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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.18588 |
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| _version_ | 1866908554863050752 |
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| author | Jin, Jiarui Wang, Haoyu Lan, Xiang Li, Jun Cheng, Gaofeng Li, Hongyan Hong, Shenda |
| author_facet | Jin, Jiarui Wang, Haoyu Lan, Xiang Li, Jun Cheng, Gaofeng Li, Hongyan Hong, Shenda |
| contents | Recent unified models such as GPT-5 have achieved encouraging progress on vision-language tasks. However, these unified models typically fail to correctly understand ECG signals and provide accurate medical diagnoses, nor can they correctly generate ECG signals. To address these limitations, we propose UniECG, the first unified model for ECG capable of concurrently performing evidence-based ECG interpretation and text-conditioned ECG generation tasks. Through a decoupled two-stage training approach, the model first learns evidence-based interpretation skills (ECG-to-Text), and then injects ECG generation capabilities (Text-to-ECG) via latent space alignment. UniECG can autonomously choose to interpret or generate an ECG based on user input, significantly extending the capability boundaries of current ECG models. Our code and checkpoints will be made publicly available at https://github.com/PKUDigitalHealth/UniECG upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18588 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniECG: Understanding and Generating ECG in One Unified Model Jin, Jiarui Wang, Haoyu Lan, Xiang Li, Jun Cheng, Gaofeng Li, Hongyan Hong, Shenda Computation and Language Recent unified models such as GPT-5 have achieved encouraging progress on vision-language tasks. However, these unified models typically fail to correctly understand ECG signals and provide accurate medical diagnoses, nor can they correctly generate ECG signals. To address these limitations, we propose UniECG, the first unified model for ECG capable of concurrently performing evidence-based ECG interpretation and text-conditioned ECG generation tasks. Through a decoupled two-stage training approach, the model first learns evidence-based interpretation skills (ECG-to-Text), and then injects ECG generation capabilities (Text-to-ECG) via latent space alignment. UniECG can autonomously choose to interpret or generate an ECG based on user input, significantly extending the capability boundaries of current ECG models. Our code and checkpoints will be made publicly available at https://github.com/PKUDigitalHealth/UniECG upon acceptance. |
| title | UniECG: Understanding and Generating ECG in One Unified Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.18588 |