发表机构
Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出具备医生先验知识的LuminaECG框架，将ECG解读转化为基于测量的视觉阅读，训练2B视觉语言骨干模型，在多基准测试中提升波形测量与诊断能力，实现跨数据集迁移并生成含预后信号的报告。
AI 中文摘要
心脏病医生通过定位波形成分、测量节律与间期模式，将这些结构化观察转化为诊断证据来解读心电图（ECG）。这种专家解读流程能否成为ECG智能体的有效先验尚不清楚。为解决该问题，我们提出LuminaECG，一种临床结构化的ECG推理框架，将ECG解读重新表述为基于测量的视觉阅读。ECG信号被渲染在标准心电图网格纸上，以保留临床解读所用的空间与尺度线索；明确划定P波、QRS复合波和T波的边界，并采用颜色编码分割将波形分解为离散的视觉测量基元。随后，我们使用低秩监督微调训练通用的2B视觉语言骨干模型，使其将这些基元与诊断推理关联，且无需修改架构。在公开、专有及ECG专家零样本基准测试中，LuminaECG在波形测量和诊断恢复两方面均有提升；在CODE-test基准上达到临床有意义的读者层级，无需重新训练即可跨地理多样化的ECG数据集迁移，且生成的报告结构包含涌现的预后信号。这些发现表明，有效的ECG智能体不仅需要更大的模型，还需要能保留可测量波形证据与临床知识间对齐关系的监督方式。
英文摘要
Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence. Whether this expert reading process can serve as an effective prior for ECG agents remains unclear. To address this question, we introduce LuminaECG, a clinically structured ECG reasoning framework that reformulates ECG interpretation as measurement-grounded visual reading. ECG signals are rendered on standard electrocardiographic grid paper to preserve the spatial and scale cues used in clinical reading. P-wave, QRS-complex, and T-wave boundaries are explicitly delineated, and color-coded segmentation decomposes the waveform into discrete visual measurement primitives. A general 2B vision-language backbone is then trained with low-rank supervised fine-tuning to associate these primitives with diagnostic reasoning, without architectural modification. Across open, proprietary, and ECG-specialist zero-shot baselines, LuminaECG improves both waveform measurement and diagnostic recovery. It reaches a clinically meaningful reader tier on the CODE-test benchmark, transfers across geographically diverse ECG datasets without retraining, and generates reports whose structure contains an emergent prognostic signal. These findings suggest that effective ECG agents require not only larger models, but supervision that preserves the alignment between measurable waveform evidence and clinical knowledge.
