基于生理电信号的自动睡眠分期算法综述

    Review of Automatic Sleep Stage Classification Algorithms Based on Physiological Signals

    • 摘要: 基于生理电信号的睡眠分期是监测睡眠过程和诊疗睡眠疾病的方法重要前提,针对人工睡眠分期方法存在耗时久、效率低、具有主观性等问题,近年来自动睡眠分期方法凭借高效性和准确性受到研究者的关注。因此,从算法设计的角度针对近6年的自动睡眠分期方法进行综述,分为传统机器学习和深度学习两大类,并对2个类别按照单通道与多通道生理电信号2种输入方式,从模型算法、信号类型、分期性能方面进行了归纳总结。通过对比可知:单通道信号输入降低了信号采集成本,更适用于家庭睡眠监测,而多通道信号输入贴合分期准则,更适用于临床睡眠分析;深度学习类算法相较于传统机器学习类更具有研究前景,其可利用深度神经网络自动学习信号内在表征,在高效处理大规模数据的同时提供较好的分期性能。深度学习方法未来的研究重点应该从模型设计的角度转变为提升模型可解释性和泛化性,从而推动深度神经网络在睡眠医学领域中的应用。

       

      Abstract: Sleep stage classification based on physiological signals is essential for monitoring sleep and diagnosing sleep disorders. Artificial sleep stage classification method is time-consuming, inefficient, and subjective. In recent years, automatic sleep stage classification method has attracted more attention due to its efficiency and accuracy. Therefore, the automatic sleep stage classification algorithms are reviewed from the past six years based on the perspective of algorithm. The literature is classified as traditional machine learning and deep learning, and each category is further summarized based on single- and multichannel physiological signal inputs, illustrating algorithms, signal types, and sleep staging performance. Comparison across the methods indicates that the single-channel input reduces the cost of signal acquisition, making it more suitable for home sleep monitoring. And the multi-channel input is closer with sleep staging guidelines, which is more appropriate for clinical analysis. Compared with traditional machine learning, deep learning methods offer more promising researching prospects. Because they utilize deep neural networks to automatically learn representation, which efficiently handle large-scale dataset and provide better sleep staging performance. Existing works demonstrate that the future sleep staging research of deep learning should focus more on improving model interpretability and generalization instead of model design, to promote the application of deep neural networks in sleep medicine field.

       

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