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Publications

Journal Article Epublish

PubMed: 42907459 DOI: 10.1038/s41467-026-76274-0

Long-range interdependencies aware signal analysis for rapid and accurate obstructive sleep apnea prediction.

Nat Commun 17(1)

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

Affiliations

  • Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
  • School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, USA.
  • Department of Pulmonology, "Victor Babeş" University of Medicine and Pharmacy Timişoara, Timişoara, Romania.
  • Center for Research and Innovation in Personalized Medicine of Respiratory Diseases (CRIPMRD), "Victor Babeş" University of Medicine and Pharmacy Timişoara, Timişoara, Romania.
  • Department I - Clinical Pharmacy and Drug Analysis, "Victor Babeş" University of Medicine and Pharmacy Timişoara, Timişoara, Romania.
  • Center for Drug Data Analysis, Cheminformatics, and the Internet of Medical Things (CDDA-CIMT), "Victor Babeş" University of Medicine and Pharmacy Timişoara, Timişoara, Romania.
  • Departments of Pediatrics, Biomedical Sciences and Dentistry and Maxillofacial Surgery, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, USA.
  • Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
  • CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
  • Hospital Universitario Río Hortega. Valladolid, Valladolid, Spain.
  • Pulmonary Department, Sleep Disorders Center, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Center of Sleep and Wake Disorders, Sahlgrenska Academy, Gothenburg University, Göteborg, Sweden.
  • Institute Für Pneumologie an der Universität Zu Köln, Cologne, Germany.
  • College of Medicine, University of Kentucky, Lexington, KY, USA.
  • COPD Foundation, Miami, FL, USA.
  • Department of Computer and Information Technology, Politehnica University Timişoara, Timişoara, Romania.
  • Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Center for Complex Particle Systems (COMPASS), University of Southern California, Los Angeles, CA, USA.
  • Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA.

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.

MeSH

Sleep Apnea, Obstructive, Respiration, Sleep, Apnea, Sleep Apnea Syndromes