title: Automated sleep scoring in rats and mice using the naive Bayes classifier.
abstract: We describe a new simple MATLAB-based method for automated scoring of rat and mouse sleep using the naive Bayes classifier. This method is highly sensitive resulting in overall auto-rater agreement of 93%, comparable to an inter-rater agreement between two human scorers (92%), with high sensitivity and specificity values for wake (94% and 96%), NREM sleep (94% and 97%) and REM sleep (89% and 97%) states. In addition to baseline sleep-wake conditions, the performance of the naive Bayes classifier was assessed in sleep deprivation and drug infusion experiments, as well as in aged and transgenic animals using multiple EEG derivations. 24-h recordings from 30 different animals were used, with approximately 5% of the data manually scored as training data for the classification algorithm.
authors: Department of Physiology, Institute of Biomedicine, PO Box 63, 00014 University of Helsinki, Helsinki, Finland. kirsi-marja.rytkonen@helsinki.fi
authors: Kirsi-Marja
authors: KM
authors: Rytkönen
authors: Jukka
authors: J
authors: Zitting
authors: Tarja
authors: T
authors: Porkka-Heiskanen
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D001499:Bayes Theorem; D004569:Electroencephalography; D004576:Electromyography; D051379:Mice; D017286:Polysomnography / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology*