Early identification of individuals at risk for cognitive decline represents a critical unmet need in clinical neurology. The George B. Moody PhysioNet Challenge 2026 tasks participants with predicting future diagnosis of mild cognitive impairment, Alzheimer's disease, or dementia from a single overnight polysomnography (PSG) recording and basic demographic data, scored by AUROC between confirmed cases and long-term cognitively intact controls. We developed a multi-stream feature extraction pipeline applied to the 622-record training set from the Human Sleep Project (three institutions: BIDMC, Emory, Stanford). Signals across ten channel groups (frontal, central, and occipital EEG; EOG; chin and leg EMG; ECG; respiratory flow and effort; and SpO2) were resampled to 128 Hz and segmented into 60-second epochs. Four hand-crafted feature sets were computed: spectral features (band power, relative power ratios, spectral entropy, and aperiodic slope across delta, theta, alpha, sigma, and beta bands); epoch statistics (mean, variance, trend, percentiles, and IQR); cross-channel coherence across clinically motivated channel pairs; and amplitude variability metrics. Instead of using raw demographic and annotation variables directly, we constructed interaction terms such as age x N3 deficit, BMI x AHI, and age x arousal index, then pruned the combined set iteratively based on cross-fold importance variance. In parallel, a deep learning encoder was trained on sequences of per-epoch descriptors spanning the full night, with epochs sampled for uniform temporal coverage. Penultimate-layer embeddings were extracted using out-of-fold inference to avoid leakage, concatenated with the pruned hand-crafted features, and passed to a regularised LightGBM classifier. Internal 10-fold stratified cross-validation yielded an AUROC of 0.900 using hand-crafted features alone, improving to 0.930 +/- 0.039 after adding deep learning embeddings. Official validation results are pending. Ongoing work focuses on additional deep feature streams and calibration under the realistic 5-15% prevalence of the validation and test sets.