Deep learning on physiological time series is interpreted through domain-specific features -- oscillatory rhythms in EEG, morphological complexes in ECG -- yet these signals sit atop a broadband aperiodic 1/f-like envelope that covaries with arousal, age, and pathology. We introduce a spectral audit framework combining aperiodic/periodic decomposition, phase-preserving Fourier interventions, sham controls, and simulation validation. Aperiodic reliance was task-dependent and architecture-general: across six neural architectures, flattening drops exceeded 0.42 balanced-accuracy points for sleep-wake classification, reached 0.07-0.13 for clinical abnormality detection, and remained minimal for motor imagery. Six of seven EEG foundation models showed FDR-significant aperiodic reliance on clinical EEG; age/sex and recording-era controls reduced but did not eliminate the effect. Applying the audit to PTB-XL ECG revealed neural drops of 0.32--0.36 persisting after demographic matching, confirming this confound class extends beyond EEG. Aperiodic controls should become standard for interpretable physiological time-series deep learning.

翻译：对生理时间序列的深度学习通常通过特定领域的特征进行解释——脑电图中的振荡节律与心电图中的形态学复合波——然而这些信号位于一个与觉醒、年龄和病理状态共变的宽带非周期1/f型包络之上。本文提出一种频谱审计框架，融合非周期/周期分解、保相傅里叶干预、假性对照及仿真验证。非周期依赖性呈现任务依赖性与架构泛化性：在六种神经架构中，平化下降导致睡眠-清醒分类的平衡精度损失超过0.42点，临床异常检测达0.07-0.13区间，而运动想象中则最小。七种脑电图基础模型中有六种在临床脑电图中显示经错误发现率校正后显著的非周期依赖性；年龄/性别及记录时期对照组可减弱但未能消除此效应。将该审计应用于PTB-XL心电图数据集，发现经人口学匹配后神经下降值仍维持0.32-0.36，证实此类混淆因素已扩展至脑电图范畴之外。非周期控制应成为可解释性生理时间序列深度学习的标准配置。