In Neuroscience research
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.
Koyanagi Iyo, Tezuka Taro, Yu Jiahui, Srinivasan Sakthivel, Naoi Toshie, Yasugaki Shinnosuke, Nakai Ayaka, Taniguchi Shimpei, Hayashi Yu, Nakano Yasushi, Sakaguchi Masanori
AI, EEG, REM sleep, deep learning, mice, sleep, sleep stage classification