Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (6): 1026-1037.DOI: 10.3778/j.issn.1673-9418.2012003

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Sleep Staging Based on Relational Induction Biases

NENG Wenpeng, LU Jun, ZHAO Caihong   

  1. 1. College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
    2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
  • Online:2021-06-01 Published:2021-06-03



  1. 1.黑龙江大学 计算机科学技术学院, 哈尔滨 150080
    2.黑龙江大学 黑龙江省数据库与并行计算重点实验室, 哈尔滨 150080


Sleep disorders seriously affect human health and life. Accurate classification of sleep stages is the key to detecting and treating sleep disorders. In recent years, methods based on deep learning have surpassed traditional machine learning methods and human experts. However, the internal structure of deep learning is complex and requires to be designed by expert who is familiar with the computer and medical fields. This paper aims to analyze the relational induction biases in the existing sleep staging model based on deep learning, and explores the basic principles of sleep staging model design. This paper analyzes relational induction biases such as translation invariance, time invariance and hierarchical processing. Firstly, the model is divided into three categories according to whether it contains convolution layer with translation invariance and recurrent layer with time invariance: convolutional neural network framework, recurrent neural network framework and hybrid neural network framework. Secondly, according to the hierarchical processing method of the frame, segment and sequence in the model, a more detailed classification is carried out. Thirdly, it analyzes the impact of different relational induction biases in the model on the performance of sleep staging. It is proposed that a relational inductive bias matched the task should be introduced when the automatic sleep staging model is designed. Finally, the advantages and limitations of sleep staging based on deep learning are discussed, and it may be necessary to use more advanced relationship induction bias to express knowledge more abstractly and combine it with other artificial intelligence technologies in the future.

Key words: deep learning, relational induction bias, convolutional neural network (CNN), recurrent neural network (RNN), sleep staging



关键词: 深度学习, 关系归纳偏置, 卷积神经网络(CNN), 循环神经网络(RNN), 睡眠分期