文献类型：期刊文献

英文题名：Deep Convolutional Network Method for Automatic Sleep Stage Classification Based on Neurophysiological Signals

作者：Sun, Yudong[1]; Wang, Bei[1]; Jin, Jing[1]; Wang, Xingyu[1]

机构：[1] Ministry of Education, Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, 200237, China

年份：2018

外文期刊名：Proceedings - 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2018

收录：EI(收录号：20191106635771)

语种：英文

外文关键词：Convolution - Neurophysiology - Inspection - Biomedical signal processing - Sleep research

摘要：The accurate interpretation of sleep stages has a very important significance in the diagnosis of sleep disorders and the assessment of sleep health. The visual inspection on sleep staging required qualified skill and enough clinical experience. Usually, the visual inspection on one's overnight sleep recording takes 1 2 hours. The automatic sleep stage interpretation can reduce the laborious task of visual inspection. In this study, a deep convolutional network model was developed for automatic sleep stage classification based on neurophysiological signals. The residual module is utilized to increase the depth of the network to extract the multi-level features of the sleep stages. The long-short term memory (LSTM) is used to learn the sleep transition mechanism during sleep process. 20-fold cross validation experiment was performed. The results showed that the developed model achieved an accuracy of 81.0 and 73.6 of the macro-averaging F1-score (MF1). ? 2018 IEEE.