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

Abstract:In multi-source ECG deployment, new sources may arrive when earlier raw ECGs cannot be retained or replayed. Isolating source-specific classifiers on a frozen backbone prevents parameter interference, but source-unknown inference still requires selecting an appropriate expert. We study this distinction with IRFE-ECG, a controlled continual-deployment framework built on frozen 1024-dimensional ECGFounder features. Each arriving source adds an isolated Balanced-Softmax linear expert, while a lightweight router is re-fitted using retained frozen training features and source labels from previously observed sources. Rather than proposing a new routing architecture, the main contribution is to separate preserved expert performance from autonomous source inference and quantify the resulting deployment gap. Across CPSC, PTB-XL, Georgia, and Chapman-Shaoxing, source-aware expert selection reaches $0.7915 \pm 0.0036$ Macro-F1, close to a matched offline independent-head reference at $0.7885 \pm 0.0009$. Without source IDs, an MLP router reaches $0.7756 \pm 0.0027$, while top-2 margin fusion reaches $0.7782 \pm 0.0022$. The top-2 improvement is small (+0.0026) and not statistically significant under paired bootstrap. Across three domain orders, the top-2-to-oracle gap remains 0.0111-0.0133, indicating a persistent source-inference gap within this protocol. The results are record-level because reliable patient identifiers were unavailable. The method replays no raw ECGs, but it retains frozen feature vectors for router updates and is therefore raw-ECG-replay-free rather than memory-free. Code is publicly available at this https URL.

Artificial Intelligence (cs.AI)

arXiv:2607.01674 [cs.AI]

(or arXiv:2607.01674v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.01674 Focus to learn more arXiv-issued DOI via DataCite

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