Manual rat sleep classification in principal component space.

Neuroscience Letters
Timothy P GilmourThyagarajan Subramanian

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.

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Citations

Sep 3, 2011·Journal of Neuroscience Methods·Kirsi-Marja RytkönenTarja Porkka-Heiskanen
Apr 30, 2013·Genes to Cells : Devoted to Molecular & Cellular Mechanisms·Genshiro A SunagawaHiroki R Ueda
Jul 11, 2014·Journal of Neurophysiology·Daniel C Haggerty, Daoyun Ji
Dec 10, 2019·NPJ Parkinson's Disease·Vishakh IyerThyagarajan Subramanian
Apr 19, 2020·Proceedings of the National Academy of Sciences of the United States of America·Timothy D WigginLeslie C Griffith

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