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Open-source logic-based automated sleep scoring software using electrophysiological recordings in rats

Authors :
Gross, Brooks A.
Walsh, Christine M.
Turakhia, Apurva A.
Booth, Victoria
Mashour, George A.
Poe, Gina R.
Source :
Journal of Neuroscience Methods. Oct2009, Vol. 184 Issue 1, p10-18. 9p.
Publication Year :
2009

Abstract

Abstract: Manual state scoring of physiological recordings in sleep studies is time-consuming, resulting in a data backlog, research delays and increased personnel costs. We developed MATLAB-based software to automate scoring of sleep/waking states in rats, potentially extendable to other animals, from a variety of recording systems. The software contains two programs, Sleep Scorer and Auto-Scorer, for manual and automated scoring. Auto-Scorer is a logic-based program that displays power spectral densities of an electromyographic (EMG) signal and σ, δ, and θ frequency bands of an electroencephalographic (EEG) signal, along with the δ/θ ratio and σ × θ, for every epoch. The user defines thresholds from the training file state definitions which the Auto-Scorer uses with logic to discriminate the state of every epoch in the file. Auto-Scorer was evaluated by comparing its output to manually scored files from 6 rats under 2 experimental conditions by 3 users. Each user generated a training file, set thresholds, and auto-scored the 12 files into 4 states (waking, non-REM, transition-to-REM, and REM sleep) in 1/4 the time required to manually score the file. Overall performance comparisons between Auto-Scorer and manual scoring resulted in a mean agreement of 80.24±7.87%, comparable to the average agreement among 3 manual scorers (83.03±4.00%). There was no significant difference between user–user and user–Auto-Scorer agreement ratios. These results support the use of our open-source Auto-Scorer, coupled with user review, to rapidly and accurately score sleep/waking states from rat recordings. [Copyright &y& Elsevier]

Details

Language :
English
ISSN :
01650270
Volume :
184
Issue :
1
Database :
Academic Search Index
Journal :
Journal of Neuroscience Methods
Publication Type :
Academic Journal
Accession number :
44416071
Full Text :
https://doi.org/10.1016/j.jneumeth.2009.07.009