不同诊断模型下，自适应心电图导联选择效果差异显著，需联合优化传感与诊断。
Evaluator-Dependent Patient-Adaptive ECG Lead-Channel Allocation
- 基于患者状态动态分配心电导联，但效果依赖具体诊断模型
- 在4导联预算下，强模型使自适应策略从负收益转为正收益
- 结果提示传感与诊断应协同设计，非独立优化
针对心电图导联选择的患者特异性采集策略可超越通用固定方案，通过匹配每位患者的实时心脏状态分配通道资源。然而，某一通道的边际价值取决于下游诊断评估器，因此在特定评估器上学习的策略在更换评估器后未必有效。本文在PTB-XL数据集上对两种策略（ECG-on-Demand与MGA）进行实验，其原始轨迹由受控掩码逻辑评估器训练，再以更具预测力的掩码波形ResNet1D评分。通过穷举搜索构建与各评估器匹配的全局固定基准，实现清晰的交互对比。在保留测试折中，$k=4$时，ECG-on-Demand在控制评估器下得$D_‘C’=-0.011$（倾向自适应），在强评估器下变为$D_‘S’=+0.029$（倾向固定），产生NLL交互值$+0.041$（95%置信区间$[+0.030, +0.050]$）。在两个策略、五个预算、三种概率指标下共30组交互估计均呈正值，且置信区间不包含零。三次后验敏感性分析——共参考评分、强评估器混合训练、评估器对齐的Strong-MGA训练——均保持正向交互区间，削弱了参考选择与掩码分布伪影的可能性。评估器对齐训练虽缩小差距但未消除。结果表明，自适应心电导联分配必须与其目标诊断模型共同开发与验证，联合优化的感知-诊断系统仍是开放问题。
原文摘要 · Abstract (English)
Patient-conditioned acquisition policies for ECG lead-channel selection can outperform population-wide fixed protocols by tailoring the channel budget to each patient's observed cardiac state. However, the value of acquiring any given channel is defined relative to a downstream diagnostic evaluator, so marginal utilities learned under one evaluator need not transfer when the evaluator is replaced. We study this evaluator dependence empirically on PTB-XL by freezing two policies (ECG-on-Demand and MGA) trained with a controlled arbitrary-mask logistic evaluator, then scoring their unchanged acquisition trajectories with a more predictive masked raw-waveform ResNet1D. Exhaustive search provides metric-matched population-wide fixed comparators separately for each evaluator, enabling a clean interaction contrast. At budget $k=4$ on a held-out evaluation fold, ECG-on-Demand shifts from $D_\mathrm{C}=-0.011$ (favoring adaptive under the controlled evaluator) to $D_\mathrm{S}=+0.029$ (favoring fixed under the strong evaluator), yielding an NLL interaction of $+0.041$ (95% CI $[+0.030, +0.050]$). Across two policies, five budgets, and three probabilistic metrics, all 30 interaction estimates are positive with paired confidence intervals excluding zero. Three post-hoc sensitivity analyses -- common-reference scoring, training the strong evaluator on a mixture of policy-generated and random masks, and evaluator-aligned Strong-MGA policy training -- each preserve a positive interaction interval, making reference-choice and mask-distribution artifacts less plausible explanations. Evaluator-aligned training reduces but does not eliminate the gap. These results indicate that adaptive ECG channel allocation should be developed and validated jointly with its intended diagnostic backbone, and that jointly optimized sensing-diagnosis systems remain an open problem.
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