We present our entry, submitted by team Koalalition, to "Screening for Cognitive Impairment During Sleep Studies: The George B. Moody PhysioNet Challenge 2026". Rather than learning directly from raw multi-channel signals, we extract a bank of 136 literature-motivated physiological features from the measurements, stratified by sleep stage. We further compute an age-residual feature for a curated set of 23 biomarkers with established age trends. The resulting tabular representation is classified with a bagging ensemble of 15 gradient-boosted trees (XGBoost), with hyperparameters selected by randomized search under stratified cross-validation. On the official challenge validation set, our submitted model trained on the large version of the dataset reaches an age-conditioned AUROC of 0.669, ranking in place 136.