Ruigt G S, Van Proosdij J N, Van Delft A M
CNS Pharmacology R and D Labs, Organon International B.V., Oss, The Netherlands.
Electroencephalogr Clin Neurophysiol. 1989 Jul;73(1):52-63. doi: 10.1016/0013-4694(89)90019-9.
An automatic rat sleep classification system is described which records and analyses bioelectrical signals from 32 rats over extended periods of time. At present this system is used routinely for the screening of drug effects on sleep. The analysis is based on 3 signals, the parieto-occipital EEG, nuchal EMG and a movement indicator signal. The on-line analysis is done per epoch of 2 sec and involves power spectral analysis of the EEG and rectification and integration of the EMG and movement signals. The automatic sleep staging into 6 stages (active and quiet waking; quiet, deep, pre-REM and REM sleep) is performed off-line. Parameters derived from a discriminant analysis of visually scored tracings of individual rats constitute the basis for the automatic scoring procedure. The movement index is used to discriminate between active and quiet waking, while the use of the EMG level improves the separation of waking and REM sleep. After the construction of hypnograms from these computer scorings a set of parameters can be extracted which characterizes the sleep-waking behavior of each individual rat. These parameters are then used to compare statistically the 2-4 treatment groups which make up each experiment of 32 rats. Experimental validation of the system is reported in an accompanying paper.
本文描述了一种自动大鼠睡眠分类系统，该系统可长时间记录和分析32只大鼠的生物电信号。目前，该系统常规用于筛选药物对睡眠的影响。分析基于三种信号，即顶枕部脑电图（EEG）、颈部肌电图（EMG）和一个运动指示信号。在线分析以2秒为一个时段进行，包括对EEG进行功率谱分析以及对EMG和运动信号进行整流和积分。自动睡眠分期为6个阶段（主动和安静觉醒；安静、深度、快速眼动睡眠前和快速眼动睡眠）是离线进行的。从对个别大鼠的视觉评分记录进行判别分析得出的参数构成了自动评分程序的基础。运动指数用于区分主动和安静觉醒，而EMG水平的使用则改善了觉醒和快速眼动睡眠的区分。根据这些计算机评分构建睡眠图后，可以提取一组表征每只大鼠睡眠-觉醒行为的参数。然后使用这些参数对构成每组32只大鼠实验的2-4个治疗组进行统计学比较。该系统的实验验证在随附的论文中报告。
