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

ABSTRACT: 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 t

SUBMITTER: Brodersen PJN 

PROVIDER: S-EPMC10824458 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature