Advancing sleep health equity through deep learning on large-scale nocturnal respiratory signals

Zhongxu Zhuang, Biao Xue, Qiang An, Hui Chu, Yue Zhang, Rui Chen, Jing Xu, Ning Ding, Xiaochuan Cui, E. Wang, Meilin Wang, Junyi Xin, Xuan Yang, Yan Xu, Yaxian Li, Chang-Hong Fu, Xiaohua Zhu, Mugen Peng () and Hong Hong ()
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Zhongxu Zhuang: Nanjing University of Science and Technology
Biao Xue: Nanjing University of Science and Technology
Qiang An: Fourth Military Medical University
Hui Chu: Nanjing University of Science and Technology
Yue Zhang: Nanjing University of Science and Technology ZiJin College
Rui Chen: The Second Affiliated Hospital of Soochow University
Jing Xu: The Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University
Ning Ding: The First Affiliated Hospital with Nanjing Medical University
Xiaochuan Cui: The Affiliated Wuxi People’s Hospital of Nanjing Medical University
E. Wang: Central South University
Meilin Wang: Nanjing Medical University
Junyi Xin: Nanjing Medical University
Xuan Yang: Nanjing University of Science and Technology
Yan Xu: Nanjing University of Science and Technology
Yaxian Li: Nanjing University of Science and Technology
Chang-Hong Fu: Nanjing University of Science and Technology
Xiaohua Zhu: Nanjing University of Science and Technology
Mugen Peng: Beijing University of Posts and Telecommunications
Hong Hong: Nanjing University of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Sleep disorders affect billions globally, yet diagnostic access remains limited by healthcare resource constraints. Here, we develop a deep learning framework that analyzes respiratory signals for remote sleep health monitoring, trained on 15,785 nights of data across diverse populations. Our approach achieves robust performance in four-stage sleep classification (82.13% accuracy on internal validation; 79.62% on external validation) and apnea-hypopnea index estimation (intraclass correlation coefficients 0.90 and 0.94, respectively). Through transfer learning, we adapt the model to radar-derived respiratory signals, enabling contactless monitoring in home environments. The framework demonstrates consistent performance across demographic subgroups, supports real-time processing through self-supervised learning techniques, and integrates with a remote sleep health management platform for clinical deployment. This approach bridges critical gaps in sleep healthcare accessibility, supporting population-level screening and monitoring, paving the way for scalable sleep healthcare, and advancing sleep health equity.

Date: 2025
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DOI: 10.1038/s41467-025-64340-y

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