UniECG: Understanding and Generating ECG in One Unified Model
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Computer Science > Computation and Language
Title:UniECG: Understanding and Generating ECG in One Unified Model
Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2509.18588 [cs.CL] |
| (or arXiv:2509.18588v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2509.18588
arXiv-issued DOI via DataCite
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Submission history
From: Jiarui Jin [view email][v1] Tue, 23 Sep 2025 03:15:53 UTC (3,309 KB)
[v2] Wed, 17 Jun 2026 07:22:49 UTC (2,080 KB)
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