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. 2009 Oct 30;184(1):10-8.
doi: 10.1016/j.jneumeth.2009.07.009. Epub 2009 Jul 15.

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

Brooks A Gross  1 , Christine M Walsh, Apurva A Turakhia, Victoria Booth, George A Mashour, Gina R Poe
Affiliations

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

Brooks A Gross et al. J Neurosci Methods. .

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 sigma, delta, and theta frequency bands of an electroencephalographic (EEG) signal, along with the delta/theta ratio and sigmaxtheta, 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.

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Figures

Figure 1
Figure 1
Sleep Scorer graphical user interface used for manually scoring and correcting files. The top 2 horizontal panels show example EMG and EEG signals, respectively. The third and fourth panels show the EEG signal in panel 2 using different filter ranges.
Figure 2
Figure 2
Process flow for data analysis prior to importing to Auto-Scorer.
Figure 3
Figure 3
Main graphical user interface for the Auto-Scorer. Data files are loaded and thresholds are entered for auto-scoring.
Figure 4
Figure 4
User interface for determining rule-based threshold settings and visualizing auto-scored states. (a) 3D graph of states for 10 s epochs based on EMG power, sigma power*theta power, and the ratio of delta power to theta power. (b) Epochs graphed by delta, theta, and sigma powers. (c) GUI for manipulating the graphs in (a) and (b). The scored states of the epochs are distinguished using 5 distinct colors: yellow = AW, green = QW, blue = QS, turquoise = TR, and red = RE. In addition, the unscored epochs for the recording are plotted as light gray dots.
Figure 5
Figure 5
a–f Threshold settings illustrated by black lines on 2D graphs of a user’s training data set. All users followed the guidelines described in section 2.3. The scored states of the epochs are distinguished using 5 distinct colors: yellow = AW, green = QW, blue = QS, turquoise = TR, and red = RE. In addition, the as yet unscored epochs from the recording are plotted as light gray dots.
Figure 6
Figure 6
A schematic diagram of the Boolean logic used in the Auto-Scorer algorithm is shown. Filled parallelograms represent the assigned state for the 10 s epoch. Diamonds represent logical decisions with true/false passing out the right/bottom side of the diamond. Rectangles represent a step when the state was changed. Heavy outlined parallelograms represent final state assignment.
Figure 7
Figure 7
Average pair-wise agreement for the 3 users and Auto-Scorer for 12 experiments. Error bars represent +/−1 standard error of the mean.
See this image and copyright information in PMC

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