Manual rat sleep classification in principal component space.
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.
Gilmour
Timothy P
TP
The Pennsylvania State University College of Medicine, Hershey, PA, USA. timgilmour@psu.edu
Fang
Jidong
J
Guan
Zhiwei
Z
Subramanian
Thyagarajan
T
D000818:Animals; D004569:Electroencephalography; D004576:Electromyography; D006801:Humans; D008297:Male; D025341:Principal Component Analysis; D051381:Rats; D017207:Rats, Sprague-Dawley; D012890:Sleep / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology; D014851:Wakefulness