A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique
A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique
复制标题
一种基于多模态网络微调技术的小鼠睡眠阶段评分深度学习算法
DOI：
10.1016/j.neures.2021.07.003
复制
发表时间：
2021-12-01
影响因子：
2.9
通讯作者：
Aoshima, Ken
中科院分区：
文献类型：
--
作者：
Akada, Keishi;Yagi, Takuya;Aoshima, Ken
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. (c) 2021 Elsevier B.V. and Japan Neuroscience Society. All rights reserved.
