文献类型：期刊文献

英文题名：A CNN model with statistical correction rules for automatic sleep stage scoring

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

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

年份：2019

起止页码：613

外文期刊名：Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019

收录：EI(收录号：20194207534211)

语种：英文

摘要：In this study, a convolutional neural network (CNN) model with statistical correction rules is developed for the automatic sleep stage scoring. Sleep is a dynamic process which consisted of several sleep stages from light sleep to deep sleep. The convolutional neural network is designed by 6 layers, using two convolution kernels of different sizes to extract the time domain and frequency domain features separately. The statistical correction rules are extracted regarding to the dynamic transition between different sleep stages. The developed method was tested on the large amount of sleep data for the inspection of consisted sleep stages during ones overnight sleep. The prediction results by CNN model were corrected by the statistical correction rules. Totally, the sleep recording of 20 subjects were evaluated. The obtained results showed that the combination of CNN and correction rules achieved rather good and reasonable performance for sleep stage scoring. ? 2019 IEEE.