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 sel

SUBMITTER: Zhuang Z 

PROVIDER: S-EPMC12546746 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature