EEG gamma frequency and sleep-wake scoring in mice: Comparing two types of supervised classifiers
EEG gamma frequency and sleep-wake scoring in mice: Comparing two types of supervised classifiers
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DOI：
10.1016/j.brainres.2010.01.069
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发表时间：
2010-03-31
期刊：
影响因子：
2.9
通讯作者：
Draguhn, Andreas
中科院分区：
文献类型：
--
作者：
Brankack, Jurij;Kukushka, Valeriy I.;Draguhn, Andreas
人们对睡眠研究的兴趣越来越大，对转基因动物昼夜节律筛查的需求也越来越大。这需要可靠的睡眠阶段评分程序。然而，目前的解决方案缺乏灵活的适应实验条件和不可靠的阶段判别变量的选择。在自由活动的C57 BL/6小鼠中记录EEG，并使用不同的频率变量集进行分析。参数包括传统的功率谱密度函数以及周期振幅分析。手动分期进行了比较，两个不同的监督分类器，线性判别分析（LDA）和分类树的性能。在REM（快速眼动）睡眠和清醒期间，伽马活动特别高。73个变量中有4个对睡眠-觉醒阶段分离最有效：上伽马、δ和上θ频带的振幅和颈部肌肉EMG。使用小的训练数据集，LDA产生更好的结果比分类树或传统的阈值公式。改变时期持续时间（4到10秒）对性能的影响很小，8到10秒产生最佳结果。在REM睡眠期间的伽马和上θ活动对于睡眠觉醒阶段分离特别有用。线性判别分析在有监督的自动分期程序中表现最好。可靠的半自动睡眠评分与LDA大大减少了分析时间。(C)2010爱思唯尔有限公司版权所有。
There is growing interest in sleep research and increasing demand for screening of circadian rhythms in genetically modified animals. This requires reliable sleep stage scoring programs. Present solutions suffer, however, from the lack of flexible adaptation to experimental conditions and unreliable selection of stage-discriminating variables. EEG was recorded in freely moving C57BL/6 mice and different sets of frequency variables were used for analysis. Parameters included conventional power spectral density functions as well as period-amplitude analysis. Manual staging was compared with the performance of two different supervised classifiers, linear discriminant analysis (LDA) and Classification Tree. Gamma activity was particularly high during REM (rapid eye movements) sleep and waking. Four out of 73 variables were most effective for sleep wake stage separation: amplitudes of upper gamma-, delta- and upper theta-frequency bands and neck muscle EMG. Using small sets of training data, LDA produced better results than Classification Tree or a conventional threshold formula. Changing epoch duration (4 to 10 s) had only minor effects on performance with 8 to 10 s yielding the best results. Gamma and upper theta activity during REM sleep is particularly useful for sleep wake stage separation. Linear discriminant analysis performs best in supervised automatic staging procedures. Reliable semiautomatic sleep scoring with LDA substantially reduces analysis time. (C) 2010 Elsevier B.V. All rights reserved.