hhc Data Creation Center, Eisai Co., Ltd., Koishikawa 4-6-10, Bunkyo-ku, Tokyo 112-8088, Japan.
Neurology Business Group, Eisai Inc., 100 Tice Blvd, Woodcliff Lake, NJ 07677, USA.
Neurosci Res. 2021 Dec;173:99-105. doi: 10.1016/j.neures.2021.07.003. Epub 2021 Jul 17.
Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose of base feature extraction, wake-sleep and rapid eye movement (REM) and non- rapid eye movement (NREM) models were developed by extracting defining features from mouse-derived electromyogram (EMG) and electroencephalogram (EEG) signals, respectively. The wake-sleep model and REM-NREM sleep model were integrated into three different algorithms including a rule-based integration approach, an ensemble stacking approach, and a multimodal with fine-tuning approach. The deep learning algorithm assessing sleep stages in animal experiments by the multimodal with fine-tuning approach showed high potential for increasing accuracy in sleep stage scoring in mice and promoting sleep research.
睡眠阶段评分对于临床前和临床研究中的睡眠结构确定非常重要。本研究的目的是开发一种新的基于深度神经网络算法的自动小鼠睡眠阶段分类系统。为了进行基本特征提取，通过从源自小鼠的肌电图 (EMG) 和脑电图 (EEG) 信号中提取定义特征，分别开发了清醒-睡眠和快速眼动 (REM) 和非快速眼动 (NREM) 模型。将清醒-睡眠模型和 REM-NREM 睡眠模型集成到三个不同的算法中，包括基于规则的集成方法、集成堆叠方法和多模态微调方法。通过多模态微调方法评估动物实验中睡眠阶段的深度学习算法，显示出在提高小鼠睡眠阶段评分准确性和促进睡眠研究方面具有很高的潜力。
