Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions

by Paul J. N. Brodersen, Hannah Alfonsa, Lukas B. Krone, Cristina Blanco-Duque, Angus S. Fisk, Sarah J. Flaherty, Mathilde C. C. Guillaumin, Yi-Ge Huang, Martin C. Kahn, Laura E. McKillop, Linus Milinski, Lewis Taylor, Christopher W. Thomas, Tomoko Yamagata, Russell G. Foster, Vladyslav V. Vyazovskiy, Colin J. Akerman Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate" —a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remar kable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is t...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research