Koyanagi Iyo, Tezuka Taro, Yu Jiahui, Srinivasan Sakthivel, Naoi Toshie, Yasugaki Shinnosuke, Nakai Ayaka, Taniguchi Shimpei, Hayashi Yu, Nakano Yasushi, Sakaguchi Masanori
International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, 305-8575 Ibaraki, Japan; Doctoral Program in Neuroscience, Degree Programs in Comprehensive Human Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, 305-8575 Ibaraki, Japan; Research Fellow of Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo 102-0083, Japan.
Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, 305-8575 Ibaraki, Japan.
Neurosci Res. 2023 Jan;186:51-58. doi: 10.1016/j.neures.2022.10.001. Epub 2022 Oct 4.
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.
睡眠阶段特异性干预被广泛用于阐明睡眠的功能及其潜在机制。对于这种干预，准确分类快速眼动（REM）睡眠至关重要。然而，尚未在小鼠体内获得全自动实时REM睡眠分类的证据。在此，我们报告了一种系统在小鼠体内的实现，该系统可从单通道脑电图（EEG）实时分类睡眠阶段。它能够以90%的灵敏度和86%的精确度进行REM睡眠特异性干预，而无需对每只小鼠进行预先配置。我们进一步衍生出了能够以更高频率采样和时间分辨率进行分类的系统。这种即插即用的睡眠分期系统为研究睡眠功能提供了一种全自动、准确且可扩展的工具。
