Fully automatic REM sleep stage-specific intervention systems using single EEG in mice. (January 2023)

• Record Type: Journal Article
• Authors: Koyanagi, Iyo; Tezuka, Taro; Yu, Jiahui; Srinivasan, Sakthivel; Naoi, Toshie; Yasugaki, Shinnosuke; Nakai, Ayaka; Taniguchi, Shimpei; Hayashi, Yu; Nakano, Yasushi; Sakaguchi, Masanori
• Is Part Of: Neuroscience research. Volume 186(2023)
• Page Start: 51
• Page End: 58
• Publication Date: 2023-01
• Subjects: Sleep -- AI -- Deep learning -- Sleep stage classification -- REM sleep -- EEG -- Mice; Neurosciences -- Research -- Periodicals; Neurosciences -- Research -- Japan -- Periodicals; Neurology -- Periodicals; Neurosciences -- Periodicals; Neurosciences -- Recherche -- Périodiques; Neurosciences -- Recherche -- Japon -- Périodiques; Neurosciences -- Research; Japan; Periodicals; 612.8
• DOI: 10.1016/j.neures.2022.10.001
• Languages: English
• ISSNs: 0168-0102

Abstract

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.