Hierarchical Transformer for Electrocardiogram Diagnosis
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912430926331904 |
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| author | Tang, Xiaoya Berquist, Jake Steinberg, Benjamin A. Tasdizen, Tolga |
| author_facet | Tang, Xiaoya Berquist, Jake Steinberg, Benjamin A. Tasdizen, Tolga |
| contents | We propose a hierarchical Transformer for ECG analysis that combines depth-wise convolutions, multi-scale feature aggregation via a CLS token, and an attention-gated module to learn inter-lead relationships and enhance interpretability. The model is lightweight, flexible, and eliminates the need for complex attention or downsampling strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Transformer for Electrocardiogram Diagnosis Tang, Xiaoya Berquist, Jake Steinberg, Benjamin A. Tasdizen, Tolga Machine Learning We propose a hierarchical Transformer for ECG analysis that combines depth-wise convolutions, multi-scale feature aggregation via a CLS token, and an attention-gated module to learn inter-lead relationships and enhance interpretability. The model is lightweight, flexible, and eliminates the need for complex attention or downsampling strategies. |
| title | Hierarchical Transformer for Electrocardiogram Diagnosis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.00755 |