Manual rat sleep classification in principal component space.
Manual rat sleep classification in principal component space.
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DOI：
10.1016/j.neulet.2009.11.052
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发表时间：
2010-01-18
影响因子：
2.5
通讯作者：
Subramanian T
中科院分区：
文献类型：
--
作者：
Gilmour TP;Fang J;Guan Z;Subramanian T
描述了一种使用主成分分析(PCA)对清醒、快速眼动(REM)睡眠或非REM(NREM)睡眠的大鼠睡眠记录进行评分的简单方法。主成分分析被用来将从每个历元提取的特征的维度降低到三个，然后将投影绘制在散点图中，其中集群在视觉上是明显的。然后直接手动选择簇，一次对整个录音进行分类。该方法在10个24小时睡眠的大鼠脑电(EEG)和肌电(EMG)记录中进行了测试。两个人类评分者执行传统的逐时代评分，与另外两个人类评分者使用新的主成分分析方法的分类进行盲目比较。总体评分者之间的中位数百分比一致性在93.7%到94.9%之间。科恩的卡帕系数中位数在0.890到0.909之间。PCA方法平均需要大约5分钟来对每个24小时记录进行分类。与传统的睡眠评分相比，这种方法具有良好的准确性和更少的时间，这表明该方法可能对睡眠研究有用。
A simple method is described for using principal component analysis (PCA) to score rat sleep recordings as awake, rapid-eye-movement (REM) sleep, or non-REM (NREM) sleep. PCA was used to reduce the dimensionality of the features extracted from each epoch to three, and the projections were then graphed in a scatterplot where the clusters were visually apparent. The clusters were then directly manually selected, classifying the entire recording at once. The method was tested in a set of ten 24-hour rat sleep electroencephalogram (EEG) and electromyogram (EMG) recordings. Classifications by two human raters performing traditional epoch-by-epoch scoring were blindly compared with classifications by another two human raters using the new PCA method. Overall inter-rater median percent agreements ranged between 93.7% and 94.9%. Median Cohen’s kappa coefficient ranged from 0.890 to 0.909. The PCA method on average required about five minutes for classification of each 24-hour recording. The combination of good accuracy and reduced time compared to traditional sleep scoring suggests that the method may be useful for sleep research.
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影响因子：
3
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通讯作者：
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