Spectral Audit Framework Reveals Task-Dependent Aperiodic Reliance in Physiological Deep Learning Models
Researchers have developed a spectral audit framework revealing that deep learning models trained on physiological signals like EEG and ECG systematically rely on aperiodic 1/f-like background noise rather than clinically meaningful features. This reliance was found to be task-dependent — strongest in sleep-wake classification and cardiac abnormality detection, minimal for motor imagery — and persisted across six neural architectures and seven EEG foundation models even after controlling for age, sex, and recording era. The findings suggest that aperiodic signal components represent an underappreciated confound that could undermine the clinical validity and interpretability of physiological deep learning systems.
A preprint posted to arXiv introduces a spectral audit framework designed to test whether deep learning models for physiological time-series data are exploiting aperiodic broadband signal components — the 1/f-like background envelope that varies with arousal, age, and pathology — rather than the domain-specific features clinicians care about, such as oscillatory rhythms in EEG or morphological complexes in ECG. The framework combines aperiodic/periodic signal decomposition, phase-preserving Fourier interventions, sham controls, and simulation validation to isolate the contribution of aperiodic components to model performance. Across six neural architectures, flattening the aperiodic component caused balanced-accuracy drops exceeding 0.42 points for sleep-wake classification and 0.07–0.13 points for clinical abnormality detection, while motor imagery tasks showed minimal impact. Six of seven EEG foundation models showed statistically significant aperiodic reliance on clinical EEG data, and the effect persisted after controlling for demographic and recording-era confounds. Extending the audit to the PTB-XL ECG benchmark revealed performance drops of 0.32–0.36 after demographic matching, confirming the issue is not limited to EEG. The authors argue that aperiodic controls should become a standard component of model evaluation and interpretability pipelines for physiological deep learning. The paper is described as being prepared for submission to a peer-reviewed journal.
What's missing
As a preprint not yet peer-reviewed, the framework's generalizability has not been independently validated. The practical impact on real-world clinical deployment of affected models is not assessed.
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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