AI RESEARCH
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
arXiv CS.LG
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ArXi:2605.27583v1 Announce Type: new Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signals with clinical reports to incorporate diagnostic semantics, but clinical reports often fail to preserve the rich physiological structure of ECG waveforms, particularly across multiple levels of abstraction ranging from coarse diagnostic categories to fine-grained morphology.