Manual rat sleep classification in principal component space

Timothy P. Gilmour, Jidong Fang, Zhiwei Guan, Thyagarajan Subramanian

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)97-101
Number of pages5
JournalNeuroscience letters
Volume469
Issue number1
DOIs
StatePublished - Jan 18 2010

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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