As part of the George B. Moody PhysioNet Challenge 2026, our team, MeDiTechDreamers, developed a framework for predicting a future diagnosis of cognitive impairment from polysomnography (PSG) using representations from neural sleep-staging models pretrained on large, heterogeneous PSG datasets. Modality-specific single-channel models independently extracted hypnodensity sequences from available electroencephalographic, electrooculographic, chin electromyographic, and electrocardiographic signals. The sequences were processed by a bidirectional long short-term memory network with attention pooling to produce a record-level embedding, which was fused with demographic, algorithmically derived, and engineered physiological features. Under leave-one-cohort-out cross-validation on the public training data, the selected configuration achieved a mean Challenge score of 0.650. On the hidden validation set, our method achieved a Challenge score of 0.702 (ranked 28th of 97 teams) and a prevalence-based reward of 0.242 (ranked 7th of 97 teams). These results suggest that modality-specific hypnodensity sequences integrated with complementary static features provide a practical basis for predicting future cognitive impairment from a single baseline PSG.