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Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee Keong Kwoh, Xiaoli Li, Cuntai Guan
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.
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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://doi.org/10.1109/tnsre.2021.3076234
Eldele, Emadeldeen, et al. "An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, 2021, pp. 809–818, https://doi.org/10.1109/tnsre.2021.3076234.
Eldele, Emadeldeen, Zhenghua Chen, Chengyu Liu, Min Wu, Chee Keong Kwoh, Xiaoli Li, and Cuntai Guan. "An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 809–818. https://doi.org/10.1109/tnsre.2021.3076234.
Eldele E., Chen Z., Liu C. [и др.] An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG // IEEE Transactions on Neural Systems and Rehabilitation Engineering. — 2021. — Vol. 29. — P. 809–818. — URL: https://doi.org/10.1109/tnsre.2021.3076234
@article{eldele2021attentionbased,
title = {{An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG}},
author = {Emadeldeen Eldele and Zhenghua Chen and Chengyu Liu and Min Wu and Chee Keong Kwoh and Xiaoli Li and Cuntai Guan},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
year = {2021},
volume = {29},
pages = {809--818},
doi = {10.1109/tnsre.2021.3076234},
url = {https://doi.org/10.1109/tnsre.2021.3076234}
}TY - JOUR AU - Eldele, Emadeldeen AU - Chen, Zhenghua AU - Liu, Chengyu AU - Wu, Min AU - Kwoh, Chee Keong AU - Li, Xiaoli AU - Guan, Cuntai TI - An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering VL - 29 SP - 809 EP - 818 PY - 2021 DA - 2021/01/01 DO - 10.1109/tnsre.2021.3076234 UR - https://doi.org/10.1109/tnsre.2021.3076234 ER -
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