基于深度学习的脑电信号自动睡眠分期研究进展

Research Progress of EEG Automatic Sleep Staging Based on Deep Learning

睡眠是人类不可或缺的生理活动,准确地睡眠分期是诊断睡眠疾病的前提。当前,基于深度学习的脑电信号自动睡眠分期正成为研究的热点,虽然相关研究取得很多进展,但距离临床应用还有一定距离。本文就该领域展开综述,详细介绍了近年来基于深度学习的脑电信号自动睡眠分期方法,综合论述目前主流神经网络在自动睡眠分期领域的研究现状及进展,分析归纳了不同模型网络的潜力优势及未来发展方向,以促进深度学习技术在基于脑电信号的自动分期研究更深入发展。

Sleep is an indispensable physiological activity of human beings. Accurate sleep staging is the premise of diagnosing sleep diseases. At present, EEG automatic sleep staging based on deep learn-ing is becoming a hot research topic. Although related researches have made a lot of progress, there is still a long way to go before clinical application. This paper reviews this field, introduces in detail the EEG automatic sleep staging methods based on deep learning in recent years, comprehensively discusses the current research status and progress of mainstream neural networks in the field of automatic sleep staging, analyzes and summarizes the potential advantages and future develop-ment direction of different model networks. In order to promote the deep learning technology in the automatic staging based on EEG further development.