PhysioTrace: Tracing Zero-Shot ECG Decisions through Clinical Observations to Waveform Evidence

ECG-language models enable flexible zero-shot diagnosis, yet their decisions remain difficult to interpret. Existing ECG explanations often focus on signal-level attribution that identifies *where* evidence lies in the waveform, while emerging ECG-language methods begin to expose *what* clinical observations support a diagnosis. However, these two views remain disconnected. We introduce **PhysioTrace**, a hierarchical explanation framework that makes ECG predictions traceable from diagnosis to clinical observation to waveform evidence. Its key idea is to use task-agnostic clinical observations as a shared intermediate representation: each diagnosis is decomposed into observation-level contributions, and each observation is grounded in the multilead ECG through observation-conditioned counterfactual attribution. Experiments across multiple ECG datasets and backbones show that PhysioTrace largely preserves zero-shot performance while providing faithful observation-level explanations and observation-specific waveform grounding. PhysioTrace therefore turns disconnected semantic and signal-level explanations into a unified and auditable reasoning structure. Code is available at https://anonymous.4open.science/r/PhysioTrace/.