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

This is the authors' abstract. We don't add key points for this paper.

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. Common sleep problems are linked to various health conditions, but are often underdiagnosed. This study presents a deep learning model trained on 15,785 nights of respiratory data for contactless sleep monitoring. Leveraging this model, the remote management platform enables real-time in-home sleep assessment, advancing sleep health equity.