A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning
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 f...
| Authors | Jasmeet Singh Bindra, Siddharth Panwar |
|---|---|
| Primary category | cs.LG |
| Published | 7 June 2026 |
| Last updated at source | 15 June 2026 |
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