An automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data

• Published in: The European journal of neuroscience Vol. 60; no. 7; pp. 5467 - 5486
• Main Authors: Yamada, Rikuhiro G., Matsuzawa, Kyoko, Ode, Koji L., Ueda, Hiroki R.
• Format: Journal Article
• Language: English
• Published: France Wiley Subscription Services, Inc 01.10.2024
• Subjects: Algorithms; automated sleep staging; EEG; EEG/EMG; Electroencephalography; Electromyography; NREM sleep; REM sleep; Sleep; Sleep and wakefulness; sleep–wake cycle; Statistical analysis; Statistics; sleep–wake cycle; EEG/EMG; automated sleep staging
• ISSN: 0953-816X; 1460-9568; 1460-9568
• DOI: 10.1111/ejn.16465

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep–wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large‐scale and long‐term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep–wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20–30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep–wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high‐throughput genetic or pharmacological methods for sleep research. By identifying three simple statistical features that can represent sleep–wake stages in a 3D space, we developed a classification process that aligns with traditional definitions and the intuition of experts.