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