ClinStab: Stability-Oriented Learning for Medical Time Series via Dual-Stream Alignment

Deep models for medical time series can achieve strong benchmark accuracy yet remain brittle under acquisition artifacts, physiological variability, and low-resource training conditions. We present \textbf{ClinStab}, a Medformer-based framework for EEG and ECG classification that studies whether channel-aware dual-stream interaction and perturbation-aware optimization can improve the clean/robustness tradeoff. The proposed \textbf{S}alience-\textbf{C}ontext \textbf{D}ual \textbf{M}odulation (SCDM) module uses energy as a routing prior: high-energy channel-aligned components receive attentive modeling, while residual components remain trainable through a lightweight context pathway. ClinStab then combines clean supervision, perturbed supervision, prediction consistency, and teacher-guided stabilization under adversarial perturbations. Across four public datasets and 11 baselines, ClinStab improves accuracy over Medformer on all datasets and improves selected robustness metrics under the perturbation settings studied here, while exposing tradeoffs on PTB ranking metrics and soft-routing alternatives. These results support ClinStab as a practical same-backbone stability intervention, not as evidence of universal clinical robustness.