Real-time sleep disorder monitoring design using dynamic temporal graphs with facial and acoustic feature fusion
Year: 2025
Type: article
Abstract: Introduction: Sleep disorders pose significant risks to patient safety, yet traditional polysomnography imposes substantial discomfort and laboratory constraints. We developed a non-invasive multimodal monitoring system for real-time sleep pathology detection. Methods: We integrated facial expression analysis via deep convolutional neural networks with audio signal processing for breathing pattern detection. Heterogeneous data streams were unified into dynamic graph representations, with graph neural networks modeling spatiotemporal patterns of sleep pathologies. Results: The system accurately detected sleep apnea, restless leg syndrome, and cardiovascular irregularities with 10.7-s average delay and 94.6% clinical agreement, achieving diagnostic accuracy comparable to polysomnography. Conclusion: This framework enables continuous non-invasive monitoring for point-of-care screening and home-based management, potentially expanding sleep medicine access for underserved populations.
Authors: Fei Pei, Ying Zhou, Qiangqiang Fu, Hong Zhou
Institutions: Tongji University, Yangpu Hospital of Tongji University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4416136243