Open-Source Algorithm for Automated Vigilance State Classification Using Single-Channel Electroencephalogram in Rodents
Year: 2025
Type: article
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 preprocessing and voting criteria improving REM detection. Validation on datasets from 10 rats and 10 mice under standard conditions showed sleep-wake state detection accuracies of 92% and 93%, respectively, closely matching manual scoring and comparable to existing methods. REM sleep detection accuracies of 89% (mice) and 91% (rats) align with previously reported (85-90%). Processing a full day of EEG data within several minutes, the algorithm is advantageous for large-scale and longitudinal studies. Its open-source design, flexibility, and scalability make it a robust, efficient tool for automated rodent sleep scoring, advancing research in standard experimental conditions, including aging and sleep deprivation.
Source: Sensors
DOI: 10.3390/s25030921
Authors: Anton Saevskiy, Natalia Suntsova, Peter Kosenko, Md Noor Alam, Andrey Kostin
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G5181984934
OpenAlex ID: W4407143127