Fully automatic REM sleep stage-specific intervention systems using single EEG in mice. (January 2023) - Koyanagi, Iyo; Tezuka, Taro; Yu, Jiahui; Srinivasan, Sakthivel; Naoi, Toshie; Yasugaki, Shinnosuke; Nakai, Ayaka; Taniguchi, Shimpei; Hayashi, Yu; Nakano, Yasushi; Sakaguchi, Masanori
Abstract: Sleep stage-specific intervention is widely used to elucidate the functions of sleep and their underlying mechanisms. For this intervention, it is imperative to accurately classify rapid-eye-movement (REM) sleep. However, the proof of fully automatic real-time REM sleep classification in vivo has not been obtained in mice. Here, we report the in vivo implementation of a system that classifies sleep stages in real-time from a single-channel electroencephalogram (EEG). It enabled REM sleep-specific intervention with 90 % sensitivity and 86 % precision without prior configuration to each mouse. We further derived systems capable of classification with higher frequency sampling and time resolution. This attach-and-go sleep staging system provides a fully automatic accurate and scalable tool for investigating the functions of sleep. Highlights: Real-time sleep-stage classification system is established for living mice. It enables high-quality REM sleep classification for multiple mice. A new AI model for 4-second resolution is established in silico.