无需重放原始心电图，实现心电数据源的自主识别与专家模型保留。
Separating Expert Retention from Autonomous Source Inference in Raw-ECG-Replay-Free Continual ECG Deployment
- 冻结预训练特征，为每个新数据源添加独立分类器，避免参数冲突。
- 在无源标识时，通过融合两个最可能专家提升性能至0.7782，优于单一路由。
- 适用于无法保存原始心电图但需持续部署新数据源的医疗场景。
在多源心电图部署中，当早期原始心电图无法保留或重放时，模型需引入新数据源。本文提出IRFE-ECG，基于冻结的1024维ECGFounder特征构建增量专家库。每个新领域添加一个平衡Softmax线性专家，仅在保留的训练特征和领域标签上训练轻量级路由器。采用验证校准的边际规则融合两个最可能专家，而非选择单一路由专家。在CPSC、PTB-XL、Georgia和Chapman-Shaoxing数据集上，有源标识时专家选择达到0.7915±0.0036的宏平均F1，接近离线独立头参考值0.7885±0.0009，证明强源感知保留能力。无源ID时，MLP路由器得0.7756±0.0027，双专家融合达0.7782±0.0022，较硬路由提升0.0026，但置信区间包含零，表明提升有限。三种领域顺序下，双专家与理想情况差距稳定在0.0111–0.0133，揭示自主源推断仍是主要瓶颈。无需重放原始心电图，但保留冻结训练特征以更新路由器，故非完全内存自由。代码已开源。
原文摘要 · Abstract (English)
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 IRFE-ECG, 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 $0.7915\pm0.0036$ Macro-F1 and a matched offline independent-head reference reaches $0.7885\pm0.0009$, supporting strong source-aware expert retention. Without source IDs, an MLP router reaches $0.7756\pm0.0027$ and top-2 margin fusion reaches $0.7782\pm0.0022$. 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.Code is available at https://github.com/yufanlu221/IRFE-ECG.
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