ECG-EGR: Event-Grounded Relational Reasoning for Clinical ECG Interpretation

Electrocardiography (ECG) is fundamental to cardiovascular diagnosis, and deep learning has substantially advanced automated ECG classification and report generation. However, most existing approaches encode ECGs into latent representations optimized for downstream prediction, leaving the correspondence between model representations, localized waveform evidence, and clinical findings largely implicit. We introduce ECG-EGR, an Event-Grounded Relational framework that factorizes ECG interpretation through a sparse, variable-cardinality set of waveform-supported Events. Each Event carries explicit lead-time support, an immutable semantic address, and a mutable reasoning state, separating evidence localization from contextual interpretation. ECG-EGR first discovers grounded Events and aligns their semantics with weak clinical supervision through unbalanced optimal transport, then relationally composes Event states to capture temporal and cross-lead dependencies while preserving their grounded identity. A language model accesses ECG information only through the composed Event representations, while a finding-conditioned retriever links generated findings to immutable Event semantics, enabling structured provenance from clinical findings to source Events and waveform regions. Extensive experiments demonstrate strong report generation performance, including a BLEU-1 score of 0.876, while achieving the strongest cross-task representation transfer among the compared methods.