Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice.

ABSTRACT: We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis. Our analysis revealed significant variances in these complexities, not only within the specific theta and delta bands but across a wide frequency spectrum. Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spe

SUBMITTER: Furutani N 

PROVIDER: S-EPMC11760340 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature