一个模型同时生成心电图并解释其医学意义，助力医学生理解。
UniECG: Understanding and Generating ECG in One Unified Model
- 用多模态数据训练，让模型理解心电图信号、图像与文本的关系。
- 可依据文字描述生成对应的心电图波形，支持案例式学习。
- 适合医学教育场景，非临床诊断工具，强调互动教学价值。
心电图解读是医学教育的基础技能，但学生仅靠静态示例难以将波形特征与诊断推理关联。本文提出UniECG，一种支持双向交互的心电图教育模型：给定心电图信号或图像，它可生成基于证据的解释；给定文字学习目标，它能生成对应的典型心电图实例，促进基于案例的学习。模型采用两阶段设计：第一阶段利用心电图信号-图像-文本数据学习具象化解释能力；第二阶段引入特殊心电图生成标记，并对齐至预训练的文本条件心电图扩散模型，实现可控的信号级生成。通过基于证据的解释与生成导向的定性分析评估，验证了其在解释与案例学习中的潜力。UniECG旨在作为教育辅助工具，推动交互式AI辅助心电图学习，而非临床诊断系统。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) interpretation is a fundamental skill in medical education, yet students often need more than static examples to connect waveform evidence with diagnostic reasoning. This paper presents UniECG as a step toward interactive ECG education. UniECG supports two complementary learning interactions: given an ECG signal or image, it generates an evidence-based explanation; given a textual learning objective, it generates a corresponding ECG signal example for case-based learning. The model follows a two-stage design. First, it learns grounded ECG explanation from ECG signal--image--text data. Second, it introduces special ECG generation tokens and aligns their hidden representations with a pretrained text-conditioned ECG diffusion model, enabling controllable signal-level ECG generation. We evaluate UniECG through grounded ECG explanation and generation-oriented qualitative analysis, examining its potential to support explanation and case-based learning. UniECG is intended as an educational aid and a research step toward interactive AI-assisted ECG learning, rather than a clinically validated diagnostic system.
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