ECG-text pretraining offers transferable electrocardiogram (ECG) representations for zero-shot diagnosis, but its supervision is inherently asymmetric: clinical reports contain sparse diagnostic phrases, while ECG signals provide rich and redundant evidence across leads and time. As a result, cross-modal learning can collapse into coarse prototype matching, relying on a few highly aligned ECG tokens while overlooking complementary evidence. Existing methods enrich text supervision or perturb ECG inputs, yet rarely model how cross-modal correspondence should adapt as the available ECG evidence changes. We propose NESA, a Non-stationary Evidence-aware Semantic Alignment framework that replaces static prototype matching with an evolving alignment trajectory. NESA estimates each ECG token's contribution to the current cross-modal alignment and progressively removes dominant tokens in latent space to expose residual evidence. To preserve semantic correspondence under evidence removal, it constructs adaptive alignment states from text-derived clinical semantics, full-view ECG context, and residual ECG evidence. This trajectory guides the ECG encoder to capture complementary diagnostic cues rather than rely on a small set of tokens. Experiments with MIMIC-IV-ECG pretraining and three downstream benchmarks show improvements in zero-shot diagnosis and label-efficient transfer. Dominant-token deletion analysis further indicates that NESA preserves cross-modal alignment after highly aligned ECG evidence is removed, supporting more distributed ECG-text grounding.

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