Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164490
Title: ADAST: Attentive cross-domain EEG-based sleep staging framework with iterative self-training
Authors: Eldele, Emadeldeen
Ragab, Mohamed
Chen, Zhenghua
Wu, Min
Kwoh, Chee Keong
Li, Xiaoli
Guan, Cuntai
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X. & Guan, C. (2022). ADAST: Attentive cross-domain EEG-based sleep staging framework with iterative self-training. IEEE Transactions On Emerging Topics in Computational Intelligence, 1-12. https://dx.doi.org/10.1109/TETCI.2022.3189695
Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
Abstract: Sleep staging is of great importance in the diagnosis and treatment of sleep disorders. Recently, numerous data-driven deep learning models have been proposed for automatic sleep staging. They mainly train the model on a large public labeled sleep dataset and test it on a smaller one with subjects of interest. However, they usually assume that the train and test data are drawn from the same distribution, which may not hold in real-world scenarios. Unsupervised domain adaption (UDA) has been recently developed to handle this domain shift problem. However, previous UDA methods applied for sleep staging have two main limitations. First, they rely on a totally shared model for the domain alignment, which may lose the domain-specific information during feature extraction. Second, they only align the source and target distributions globally without considering the class information in the target domain, which hinders the classification performance of the model while testing. In this work, we propose a novel adversarial learning framework called ADAST to tackle the domain shift problem in the unlabeled target domain. First, we develop an unshared attention mechanism to preserve the domain-specific features in both domains. Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels. We also propose dual distinct classifiers to increase the robustness and quality of the pseudo labels. The experimental results on six cross-domain scenarios validate the efficacy of our proposed framework and its advantage over state-of-the-art UDA methods.
URI: https://hdl.handle.net/10356/164490
ISSN: 2471-285X
DOI: 10.1109/TETCI.2022.3189695
Schools: School of Computer Science and Engineering 
Organisations: Institute for Infocomm Research (IR), Centre for Frontier Research (CFAR), A*STAR
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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