Van Gelder R N, Edgar D M, Dement W C
Sleep Disorders and Research Center, Stanford University School of Medicine, California 94304.
Sleep. 1991 Feb;14(1):48-55. doi: 10.1093/sleep/14.1.48.
Long-term circadian studies of sleep and wakefulness in rodents have been hindered by the labor required to analyze long polygraph records. To expedite such studies, we have designed and implemented SCORE, a microcomputer-based real-time sleep scoring system for rodents. The electroencephalograph is digitized in 10-s epochs at 100 Hz. Frequency and amplitude information from the waveform are extracted into a 48-dimension vector that is then compared to previously taught vectors representing the canonical features of four arousal states: wakefulness, theta-dominated wakefulness, rapid eye movement (REM) sleep, and nonREM (NREM) sleep. Match values are assigned for each state to each epoch; after excluding states based on wheel-running or drinking activity data, the nonexcluded state with the best match value for the epoch is scored. Analysis of over 23,000 epochs for four mice yielded an overall agreement of 94.0% between two human scorers and the program, compared with a 94.5% agreement between the two human scorers. The SCORE algorithm matched the human concensus best for wakefulness (97.8%) and NREM sleep (94.7%), but was lower for REM sleep (75.2%) and theta-dominated wakefulness (83.3%). Most errors in scoring of REM sleep were in close temporal proximity to human-scored REM epochs. SCORE is capable of scoring arousal states for eight animals simultaneously in real time on a standard IBM PC equipped with a commercially available analog-to-digital conversion board, and should considerably facilitate the performance of long-term studies of sleep and wakefulness in the rodent.
长期对啮齿动物睡眠和清醒状态进行昼夜节律研究一直受到分析长时间多导记录所需人力的阻碍。为了加快此类研究，我们设计并实施了SCORE，这是一种基于微型计算机的啮齿动物实时睡眠评分系统。脑电图以100Hz的频率在10秒的时间段内进行数字化处理。波形的频率和幅度信息被提取到一个48维向量中，然后与先前教授的代表四种觉醒状态（清醒、θ波主导的清醒、快速眼动（REM）睡眠和非快速眼动（NREM）睡眠）典型特征的向量进行比较。为每个时间段的每种状态分配匹配值；根据转轮或饮水活动数据排除某些状态后，为该时间段匹配值最佳的未排除状态进行评分。对四只小鼠的23000多个时间段进行分析，结果显示两名人类评分者与该程序之间的总体一致性为94.0%，而两名人类评分者之间的一致性为94.5%。SCORE算法在清醒状态（97.8%）和NREM睡眠状态（94.7%）下与人类共识的匹配度最高，但在REM睡眠状态（75.2%）和θ波主导的清醒状态（83.3%）下较低。REM睡眠评分中的大多数错误与人类评分的REM时间段在时间上非常接近。SCORE能够在配备市售模数转换板的标准IBM个人电脑上同时实时对八只动物的觉醒状态进行评分，并且应该会大大促进对啮齿动物睡眠和清醒状态的长期研究。
