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
• Other Authors: School of Computer Science and Engineering
• Format: 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
• Institution: Nanyang Technological University

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...