A visual aid for computer-based analysis of sleep-wake state in rats.

Computer-based sleep scoring systems are often calibrated by reference to a conventional visual analysis of electroencephalographic (EEG) and electromyographic (EMG) traces. However, these types of data place high demands on digital storage capacity which may limit the duration or feasibility of some studies. The present paper describes an approach to visual analysis that involves reconstruction of a waveform (termed a "pseudopolygram" (PPG)) from conditioned data derived from the EEG and EMG. The PPG is the sum of three sine waves, each of which has a distinct frequency (non-REM sleep (NREM), 3 Hz; rapid eye movement sleep (REM), 7 Hz and wakefulness (WAKE), 60 Hz) and amplitude proportional to the value of a state-specific scoring variable. Thus, in NREM sleep the wave depicting the NREM quantifier has high amplitude and produces a PPG with dominant 3 Hz frequency. In REM sleep, the wave depicting the REM quantifier has high amplitude and produces a PPG with a dominant 7 Hz frequency, and in WAKE the PPG is dominated by 60 Hz. Thus, the PPG provides a means for visual discrimination of the three behavioural states. Validation studies found an overall reliability of 94% compared with conventional visual analysis of EEG and EMG. The PPG was also found to remain accurate in rats after 24 h of sleep deprivation.

Design and validation of a computer-based sleep-scoring algorithm.

A computer-based sleep scoring algorithm was devised for the real time scoring of sleep-wake state in Wistar rats. Electroencephalogram (EEG) amplitude (microV(rms)) was measured in the following frequency bands: delta (delta; 1.5-6 Hz), theta (Theta; 6-10 Hz), alpha (alpha; 10.5-15 Hz), beta (beta; 22-30 Hz), and gamma (gamma; 35-45 Hz). Electromyographic (EMG) signals (microV(rms)) were recorded from the levator auris longus (neck) muscle, as this yielded a significantly higher algorithm accuracy than the spinodeltoid (shoulder) or temporalis (head) muscle EMGs (ANOVA; P=0.009). Data were obtained using either tethers (n=10) or telemetry (n=4). We developed a simple three-step algorithm that categorizes behavioural state as wake, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, based on thresholds set during a manually-scored 90-min preliminary recording. Behavioural state was assigned in 5-s epochs. EMG amplitude and ratios of EEG frequency band amplitudes were measured, and compared with empirical thresholds in each animal.STEP 1: EMG amplitude greater than threshold? Yes: "active" wake, no: sleep or "quiet" wake. STEP 2: EEG amplitude ratio (delta x alpha)/(beta x gamma) greater than threshold? Yes: NREM, no: REM or "quiet" wake. STEP 3: EEG amplitude ratio Theta(2)/(delta x alpha) greater than threshold? Yes: REM, no: "quiet" wake. The algorithm was validated with one, two and three steps. The overall accuracy in discriminating wake and sleep (NREM and REM combined) using step one alone was found to be 90.1%. Overall accuracy using the first two steps was found to be 87.5% in scoring wake, NREM and REM sleep. When all three steps were used, overall accuracy in scoring wake, NREM and REM sleep was determined to be 87.9%. All accuracies were derived from comparisons with unequivocally-scored epochs from four 90-min recordings as defined by an experienced human rater. The algorithms were as reliable as the agreement between three human scorers (88%).