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

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.

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Publication Details

Journal
Nature Communications
Published
2025-10-22
DOI
https://doi.org/10.1038/s41467-025-64340-y
Primary Topic
Obstructive Sleep Apnea Research
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Biao Xue, Chang–Hong Fu, Xiaochuan Cui, Mugen Peng et al.
Nature Communications
Obstructive Sleep Apnea Research
article

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

Biao Xue, Chang–Hong Fu, Xiaochuan Cui, Mugen Peng, Hui Chu, E Wang, Zhongxu Zhuang, Junyi Xin, Rui Chen, Yan Li, Yue Zhang, Yan Xu, Xiaohua Zhu, Xuan Yang, Meilin Wang, Ning Ding, Jing Xu, Hong Hong, Q. An
article en

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.

Nature CommunicationsVol. 16(1)
Central South University (CN), Beijing University of Posts and Telecommunications (CN), Xuzhou Medical College (CN), Nanjing University of Science and Technology (CN), Soochow University (CN), Huaian First People’s Hospital (CN), Second Affiliated Hospital of Soochow University (CN), Second People’s Hospital of Huai’an (CN), Wuxi People's Hospital (CN), Xiangya Hospital Central South University (CN), Nanjing University of Science and Technology ZiJin College, Nanjing Medical University (CN), Air Force Medical University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 62%
Obstructive Sleep Apnea Research
DOIOpen Access PDFOpenAlex
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