Thankachan Stephen, Gerashchenko Andrei, Kastanenka Ksenia V, Bacskai Brian J, Gerashchenko Dmitry
Harvard Medical School / Veterans Affairs Boston Healthcare System, West Roxbury, MA 02132, USA.
Neurotargeting Systems, Inc., Chestnut Hill, MA 02467, USA.
MethodsX. 2022 Aug 8;9:101811. doi: 10.1016/j.mex.2022.101811. eCollection 2022.
Studying the biology of sleep requires accurate and efficient assessment of the sleep stages. However, analysis of sleep-wake cycles in mice and other laboratory animals remains a time-consuming and laborious process. In this study, we developed a Python script and a process for the streamlined analysis of sleep data that includes real-time processing of electroencephalogram (EEG) and electromyogram (EMG) signals that is compatible with commercial sleep-recording software that supports user datagram protocol (UDP) communication. The process consists of EEG/EMG data acquisition, automated threshold calculation for real-time determination of sleep stages, sleep staging and EEG power spectrum analysis. It also allows data storage in the format that facilitates further analysis of the sleep pattern in mice. The described method is aimed at increasing efficiency of sleep stage scoring and analysis in mice thus facilitating sleep research. • A process of EEG/EMG recording and streamline analysis of sleep-wake cycle in real time in mice. • The compatibility with commercial sleep-recording software that can generate a UDP stream. • The capability of further analysis of recorded data by an open-source software.
研究睡眠生物学需要对睡眠阶段进行准确而高效的评估。然而，分析小鼠和其他实验动物的睡眠-觉醒周期仍然是一个耗时费力的过程。在本研究中，我们开发了一个Python脚本以及一个用于简化睡眠数据分析的流程，该流程包括对脑电图（EEG）和肌电图（EMG）信号的实时处理，且与支持用户数据报协议（UDP）通信的商业睡眠记录软件兼容。该流程包括EEG/EMG数据采集、用于实时确定睡眠阶段的自动阈值计算、睡眠分期以及EEG功率谱分析。它还允许以便于进一步分析小鼠睡眠模式的格式存储数据。所描述的方法旨在提高小鼠睡眠阶段评分和分析的效率，从而促进睡眠研究。
• 小鼠EEG/EMG记录及实时简化分析睡眠-觉醒周期的流程。
• 与可生成UDP流的商业睡眠记录软件的兼容性。
• 通过开源软件对记录数据进行进一步分析的能力。
