Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

ABSTRACT Radio signals have emerged as a promising modality for cardiac monitoring, enabling fully contactless and operation‐free measurement. However, the lack of semantic understanding, i.e., the ability to interpret signal dynamics in clinically meaningful terms, remains a fundamental barrier to performance, interpretability, and clinical translation. Here, we introduce a semantic representation framework for radio‐based cardiac monitoring, grounded in an information bottleneck formulation. Our approach leverages intrinsic semantic invariance in the signal by integrating intra‐modal variability compression with cross‐modal semantic alignment. This enables the transformation of radio measurements into a structured representation space where cardiac semantics are encoded in an interpretable and clinically meaningful manner. We validate the proposed framework on a large‐scale cohort of 9518 outpatients. The learned semantic representations exhibit strong alignment with reference semantics. Building on these representations, our method achieves interpretable and clinical‐grade cardiac monitoring, including heart rhythm monitoring with a median inter‐beat interval error of 9.4 ms (95% CI: 9.3–9.6), and arrhythmia diagnosis for atrial fibrillation and premature beats, with F1 scores of 0.929 (95% CI: 0.906–0.948) and 0.867 (95% CI: 0.847–0.887), respectively. We also demonstrate the effectiveness of the proposed framework in practical long‐term, daily‐life deployment scenarios. These results highlight semantic representation as a key enabler for achieving reliable and transparent radio cardiac monitoring.