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

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.