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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2412.04067 |
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| _version_ | 1866909417360850944 |
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| author | Bleich, Amnon Linnemann, Antje Diem, Bjoern H. Conrad, Tim OF |
| author_facet | Bleich, Amnon Linnemann, Antje Diem, Bjoern H. Conrad, Tim OF |
| contents | Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HCPs) as training data. These reports, while often inconsistent, provide a valuable foundation for automated learning. We introduce an encoder-decoder-based method that uses these reports to train models to generate detailed descriptions of ECG episodes. This represents a significant advancement in ECG analysis automation, with potential applications in zero-shot classification and automated clinical decision support.
The model is tested on various datasets, including both 1- and 12-lead ECGs. It significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.53% compared to 24.51% achieved by the reference model. Furthermore, several key design choices are discussed, providing a comprehensive overview of current challenges and innovations in this domain.
The source codes for this research are publicly available in our Git repository https://git.zib.de/ableich/ecg-comment-generation-public |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04067 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning Bleich, Amnon Linnemann, Antje Diem, Bjoern H. Conrad, Tim OF Computation and Language Artificial Intelligence Recent advances in deep learning and natural language generation have significantly improved image captioning, enabling automated, human-like descriptions for visual content. In this work, we apply these captioning techniques to generate clinician-like interpretations of ECG data. This study leverages existing ECG datasets accompanied by free-text reports authored by healthcare professionals (HCPs) as training data. These reports, while often inconsistent, provide a valuable foundation for automated learning. We introduce an encoder-decoder-based method that uses these reports to train models to generate detailed descriptions of ECG episodes. This represents a significant advancement in ECG analysis automation, with potential applications in zero-shot classification and automated clinical decision support. The model is tested on various datasets, including both 1- and 12-lead ECGs. It significantly outperforms the state-of-the-art reference model by Qiu et al., achieving a METEOR score of 55.53% compared to 24.51% achieved by the reference model. Furthermore, several key design choices are discussed, providing a comprehensive overview of current challenges and innovations in this domain. The source codes for this research are publicly available in our Git repository https://git.zib.de/ableich/ecg-comment-generation-public |
| title | Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.04067 |