Cross-Site Prediction of Future Cognitive Impairment from Polysomnography Using Hybrid Physiological Representations

alireza khorramfard1, NAIMAHMED NESARAGI2, Daniel Teichmann3, Abdolrahman Peimankar4
1Department of Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran, 2OsloUniversityHospital,TheIntereventionCenter, 3SDU Health Informatics and Technology, Maersk Mc-Kinney Moeller Institute, SDU University of Southern Denmark, 4University of Southern Denmark


Abstract

For the 2026 George B. Moody PhysioNet Challenge, we developed a cross-site framework for predicting cognitive impairment 1--6 years after polysomnography (PSG) by combining frozen pretrained multimodal PSG representations with clinically derived physiological features. Model development used leave-one-site-out validation across three centres, followed by official evaluation on an unseen centre. The hybrid ranking pathway achieved an Age-conditioned AUROC of 0.708, Age-weighted AUROC of 0.755, AUROC of 0.799, and AUPRC of 0.273. However, its initial binary operating point generalized poorly, yielding a Reward of -0.293. Because threshold adjustment alone did not resolve this discrepancy, we decoupled continuous risk ranking from binary classification while preserving the ranking pathway. A clinically restricted ExtraTrees decision head using sleep, oxygenation, and age-relative features increased Reward to 0.156 while leaving all ranking metrics unchanged. These findings indicate that cross-site discrimination and threshold-dependent decision performance can generalize differently, and that a rich hybrid representation for risk ranking can be effectively complemented by a simpler clinical decision pathway.