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

• Main Authors: Eldele, Emadeldeen, Chen, Zhenghua, Liu, Chengyu, Wu, Min, Kwoh, Chee Keong, Li, Xiaoli, Guan, Cuntai
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Computer and Information Science; Engineering; Sleep stage classification, sleep-edf, SHHS
• 在線閱讀: https://hdl.handle.net/10356/155623

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...