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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.10758 |
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| _version_ | 1866910819689693184 |
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| author | Han, Seungwoo |
| author_facet | Han, Seungwoo |
| contents | With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix Han, Seungwoo Signal Processing Artificial Intelligence With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification. |
| title | Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2410.10758 |