Literature DB >> 11008419

Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG.

B Kemp1, A H Zwinderman, B Tuk, H A Kamphuisen, J J Oberyé.   

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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Year:  2000        PMID: 11008419     DOI: 10.1109/10.867928

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  38 in total

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Review 4.  Circuit-based interrogation of sleep control.

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5.  Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.

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6.  Measures of entropy and complexity in altered states of consciousness.

Authors:  D M Mateos; R Guevara Erra; R Wennberg; J L Perez Velazquez
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7.  Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

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Review 8.  Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review.

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Journal:  Sleep Disord       Date:  2015-07-21

9.  Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

Authors:  Huy Phan; Fernando Andreotti; Navin Cooray; Oliver Y Chen; Maarten De Vos
Journal:  IEEE Trans Biomed Eng       Date:  2018-10-22       Impact factor: 4.538

10.  Assessment of Sleep Spindle Density among Genetically Positive Spinocerebellar Ataxias Types 1, 2, and 3 Patients.

Authors:  Doniparthi Venkata Seshagiri; Ragasudha Botta; Arun Sasidharan; Pramod Kumar Pal; Sanjeev Jain; Ravi Yadav; Bindu M Kutty
Journal:  Ann Neurosci       Date:  2018-03-08
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