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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

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  • 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,
  • Hong Hong

摘要

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.