Literature DB >> 19703489

Automated analysis of sleep-wake state in rats.

Richard Stephenson1, Aimee M Caron, Daniel B Cassel, Jennifer C Kostela.   

Abstract

A fully automated computer-based sleep scoring system is described and validated for use in rats. The system was designed to emulate visual sleep scoring by using the same basic features of the electroencephalogram (EEG) and electromyogram (EMG), and a similar set of decision-making rules. State indices are calculated for each 5s epoch by combination of amplitudes (microVrms) of 6 filtered EEG frequency bands (EEGlo, d.c.-1.5Hz; delta, 1.5-6Hz; theta, 6-9Hz; alpha, 10.5-15Hz; beta, 22-30Hz; gamma, 35-45Hz; Sigma(EEG)=delta+theta+alpha+beta+gamma) and EMG (10-100Hz) yielding dimensionless ratios: WAKE-index=(EMGxgamma)/theta; NREM-index=(deltaxalpha)/gamma(2); REM-index=theta(3)/(deltaxalphaxEMG); artifact-index=[(2xEEG(lo))+beta]*(gamma/Sigma(EEG)). The index values are re-scaled and normalized, thereby dispensing with the need for animal-specific threshold values. The system was validated by direct comparison with visually scored data in 9 rats. Overall, the computer and visual scores were 96% concordant, which is similar to inter-rater agreement in visual scoring. False-positive error rate was <5%. A re-test protocol in 7 rats confirmed the long-term stability of the system in studies lasting 5 weeks. The system was implemented and further validated in a study of sleep architecture in 7 rats under a 12:12h LD cycle.

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Year:  2009        PMID: 19703489     DOI: 10.1016/j.jneumeth.2009.08.014

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  13 in total

1.  Statistical properties of sleep-wake behavior in the rat and their relation to circadian and ultradian phases.

Authors:  Richard Stephenson; Svetlana Famina; Aimee M Caron; Joonbum Lim
Journal:  Sleep       Date:  2013-09-01       Impact factor: 5.849

2.  Behavioral sleep-wake homeostasis and EEG delta power are decoupled by chronic sleep restriction in the rat.

Authors:  Richard Stephenson; Aimee M Caron; Svetlana Famina
Journal:  Sleep       Date:  2015-05-01       Impact factor: 5.849

3.  Energy expenditure is affected by rate of accumulation of sleep deficit in rats.

Authors:  Aimee M Caron; Richard Stephenson
Journal:  Sleep       Date:  2010-09       Impact factor: 5.849

4.  Unsupervised online classifier in sleep scoring for sleep deprivation studies.

Authors:  Paul-Antoine Libourel; Alexandra Corneyllie; Pierre-Hervé Luppi; Guy Chouvet; Damien Gervasoni
Journal:  Sleep       Date:  2015-05-01       Impact factor: 5.849

5.  Manual rat sleep classification in principal component space.

Authors:  Timothy P Gilmour; Jidong Fang; Zhiwei Guan; Thyagarajan Subramanian
Journal:  Neurosci Lett       Date:  2009-11-26       Impact factor: 3.046

6.  Automatic detection of periods of slow wave sleep based on intracranial depth electrode recordings.

Authors:  Chrystal M Reed; Kurtis G Birch; Jan Kamiński; Shannon Sullivan; Jeffrey M Chung; Adam N Mamelak; Ueli Rutishauser
Journal:  J Neurosci Methods       Date:  2017-02-24       Impact factor: 2.390

7.  Sleep scoring made easy-Semi-automated sleep analysis software and manual rescoring tools for basic sleep research in mice.

Authors:  M Kreuzer; S Polta; J Gapp; C Schuler; E F Kochs; T Fenzl
Journal:  MethodsX       Date:  2015-04-24

8.  Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated.

Authors:  Laurens Reinke; Johannes H van der Hoeven; Michel Jam van Putten; Willem Dieperink; Jaap E Tulleken
Journal:  Crit Care       Date:  2014-04-09       Impact factor: 9.097

9.  Investigation of sleep-wake rhythm in non-human primates without restraint during data collection.

Authors:  Akiyoshi Ishikawa; Keita Sakai; Takehiro Maki; Yuri Mizuno; Kimie Niimi; Yasuhiro Oda; Eiki Takahashi
Journal:  Exp Anim       Date:  2016-10-18

10.  An automated sleep-state classification algorithm for quantifying sleep timing and sleep-dependent dynamics of electroencephalographic and cerebral metabolic parameters.

Authors:  Michael J Rempe; William C Clegern; Jonathan P Wisor
Journal:  Nat Sci Sleep       Date:  2015-09-01
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