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Cited by 15Obstructive Sleep Apnea ResearchNon-Invasive Vital Sign MonitoringNeuroscience of respiration and sleep

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Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis

Open access
Wang, Haoyu, Qiu, Xihe, Li, Bin, Tan, Xiaoyu, Huang, Jingjing·2025·Complex & Intelligent Systems
DOI 10.1007/s40747-024-01648-0
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Abstract

Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel hetero geneous g raph c onvolutional f usion net work ( HeteroGCFNet ) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at https://github.com/AmbitYuki/HeteroGCFNet .

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Metadata

Authors
Wang, H., Qiu, X., Li, B., Tan, X., & Huang, J.
Affiliations
  • Shanghai University of Engineering Science (CN)
  • National University of Singapore (SG)
  • Fudan University (CN)
Corresponding author
  • Qiu, Xihe
  • Huang, Jingjing
ORCID
Venue
Complex & Intelligent Systems
Publisher
Springer Science and Business Media LLC
Published
2024-11-18
Volume
11
Issue
1
ISSN
  • 2198-6053 (ISSN-L)
  • 2199-4536
DOI
10.1007/s40747-024-01648-0
OpenAlex
W4404494855
Cited by
15
References
1
Citations by year
2026: 7 · 2025: 8
FWCI
4.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Complex & Intelligent Systems (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Natural Science Foundation of China (62102241)
  • Natural Science Foundation of Shanghai Municipality (23ZR1425400)
  • Eye & ENT Hospital’s double priority project A (YGJC026)
  • Shanghai Municipal Key Clinical Specialty, China (shslczdzk00801)
Topics
  • Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Neuroscience of respiration and sleep
Keywords
obstructive sleep apnea-hypopnea syndrome, automated diagnosis, polysomnography, multimodal physiological signals, knowledge graphs

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Wang, H., Qiu, X., Li, B., Tan, X., & Huang, J. (2025). Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-024-01648-0

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