Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis
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 clin...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01648-0 |
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| author | Haoyu Wang Xihe Qiu Bin Li Xiaoyu Tan Jingjing Huang |
| author_facet | Haoyu Wang Xihe Qiu Bin Li Xiaoyu Tan Jingjing Huang |
| author_sort | Haoyu Wang |
| collection | DOAJ |
| description | 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 heterogeneous graph convolutional fusion network (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 . |
| format | Article |
| id | doaj-art-38335331300d4e999af1aabf5445e01c |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-38335331300d4e999af1aabf5445e01c2025-02-02T12:49:20ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111810.1007/s40747-024-01648-0Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosisHaoyu Wang0Xihe Qiu1Bin Li2Xiaoyu Tan3Jingjing Huang4School of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceSchool of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceSchool of Electronic and Electrical Engineering, Shanghai University of Engineering ScienceINF Technology (Shanghai) Co., Ltd.ENT institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan UniversityAbstract 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 heterogeneous graph convolutional fusion network (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 .https://doi.org/10.1007/s40747-024-01648-0Obstructive sleep apnea-hypopnea syndromeMultimodal signalsHeterogeneous graphPersonalized healthcare |
| spellingShingle | Haoyu Wang Xihe Qiu Bin Li Xiaoyu Tan Jingjing Huang Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis Complex & Intelligent Systems Obstructive sleep apnea-hypopnea syndrome Multimodal signals Heterogeneous graph Personalized healthcare |
| title | Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis |
| title_full | Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis |
| title_fullStr | Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis |
| title_full_unstemmed | Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis |
| title_short | Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis |
| title_sort | multimodal heterogeneous graph fusion for automated obstructive sleep apnea hypopnea syndrome diagnosis |
| topic | Obstructive sleep apnea-hypopnea syndrome Multimodal signals Heterogeneous graph Personalized healthcare |
| url | https://doi.org/10.1007/s40747-024-01648-0 |
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