A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information

A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole...

Full description

Bibliographic Details
Main Authors: Wei Zhou, Hangyu Zhu, Ning Shen, Hongyu Chen, Cong Fu, Huan Yu, Feng Shu, Chen Chen, Wei Chen
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Sleep stage
deep learning
EEG
multiple multiscale convolutional neural network
residual squeeze and excitation block
time sequence encoder
Online Access:https://ieeexplore.ieee.org/document/9940984/

Internet

https://ieeexplore.ieee.org/document/9940984/

Similar Items