Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents.

ABSTRACT: Accurate identification of sleep stages is essential for understanding sleep physiology and its role in neurological and behavioral research. Manual scoring of polysomnographic data, while reliable, is time-intensive and prone to variability. This study presents a novel Python-based algorithm for automated vigilance state scoring using single-channel electroencephalogram (EEG) recordings from rats and mice. The algorithm employs artifact processing, multi-band frequency analysis, and Gaussian mixture model (GMM)-based clustering to classify wakefulness, non-rapid, and rapid eye movement sleep (NREM and REM sleep, respectively). Combining narrow and broad frequency bands across the delta, theta, and sigma ranges, it uses a majority voting system to enhance accuracy, with tailored preprocess

SUBMITTER: Saevskiy A 

PROVIDER: S-EPMC11820057 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature