发表机构
Medical AI Co., Ltd.（医疗人工智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对流式生物信号测试时自适应的样本选择难题，提出RECAST采样模块，从时间近期性等三方面选样，在血压数据集上提升精度，延迟极低，适配长期监测需求。
AI 中文摘要
流式生物信号因受试者不同及随时间漂移，导致在人群上训练的模型在长期监测中精度下降。测试时自适应（Test-Time Adaptation, TTA）可通过对传入样本更新模型实现在线个性化，但流数据中存在一个未解决的关键问题：哪些样本应驱动每次更新？使用所有缓冲样本会因无关片段模糊更新效果，仅用最新片段则会使更新带有噪声且不稳定。最有用的样本应是近期的、与当前生理状态对齐且足够可靠可用于学习的。我们提出RECAST（REcent & Context-Aware Sampling for TTA），这是一个用于缓冲式TTA框架的轻量级采样模块。RECAST从三个维度构建每个自适应批次：时间近期性、上下文相似性和预测可靠性，它仅改变所用样本，模型和训练目标保持不变。在两个血压数据集上，RECAST相比基线和消融实验提升了估计精度和趋势跟踪能力，在两个数据集上的患者级增益均具有统计学显著性，在常规基准上实现广泛改进，在急诊室场景中增益集中于最难处理的患者。RECAST保持实用性，在单个GPU和CPU核心上每片段仅增加亚秒级延迟。
英文摘要
Streaming biosignals vary across subjects and drift over time, so population-trained models lose accuracy during long-term monitoring. Test-time adaptation (TTA) enables online personalization by updating the model on incoming samples. But in a stream, a basic question is left open: \emph{which samples should drive each update?} Using all buffered samples blurs the update with irrelevant segments. Using only the latest segment makes the update noisy and unstable. The most useful samples are recent, aligned with the current physiological state, and reliable enough to learn from. We propose \textbf{RECAST} (REcent \& Context-Aware Sampling for TTA), a lightweight sampling module for buffered TTA frameworks. RECAST builds each adaptation batch from three signals: temporal recency, contextual similarity, and predictive reliability. It changes only which samples are used, leaving the model and the training objective unchanged. On two blood-pressure datasets, RECAST improves estimation accuracy and trend tracking over baselines and ablations. The per-patient gains are statistically significant on both datasets, with broad improvement on the regular benchmark and gains concentrated on the hardest patients in the emergency-department setting. RECAST stays practical, adding only sub-second latency per segment on a single GPU and CPU core.
