The Pennsylvania State University College of Medicine, Hershey, PA, USA.
Neurosci Lett. 2010 Jan 18;469(1):97-101. doi: 10.1016/j.neulet.2009.11.052. Epub 2009 Nov 26.
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-h 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 5 min for classification of each 24-h recording. The combination of good accuracy and reduced time compared to traditional sleep scoring suggests that the method may be useful for sleep research.
描述了一种使用主成分分析（PCA）对大鼠睡眠记录进行评分的简单方法，可将其分为清醒、快速眼动（REM）睡眠或非快速眼动（NREM）睡眠。PCA 用于将从每个时段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，其中聚类是显而易见的。然后直接手动选择聚类，一次性对整个记录进行分类。该方法在一组十只 24 小时大鼠脑电图（EEG）和肌电图（EMG）记录中进行了测试。由两位人类评分员进行的传统时段评分的分类与另外两位人类评分员使用新的 PCA 方法进行的分类进行了盲法比较。总体内部评分员中位数百分比一致性在 93.7%到 94.9%之间。Cohen's kappa 系数中位数在 0.890 到 0.909 之间。PCA 方法平均需要大约 5 分钟对每个 24 小时的记录进行分类。与传统睡眠评分相比，该方法具有良好的准确性和减少的时间，这表明该方法可能对睡眠研究有用。
