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Lookup NU author(s): Bing Zhai, Dr Haoran Duan, Dr Yu GuanORCiD, Dr Wai Lok Woo
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2013 IEEE. Long-term non-invasive sleep stage monitoring is instrumental in comprehending the progression of sleep disorders, cardiovascular diseases, and the interplay between sleep, type 2 diabetes, and neurodegenerative diseases. However, the conventional deep learning approach is susceptible to personal attributes (PAs) such as age, Body Mass Index, and severity of sleep apnea existing in the training dataset, potentially hindering its generalisation capacity to unseen cohorts. This paper introduces DSleepNet, a novel approach that disentangles the feature space into PA-specific and PA-agnostic components using two probabilistic encoders. The PA-agnostic features, designed to remain unaffected by personal attributes, outperformed the baseline CNN, improving the mean F1 score by up to 8.7% (baseline: 60.3) and Cohen's Kappa by 4.7% (baseline: 55.5), especially in reducing the impact of sleep apnea. DSleepNet functions without the need for target cohort data during training. It operates without the need to access PA data during inference, nor does it require fine-tuning. A novel Independent Excitation mechanism is incorporated into the latent feature space to remove correlations between the two types of features. Comprehensive testing in various PA settings has demonstrated its efficacy in improving the model's robustness. Our code is available at: https://github.com/bzhai/DSleepNet.
Author(s): Zhai B, Duan H, Guan Y, Phan H, Woo WL
Publication type: Article
Publication status: Published
Journal: IEEE Journal of Biomedical and Health Informatics
Year: 2025
Pages: Epub ahead of print
Online publication date: 13/02/2025
Acceptance date: 02/04/2018
Date deposited: 04/03/2025
ISSN (print): 2168-2194
ISSN (electronic): 2168-2208
Publisher: IEEE
URL: https://doi.org/10.1109/JBHI.2025.3541851
DOI: 10.1109/JBHI.2025.3541851
