Long-range interdependencies aware signal analysis for rapid and accurate obstructive sleep apnea prediction
Year: 2026
Type: article
Abstract: Obstructive sleep apnea, characterized by frequent interruptions in breathing and intermittent hypoxia, is a public health concern due to its potential to increase multiple organ system morbidity and mortality. Although polysomnography is the gold standard for diagnosis, it is laborious, costly, and complex. Here, we present a biological geometry-aware artificial intelligence framework that processes physiological signals to predict sleep apnea severity. This framework extracts multifractal coupling features that capture long-range dependencies among physiological processes and integrates them into deep learning for classification. We validate this approach across five heterogeneous cohorts comprising more than 35,000 subjects. The method achieves robust and consistent performance, with area-under-the-curve values exceeding 0.91 and improvements of 8–18% over state-of-the-art models. The extracted coupling representations exhibit rapid convergence and consistent performance across heterogeneous cohorts. This framework provides a robust representation of physiological dynamics and supports future developments in automated analysis of sleep-disordered breathing. Researchers develop a framework that captures long-range physiological interactions from sleep recordings to predict obstructive sleep apnea severity. Validated in more than 35,000 individuals, it shows robust performance across diverse populations.
Source: Nature Communications
Authors: Zhenkun Wang, Chenzhong Yin, Anzhe Cheng, Xinghe Chen, Mingxi Cheng, Li An, Haoqing Wang, Haofan Sun, Li Ding, Stefan Mihaicuta, Lucretia Udrescu, David Gozal, Félix del Campo, Fernando Vaquerizo-Villar, Roberto Hornero, Jan Hedner, Winfried Randerath, David M. Mannino, Shahin Nazarian, Mihai Udrescu, Paul Bogdan
Institutions: University of Southern California, Arizona State University, Victor Babeș University of Medicine and Pharmacy Timișoara, Marshall University, Universidad de Valladolid, Instituto de Salud Carlos III, Hospital Universitario Río Hortega, Sahlgrenska University Hospital, University of Gothenburg, University of Cologne, University of Kentucky, COPD Foundation, Politehnica University of Timisoara
Language: English
Subfield: Physiology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,990
Funders: National Science Foundation, Northrop Grumman, University of Southern California, Arizona State University, Carnegie Mellon University, COPD Foundation, Ministerio de Ciencia e Innovación, Sahlgrenska Universitetssjukhuset, Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, Universidad de Valladolid, Universität zu Köln, National Institutes of Health, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Viterbi School of Engineering, University of Southern California, Instituto de Salud Carlos III, Sahlgrenska Akademin, European Regional Development Fund, Agencia Estatal de Investigación, Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare, National Institute on Aging, European Social Fund, Army Research Office
Awards: G1037014486, G1050765037, G1304932662, CAREER: Embracing Complexity: A Fractal Calculus Approach to the Modeling and Optimization of Medical Cyber-Physical Systems, G1837804946, G1855990336, CENTER FOR COMPLEX PARTICLE SYSTEMS (COMPASS), G2052406043, G2243702923, G2262748287, G2309174360, G300979063, G3331264565, G3480869486, G3518538256, G3618234253, G3699406740, G3779662906, G410571670, G425668148, G430777504, G451917667, G4580314898, Neurovascular calcification, Alzheimer’s disease and related dementias in two Native South American populations, G5080475149, G5342369629, G5421974903, G5587375819, G5805502524, G5867013586, CPS: Small: Uncertainty-aware Framework for Specifying, Designing and Verifying Cyber-Physical Systems, G5967599077, G6193981907, G6218905885, G7452299184, G745419941, G8186789035, MCA:Theoretical Foundations of NeuroAI: A Modeling Framework Motivated by Living Neuronal Network Dynamics, Interpretable machine learning to synergize brain age estimation and neuroimaging genetics
OpenAlex ID: W7213983825