A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion

Frontiers in Human Neuroscience

卷:15

Sha Sha¹  Yuanhua Qiao⁴  Changming Wang⁵  Mingai Li⁶  Lijuan Duan⁷  Zeyu Wang⁷  Mengying Li⁷ 

[1] Beijing Anding Hospital, Capital Medical University, Beijing, China;

[2] Beijing Key Laboratory of Trusted Computing, Beijing, China;

[3] Brain-Inspired Intelligence and Clinical Translational Research Center, Beijing, China;

[4] College of Applied Sciences, Beijing University of Technology, Beijing, China;

[5] Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China;

[6] Faculty of Information Technology, Beijing University of Technology, Beijing, China;

[7] National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing, China;

关键词: deep learning;    HHT;    sleep stage classification;    multimodal physiological signals;    fusion networks;   

DOI  :  10.3389/fnhum.2021.727139

来源: DOAJ

【 摘 要 】

Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient’s sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.

【 授权许可】

Unknown