Wearable devices enable continuous, population-scale monitoring of physiological signals, such as photoplethysmography (PPG), creating new opportunities for data-driven clinical assessment. Time-series extrinsic regression (TSER) models increasingly leverage PPG signals to estimate clinically relevant outcomes, including heart rate, respiratory rate, and oxygen saturation. For clinical reasoning and trust, however, single point estimates alone are insufficient: clinicians must also understand whether predictions are stable under physiologically plausible variations and to what extent realistic, attainable changes in physiological signals would meaningfully alter a model's prediction. Counterfactual explanations (CFE) address these "what-if" questions, yet existing time series CFE generation methods are largely restricted to classification, overlook waveform morphology, and often produce physiologically implausible signals, limiting their applicability to continuous biomedical time series. To address these limitations, we introduce EvoMorph, a multi-objective evolutionary framework for generating physiologically plausible and diverse CFE for TSER applications. EvoMorph optimizes morphology-aware objectives defined on interpretable signal descriptors and applies transformations to preserve the waveform structure. We evaluated EvoMorph on three PPG datasets (heart rate, respiratory rate, and oxygen saturation) against a nearest-unlike-neighbor baseline. In addition, in a case study, we evaluated EvoMorph as a tool for uncertainty quantification by relating counterfactual sensitivity to bootstrap-ensemble uncertainty and data-density measures. Overall, EvoMorph enables the generation of physiologically-aware counterfactuals for continuous biomedical signals and supports uncertainty-aware interpretability, advancing trustworthy model analysis for clinical time-series applications.

翻译：可穿戴设备使得对光电容积脉搏波（PPG）等生理信号进行连续、群体规模的监测成为可能，为数据驱动的临床评估创造了新机遇。时间序列外生回归（TSER）模型越来越多地利用PPG信号来估计临床相关指标，包括心率、呼吸频率和血氧饱和度。然而，对于临床推理和信任建立，仅提供单点估计值是不够的：临床医生还必须理解预测在生理学上合理的变化下是否稳定，以及生理信号在现实可实现的改变会在多大程度上显著影响模型的预测。反事实解释（CFE）正是针对这类"假设"问题而提出的，但现有的时间序列CFE生成方法大多局限于分类任务，忽略了波形形态学特征，且常产生生理学上不可信的信号，这限制了其在连续生物医学时间序列中的应用。为克服这些局限，我们提出了EvoMorph——一个为TSER应用生成生理学可信且多样化CFE的多目标进化框架。EvoMorph优化了基于可解释信号描述符定义的形态感知目标，并通过施加变换以保持波形结构。我们在三个PPG数据集（心率、呼吸频率和血氧饱和度）上针对最近异类邻居基线评估了EvoMorph。此外，通过案例研究，我们通过将反事实敏感性与自助法集成不确定性及数据密度度量相关联，评估了EvoMorph作为不确定性量化工具的有效性。总体而言，EvoMorph能够为连续生物医学信号生成具有生理学感知的反事实解释，并支持不确定性感知的可解释性，从而推动了临床时间序列应用中可信模型分析的发展。