An attention-based deep learning approach for sleep stage classification with single-channel EEG

An attention-based deep learning approach for sleep stage classification with single-channel EEG

Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction...

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Bibliographic Details
Main Authors: Eldele, Emadeldeen, Chen, Zhenghua, Liu, Chengyu, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli, Guan, Cuntai
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2022
Subjects:
Computer and Information Science
Engineering
Sleep stage classification, sleep-edf, SHHS
Online Access:https://hdl.handle.net/10356/155623

Internet

https://hdl.handle.net/10356/155623

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