SWP were monitored throughout two nights in 22 subjects. Each subject took temazepam (a benzodiazepine) on one of the two nights. Both estimators detected the effects of age, temazepam, and time of night on sleep. Females were found to have twice the SWP of males, but no gender effect on SW% was found. This confirms earlier reports that gender affects SWP but not sleep depth. Subjectively assessed differences in sleep quality between the nights were correlated to differences in SW%, not in SWP. These results demonstrate that slow-wave microcontinuity, being based on a physiological model of sleep, reflects sleep depth more closely than SWP does.
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Citations
Cites
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564
Cited by
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG
Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376
Zhu, G., Li, Y., & Wen, P. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2303991 · Cited by 384
ISRUC-Sleep: A comprehensive public dataset for sleep researchers
Khalighi, S., Sousa, T., Santos, J. M., & Nunes, U. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.013 · Cited by 367
Metadata
- Authors
- Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2000
- Volume
- 47
- Issue
- 9
- Pages
- 1185-1194
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2144691514
- Cited by
- 953
- References
- 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L. (2000). Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.867928
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