Physiology-Guided Age-Stage-Consistent Diffusion for Sleep EEG Generation

Conditional diffusion models can generate physiological time series that appear realistic in spectral and feature statistics; however, marginal realism does not establish whether age-related changes remain compatible with an assigned sleep stage. We study this globally realistic yet conditionally contradictory behavior as age–stage physiological inconsistency in sleep EEG. To address it, we propose Phy-DiffKAN, which represents condition-dependent physiological structure as a factorized prior field over an interpretable 12-dimensional coordinate space. The prior field decomposes age, recorded sex, sleep stage, and their pairwise interactions into six branches, smoothly approximates the finite-anchor states of each branch, and projects them into physiology-structured tokens for a Transformer diffusion denoiser. Branch-drop and branch-mismatch constraints further enforce branch necessity and value specificity. We evaluate Phy-DiffKAN on a subject-disjoint Sleep-EDF cohort with controlled demographic allocation and complete per-subject stage coverage, using overall generation quality, fixed-stage preservation, and age-edit responsiveness. Phy-DiffKAN achieves an FID of 0.53, a feature-manifold F1 score of 0.78, a LogPSD distance of 0.06, and a five-stage balanced accuracy of 0.87. Under young$\to$old REM reassignment with fixed diffusion noise, Phy-DiffKAN preserves REM-stage identity, suppresses leakage into N2, and retains near-real age decodability; complementary N1 age sweeps show the same stage-preservation pattern beyond REM.