Separating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment

arXiv:2607.01674v1 Announce Type: new Abstract: In multi-source ECG deployment, models may need to incorporate new data sources when earlier raw ECGs cannot be retained or replayed. Freezing a pretrained backbone and assigning each source an isolated classifier prevents parameter interference, but deployment still requires selecting an expert when source metadata are unavailable. We study this distinction through ours{}, an incremental expert bank built on frozen 1024-dimensional ECGFounder features. Each arriving domain adds a balanced-softmax linear expert, while a lightweight router is fitted only on retained training features and domain labels from sources observed so far. A validation-calibrated margin rule fuses the two most likely experts instead of committing to a single routed expert. On CPSC, PTB-XL, Georgia, and Chapman-Shaoxing, source-aware expert selection reaches 0.7915pm0.0036 Macro-F1 and a matched offline independent-head reference reaches 0.7885pm0.0009, supporting strong source-aware expert retention. Without source IDs, an MLP router reaches 0.7756pm0.0027 and top-2 margin fusion reaches 0.7782pm0.0022. The top-2 gain over hard MLP routing is small (+0.0026), with a 95% confidence interval from paired bootstrap that includes zero. Across three domain orders, the top-2-to-oracle gap remains 0.0111--0.0133, identifying autonomous source inference as the main remaining bottleneck. No raw ECGs are replayed, but frozen training features are retained for router updates; the method is therefore not memory-free.

Source: arXiv cs.AI | 2026-07-03