A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging

作者全名："Zhao, Dechun; Jiang, Renpin; Feng, Mingyang; Yang, Jiaxin; Wang, Yi; Hou, Xiaorong; Wang, Xing"

作者地址："[Zhao, Dechun; Feng, Mingyang; Yang, Jiaxin; Wang, Yi] Chongqing Univ Posts & Telecommun, Coll Bioinformat, Chongqing, Peoples R China; [Jiang, Renpin] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing, Peoples R China; [Hou, Xiaorong] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Wang, Xing] Chongqing Univ, Coll Bioengn, Chongqing, Peoples R China"

通信作者："Zhao, DC (通讯作者)，Chongqing Univ Posts & Telecommun, Coll Bioinformat, Chongqing, Peoples R China.; Wang, X (通讯作者)，Chongqing Univ, Coll Bioengn, Chongqing, Peoples R China."

来源：TECHNOLOGY AND HEALTH CARE

ESI学科分类：MOLECULAR BIOLOGY & GENETICS

WOS号：WOS:000773422000002

JCR分区：Q4

影响因子：1.6

年份：2022

卷号：30

期号：2

开始页：323

结束页：336

文献类型：Article

关键词：Sleep staging; deep learning; one-dimensional convolutional neural network; long short-term memory

摘要："BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection. OBJECTIVE: This paper proposed a deep learning algorithm without feature extraction based on one-dimensional convolutional neural network and long short-term memory. METHODS: The algorithm can automatically divide sleep into 5 phases including awake period, non-rapid eye movement sleep period (N1 similar to N3) and rapid eye movement using the electroencephalogram signals. The raw signal was processed by the wavelet transform. Then, the processed signal was directly input into the deep learning algorithm to obtain the staging result. RESULTS: The accuracy of staging is 93.47% using the Fpz-Cz electroencephalogram signal. When using the Fpz-Cz and electroencephalogram signal, the algorithm can obtain the highest accuracy of 94.15%. CONCLUSION: These results show that this algorithm is suitable for different physiological signals and can realize end-to-end automatic sleep staging without any manual feature extraction."

基金机构："National Natural Science Foundation of China [31700856]; Natural Science Foundation of Chongqing, China [cstc2018jcyjAX0163]; special research project of philosophy and social science of Chongqing Medical University, China [201712]"

基金资助正文："This paper is sponsored by the National Natural Science Foundation of China (31700856), the Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0163) and the special research project of philosophy and social science of Chongqing Medical University, China (Grant No. 201712)."