Manual rat sleep classification in principal component space

NEUROSCIENCE LETTERS

卷:469

Article

Gilmour, Timothy P.¹  Fang, Jidong¹  Guan, Zhiwei¹  Subramanian, Thyagarajan¹ 

[1] Penn State Univ, Coll Med, Hershey, PA USA

关键词: Rat;    Sleep scoring;    Principal component analysis;    Electroencephalogram;    Electromyogram;   

DOI  :  10.1016/j.neulet.2009.11.052

来源: Elsevier

【 摘 要 】

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. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

【 授权许可】

Free