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  "author": "Yuze Liu",
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  "author": "Yuze Liu",
            
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  "author_email": "",
            
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  "doi": "10.57702/tdfcq37c",
            
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  "doi_date_published": "2024-12-02",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Ziming Zhao",
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      "extra_author": "Ziming Zhao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Tiehua Zhang",
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      "extra_author": "Tiehua Zhang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    {
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    {
            
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      "extra_author": "Kang Wang",
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      "extra_author": "Kang Wang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Xin Chen",
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      "extra_author": "Xin Chen",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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      "extra_author": "Xiaowei Huang",
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      "extra_author": "Xiaowei Huang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jun Yin",
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      "extra_author": "Jun Yin",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Zhishu Shen",
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      "extra_author": "Zhishu Shen",
            
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      "orcid": ""
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      "orcid": ""
            
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      "description": "",
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      "description": "",
            
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      "name": "sleep-stage-classification",
            
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      "title": "Sleep Stage Classification"
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      "title": "Sleep Stage Classification"
            
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  "name": 
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  "name": 
            
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temporal-data-for-sleep-stage-classification-via-hypergraph-learning",
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temporal-data-for-sleep-stage-classification-via-hypergraph-learning",
            
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  "notes": "Sleep stage classification is crucial for detecting 
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  "notes": "Sleep stage classification is crucial for detecting 
            
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patients' health conditions. Existing models, which mainly use 
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patients' health conditions. Existing models, which mainly use 
            
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Convolitional Neural Networks (CNN) for modelling Euclidean data and 
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Convolitional Neural Networks (CNN) for modelling Euclidean data and 
            
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Graph Convolution Networks (GNN) for modelling non-Euclidean data, are 
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Graph Convolution Networks (GNN) for modelling non-Euclidean data, are 
            
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unable to consider the heterogeneity and interactivity of multimodal 
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unable to consider the heterogeneity and interactivity of multimodal 
            
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data as well as the spatial-temporal correlation simultaneously, which 
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data as well as the spatial-temporal correlation simultaneously, which 
            
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hinders a further improvement of classification performance.",
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hinders a further improvement of classification performance.",
            
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      "display_name": "Sleep Stage Classification",
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      "display_name": "Sleep Stage Classification",
            
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      "id": "c5a6608e-6285-41f3-a21d-c0dab955f8c6",
            
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      "name": "Sleep Stage Classification",
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      "name": "Sleep Stage Classification",
            
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      "state": "active",
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      "state": "active",
            
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      "display_name": "Spatial-Temporal Data",
            
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      "id": "d64d5833-a8bf-4094-afae-0666d41f413a",
            
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      "name": "Spatial-Temporal Data",
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  ],
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  ],
            
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  "title": "Exploiting Spatial-Temporal Data for Sleep Stage 
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  "title": "Exploiting Spatial-Temporal Data for Sleep Stage 
            
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Classification via Hypergraph Learning",
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Classification via Hypergraph Learning",
            
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}
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}