AI 中文总结
针对多源ECG部署中无法保留原始数据的问题，提出基于冻结特征和增量专家库的方法，通过验证校准的边际融合规则减少源推断误差，实验表明专家保留效果接近离线参考，但自主源推断仍是主要瓶颈。
AI 中文摘要
在多源ECG部署中，当无法保留或重放早期原始ECG时，模型可能需要纳入新的数据源。冻结预训练骨干网络并为每个源分配独立的分类器可以防止参数干扰，但部署时在源元数据不可用的情况下仍需选择专家。我们通过\ours{}研究这一区别，该方法基于冻结的1024维ECGFounder特征构建增量专家库。每个到达的域添加一个平衡softmax线性专家，而一个轻量级路由器仅根据保留的训练特征和迄今为止观察到的源域标签进行拟合。一个经过验证校准的边际规则融合两个最可能的专家，而不是仅依赖单个路由专家。在CPSC、PTB-XL、Georgia和Chapman-Shaoxing数据集上，源感知的专家选择达到$0.7915\pm0.0036$ Macro-F1，匹配的离线独立头参考达到$0.7885\pm0.0009$，支持强源感知专家保留。没有源ID时，MLP路由器达到$0.7756\pm0.0027$，top-2边际融合达到$0.7782\pm0.0022$。top-2相对于硬MLP路由的增益很小（$+0.0026$），配对bootstrap的95%置信区间包含零。在三种域顺序下，top-2与oracle之间的差距保持在$0.0111$--$0.0133$，表明自主源推断是主要剩余瓶颈。没有重放原始ECG，但保留了冻结的训练特征用于路由器更新；因此该方法并非无记忆。
英文摘要
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 https://github.com/yufanlu221/IRFE-ECG.
CommentsSubmitted to BIBM 2026
