As part of the George B. Moody PhysioNet Challenge 2026, team Better Call Sandman developed two approaches for predicting future cognitive impairment from polysomnography. The first combined engineered physiological and sleep-related features with XGBoost, while the second used a convolutional neural network (CNN) with foundation-model pretraining and transfer learning. XGBoost achieved a mean cross-validation age-conditioned area under the receiver operating characteristic curve (AUROC) of 0.870 on the public training data. On the official hidden validation set, XGBoost achieved an age-conditioned AUROC of 0.636, compared with 0.574 for the CNN, ranking 191st of 514 submissions. Based on this performance, XGBoost was selected as our final model for evaluation on the hidden test set. Although the feature-based approach performed better in the Challenge, our CNN experiments indicate that transfer learning remains a promising direction, particularly with richer multimodal inputs and improved generalization.