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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11939 |
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| _version_ | 1866915520658276352 |
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| author | Li, Haitao Liu, Che Ding, Zhengyao Liu, Ziyi Shao, Wenqi Huang, Zhengxing |
| author_facet | Li, Haitao Liu, Che Ding, Zhengyao Liu, Ziyi Shao, Wenqi Huang, Zhengxing |
| contents | Electrocardiograms (ECGs) are essential for diagnosing cardiovascular diseases. However, existing ECG-Report contrastive learning methods focus on whole-ECG and report alignment, missing the link between local ECG features and individual report tags. In this paper, we propose FG-CLEP (Fine-Grained Contrastive Language ECG Pre-training), which achieves fine-grained alignment between specific ECG segments and each tag in the report via tag-specific ECG representations. Furthermore, we found that nearly 55\% of ECG reports in the MIMIC-ECG training dataset lack detailed waveform features, which hinders fine-grained alignment. To address this, we introduce a coarse-to-fine training process that leverages large language models (LLMs) to recover these missing waveform features and validate the LLM outputs using a coarse model. Additionally, fine-grained alignment at the tag level, rather than at the report level, exacerbates the false negative problem, as different reports may share common tags. To mitigate this, we introduce a semantic similarity matrix to guide the model in identifying and correcting false negatives. Experiments on six datasets demonstrate that FG-CLEP significantly improves fine-grained alignment, outperforming state-of-the-art methods in both zero-shot prediction and linear probing. Meanwhile, the fine-grained reports we generate also enhance the performance of other methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-grained Contrastive Learning for ECG-Report Alignment with Waveform Enhancement Li, Haitao Liu, Che Ding, Zhengyao Liu, Ziyi Shao, Wenqi Huang, Zhengxing Signal Processing Artificial Intelligence Machine Learning Electrocardiograms (ECGs) are essential for diagnosing cardiovascular diseases. However, existing ECG-Report contrastive learning methods focus on whole-ECG and report alignment, missing the link between local ECG features and individual report tags. In this paper, we propose FG-CLEP (Fine-Grained Contrastive Language ECG Pre-training), which achieves fine-grained alignment between specific ECG segments and each tag in the report via tag-specific ECG representations. Furthermore, we found that nearly 55\% of ECG reports in the MIMIC-ECG training dataset lack detailed waveform features, which hinders fine-grained alignment. To address this, we introduce a coarse-to-fine training process that leverages large language models (LLMs) to recover these missing waveform features and validate the LLM outputs using a coarse model. Additionally, fine-grained alignment at the tag level, rather than at the report level, exacerbates the false negative problem, as different reports may share common tags. To mitigate this, we introduce a semantic similarity matrix to guide the model in identifying and correcting false negatives. Experiments on six datasets demonstrate that FG-CLEP significantly improves fine-grained alignment, outperforming state-of-the-art methods in both zero-shot prediction and linear probing. Meanwhile, the fine-grained reports we generate also enhance the performance of other methods. |
| title | Fine-grained Contrastive Learning for ECG-Report Alignment with Waveform Enhancement |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.11939 |