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arXiv · 2606.08583

A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning

Jasmeet Singh Bindra·Siddharth Panwar
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Abstract

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.

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Jasmeet Singh Bindra, Siddharth Panwar. 2026-06-15. A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning. https://arxiv.org/abs/2606.08583

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