Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 15Obstructive Sleep Apnea ResearchNon-Invasive Vital Sign MonitoringNeuroscience of respiration and sleep

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free accountOpen PDF

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 .

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s40747-024-01648-0

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 61

  1. Peng X et al (2021) All-nanofiber self-powered skin-interfaced real-time respiratory monitoring system for obstructive sleep apnea-hypopnea syndrome diagnosing. Adv Funct Mater 31(34):2103559 · DOI
  2. O’Connor-Reina C et al (2020) Tongue peak pressure: a tool to aid in the identification of obstruction sites in patients with obstructive sleep apnea/hypopnea syndrome. Sleep Breathing 24:281–286 · DOI
  3. Kong W et al (2021) Biomarkers of alzheimer’s disease in severe obstructive sleep apnea-hypopnea syndrome in the chinese population. Eur Arch Oto-Rhino-Laryng 278:865–872 · DOI
  4. Poka-Mayap V et al (2020) Obstructive sleep apnea and hypopnea syndrome in patients admitted in a tertiary hospital in cameroon: prevalence and associated factors. PLoS One 15(1):0227778 · DOI
  5. Yuan F et al (2021) Correlation between obstructive sleep apnea hypopnea syndrome and hypertension: a systematic review and meta-analysis. Annals of Palliative Medicine 10(12):122512261–122512261 · DOI
  6. Li Y, Wang Y (2021) Obstructive sleep apnea-hypopnea syndrome as a novel potential risk for aging. Aging Dis 12(2):586 · DOI
  7. Khandoker AH, Palaniswami M, Karmakar CK (2008) Support vector machines for automated recognition of obstructive sleep apnea syndrome from ecg recordings. IEEE Trans Inform Technol Biomed 13(1):37–48 · DOI
  8. Sundararajan K et al (2021) Sleep classification from wrist-worn accelerometer data using random forests. Sci Rep 11(1):24 · DOI
  9. Weaver EM, Kapur V, Yueh B (2004) Polysomnography vs self-reported measures in patients with sleep apnea. Arch Otolaryngol Head Neck Surg 130(4):453–458 · DOI
  10. Yildirim O, Baloglu UB, Acharya UR (2019) A deep learning model for automated sleep stages classification using psg signals. Int J Environ Res Public Health 16(4):599 · DOI
  11. Howe-Patterson M, Pourbabaee B, Benard F (2018). Automated detection of sleep arousals from polysomnography data using a dense convolutional neural network. In: 2018 Computing in Cardiology Conference (CinC), vol. 45 . IEEE · DOI
  12. ElMoaqet H et al (2020) Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors 20(18):5037 · DOI
  13. Wang H et al (2023) Neural-seir: A flexible data-driven framework for precise prediction of epidemic disease. Math Biosci Eng 20(9):16807–16823 · DOI
  14. Bear D, et al (2020). Learning physical graph representations from visual scenes. In: Advances in Neural Information Processing Systems 33, 6027–6039
  15. Xiang H, Zeng T, Yang Y (2020). A novel sleep stage classification via combination of fast representation learning and semantic-to-signal learning. In: 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE · DOI
  16. Kazemi SM et al (2020) Representation learning for dynamic graphs: A survey. J Mach Learn Res 21(70):1–73
  17. Li R et al (2023) Convolutional neural network for screening of obstructive sleep apnea using snoring sounds. Biomed Signal Process Control 86:104966
  18. Ma G, et al (2015). Unsupervised snore detection from respiratory sound signals. In: 2015 IEEE International Conference on Digital Signal Processing (DSP) . IEEE · DOI
  19. Zhao Y, et al (2011). A snoring detector for osahs based on patient’s individual personality. In: 2011 3rd International Conference on Awareness Science and Technology (iCAST) . IEEE
  20. Wang W et al (2021) Evaluating the performance of five scoring systems for prescreening obstructive sleep apnea-hypopnea syndrome. Sleep Breathing 25:1685–1692 · DOI
  21. Liu Y et al (2021) Cognitive function and life quality of patients with moderate-to-severe obstructive sleep apnea-hypopnea syndrome in china. Expert Rev Resp Med 15(3):435–440 · DOI
  22. Chen L, Zou S, Wang J (2022). Association of obstructive sleep apnea syndrome (osa/osahs) with coronary atherosclerosis risk: systematic review and meta-analysis. Computational and Mathematical Methods in Medicine · DOI
  23. Li H-Y et al (2020) How to manage continuous positive airway pressure (cpap) failure-hybrid surgery and integrated treatment. Auris Nasus Larynx 47(3):335–342 · DOI
  24. Thong BKS et al (2022) Telehealth technology application in enhancing continuous positive airway pressure adherence in obstructive sleep apnea patients: A review of current evidence. Front Med 9:877765
  25. Sheta A et al (2021) Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Appl Sci 11(14):6622 · DOI
  26. Song Y et al (2023) Ahi estimation of osahs patients based on snoring classification and fusion model. Am J Otolaryngol 44(5):103964 · DOI
  27. Jothi ESJ, Anitha J, Hemanth DJ (2022) A photoplethysmography-based diagnostic support system for obstructive sleep apnea using deep learning approaches. Comp Electric Eng 102:108279
  28. Huttunen R et al (2022) A comparison of signal combinations for deep learning-based simultaneous sleep staging and respiratory event detection. IEEE Trans Biomed Eng 70(5):1704–1714 · DOI
  29. Sheta A et al (2021) Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Appl Sci 11(14):6622 · DOI
  30. Sun H et al (2020) Sleep staging from electrocardiography and respiration with deep learning. Sleep 43(7):306 · DOI
  31. Ramachandran A, Karuppiah A (2021). A survey on recent advances in machine learning based sleep apnea detection systems. Healthcare 9(7) · DOI
  32. Bahrami M, Forouzanfar M (2022) Sleep apnea detection from single-lead ecg: A comprehensive analysis of machine learning and deep learning algorithms. IEEE Trans Instrum Measure 71:1–11
  33. Xing X, et al (2020). Multi-level attention graph neural network for clinically interpretable pathway-level biomarkers discovery. bioRxiv, 2020–12 · DOI
  34. Qiu X, et al (2024). Gk bertdta: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Computers in Biology and Medicine, 108376 · DOI
  35. Wierciński T et al (2022) Emotion recognition from physiological channels using graph neural network. Sensors 22(8):2980 · DOI
  36. Jia Z, et al (2020). Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In: IJCAI, vol. 2021 · DOI
  37. Xu Y et al (2021) Classifying vulnerability to sleep deprivation using resting-state functional mri graph theory metrics. Front Neurosci 15:660365
  38. Diykh M, Li Y, Abdulla S (2020) Eeg sleep stages identification based on weighted undirected complex networks. Comp Methods Prog Biomed 184:105116 · DOI
  39. Ji X, Li Y, Wen P (2022) Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification. IEEE Trans Neural Syst Rehab Eng 30:1464–1472 · DOI
  40. Wang Q et al (2022) Multi-layer graph attention network for sleep stage classification based on eeg. Sensors 22(23):9272 · DOI
  41. Cai Q et al (2020) A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from eeg signals. IEEE Trans Circuits Syst 68(2):777–781 · DOI
  42. Zhu G, Li Y, Wen P (2014) Analysis and classification of sleep stages based on difference visibility graphs from a single-channel eeg signal. IEEE J Biomed Health Inform 18(6):1813–1821 · DOI
  43. Hu Y, Shi W, Yeh C-H (2024) Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction. Comput Methods Prog Biomed 244:107930
  44. Cisotto G, et al (2020). Comparison of attention-based deep learning models for eeg classification. arXiv preprint arXiv:2012.01074 · DOI
  45. Chen T, et al (2022). Ms $$^{2} $$-gnn: Exploring gnn-based multimodal fusion network for depression detection. IEEE Transactions on Cybernetics · DOI
  46. Li M, Chen H, Cheng Z (2022) An attention-guided spatiotemporal graph convolutional network for sleep stage classification. Life 12(5):622 · DOI
  47. Kemp B et al (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg. IEEE Trans Biomed Eng 47(9):1185–1194 · DOI
  48. Alvarez-Estevez D, Rijsman RM (2021) Inter-database validation of a deep learning approach for automatic sleep scoring. PloS one 16(8):0256111 · DOI
  49. Lee H et al (2022) A large collection of real-world pediatric sleep studies. Sci Data 9(1):421 · DOI
  50. Hasan N et al (2021) Densenet convolutional neural networks application for predicting covid-19 using ct image. SN Comput Sci 2(5):389 · DOI
  51. Zhu T, Luo W, Yu F (2020) Convolution-and attention-based neural network for automated sleep stage classification. Int J Environm Res Public Health 17(11):4152 · DOI
  52. Strodthoff N, Wagner P, Schäfer J, Cimr L, Kreil D, Igel C, Tino P (2020) Deep learning for ecg analysis: Benchmarks and insights from ptb-xl. IEEE J Biomed Health Inform 25(5):1519–1528 · DOI
  53. Reddy L, Joshi G, Reddy M.G, Prabhu S.S (2021). Imle-net: An interpretable multi-level multi-channel model for ecg classification. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE · DOI
  54. Sridhar N, Rai A, Jung T-P, Cvetkovic B (2020) Deep learning for automated sleep staging using instantaneous heart rate. NPJ Digital Med 3(1):106 · DOI
  55. Eldele E, Shaban H, Khalil D, Elsaid MA (2021) An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Trans Neural Syst Rehab Eng 29:809–818 · DOI
  56. Hu R, Singh A (2021). Unit: Multimodal multitask learning with a unified transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision · DOI · Paper page
  57. Phan H, Khalighi S, Ghassemi M, Deters R (2021) Xsleepnet: Multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell 44(9):5903–5915
  58. Zhou W, Huang Y, Zhou Z, Liu Z, Tang L, Yu X (2022) A lightweight segmented attention network for sleep staging by fusing local characteristics and adjacent information. IEEE Trans Neural Syst Rehab Eng 31:238–247 · DOI
  59. Ding L et al (2023) Automatically detecting apnea-hypopnea snoring signal based on vgg19+ lstm. Biomed Signal Process Control 80:104351 · DOI
  60. Zhu H, Dong G, Huang G, Qian Y (2023) Masksleepnet: A cross-modality adaptation neural network for heterogeneous signals processing in sleep staging. IEEE J Biomed Health Inform 27(5):2353–2364 · DOI
  61. Morokuma S, Tsunoda H, Nagata M, Yagyu K, Takahashi K (2023) Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders. Sci Rep 13(1):17730 · DOI

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.