Journal ArticleDOI
Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG
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TLDR
Slow-wave microcontinuity, being based on a physiological model of sleep, reflects sleep depth more closely than SWP does, and confirms earlier reports that gender affects SWP but not sleep depth.Abstract:
Increasing depth of sleep corresponds to an increasing gain in the neuronal feedback loops that generate the low-frequency (slow-wave) electroencephalogram (EEG). The authors 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 microconfinuity (SW%). It is not affected by anatomical parameters such as skull thickness, which can considerably bias the commonly used slow-wave power (SWP). In the authors' 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.read more
Citations
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TL;DR: In this paper, the authors present a review of 154 studies that apply deep learning to EEG, published between 2010 and 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring.
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References
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Journal ArticleDOI
Thalamocortical oscillations in the sleeping and aroused brain
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