Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155623
Title: An attention-based deep learning approach for sleep stage classification with single-channel EEG
Authors: Eldele, Emadeldeen
Chen, Zhenghua
Liu, Chengyu
Wu, Min
Kwoh, Chee Keong
Li, Xiaoli
Guan, Cuntai
Keywords: Computer and Information Science
Engineering
Issue Date: 2021
Source: Eldele, E., Chen, Z., Liu, C., Wu, M., Kwoh, C. K., Li, X. & Guan, C. (2021). An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 29, 809-818. https://dx.doi.org/10.1109/TNSRE.2021.3076234
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Abstract: 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 module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep.
URI: https://hdl.handle.net/10356/155623
ISSN: 1534-4320
DOI: 10.1109/TNSRE.2021.3076234
DOI (Related Dataset): 10.21979/N9/EUHGHS
10.21979/N9/EAMYFO
10.21979/N9/MA1AVG
Rights: © 2021 The Author(s). Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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