Spectral Audit Framework Reveals Task-Dependent Aperiodic Reliance in Physiological Deep Learning Models
Researchers introduced a spectral audit framework that reveals deep learning models trained on EEG and ECG signals rely heavily on aperiodic (broadband 1/f-like) components rather than clinically meaningful oscillatory features. The framework combines aperiodic/periodic decomposition with phase-preserving interventions and sham controls, showing task-dependent reliance patterns across six neural architectures. This finding suggests aperiodic controls should become standard practice in physiological time-series deep learning to improve model interpretability and clinical reliability.
A new spectral audit framework demonstrates that deep learning models applied to physiological signals like EEG and ECG rely significantly on aperiodic broadband components that correlate with arousal, age, and pathology—features that may not be clinically meaningful. Testing across six neural architectures revealed task-dependent patterns: aperiodic reliance was substantial for sleep-wake classification (>0.42 balanced-accuracy point drops) and clinical abnormality detection (0.07-0.13 drops), but minimal for motor imagery tasks. The framework employs phase-preserving Fourier interventions, sham controls, and simulation validation to isolate aperiodic contributions. Six of seven EEG foundation models showed statistically significant aperiodic reliance on clinical data, and this confound persisted even after controlling for age, sex, and recording era. Application to the PTB-XL ECG dataset confirmed the effect extends beyond EEG, with neural performance drops of 0.32–0.36 remaining after demographic matching.
What's missing
The preprint does not discuss potential clinical implications or whether models with reduced aperiodic reliance maintain diagnostic accuracy in real-world deployment. Additionally, the paper does not address whether domain-specific architectures designed to suppress aperiodic components might improve generalization across different recording conditions or patient populations.
What different sources said
- arXiv cs.LGCenter
A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning
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