  Alzheimer's disease (AD) and sleep disorders exhibit a close association,
where disruptions in sleep patterns often precede the onset of Mild Cognitive
Impairment (MCI) and early-stage AD. This study delves into the potential of
utilizing sleep-related electroencephalography (EEG) signals acquired through
polysomnography (PSG) for the early detection of AD. Our primary focus is on
exploring semi-supervised Deep Learning techniques for the classification of
EEG signals due to the clinical scenario characterized by the limited data
availability. The methodology entails testing and comparing the performance of
semi-supervised SMATE and TapNet models, benchmarked against the supervised XCM
model, and unsupervised Hidden Markov Models (HMMs). The study highlights the
significance of spatial and temporal analysis capabilities, conducting
independent analyses of each sleep stage. Results demonstrate the effectiveness
of SMATE in leveraging limited labeled data, achieving stable metrics across
all sleep stages, and reaching 90% accuracy in its supervised form. Comparative
analyses reveal SMATE's superior performance over TapNet and HMM, while XCM
excels in supervised scenarios with an accuracy range of 92 - 94%. These
findings underscore the potential of semi-supervised models in early AD
detection, particularly in overcoming the challenges associated with the
scarcity of labeled data. Ablation tests affirm the critical role of
spatio-temporal feature extraction in semi-supervised predictive performance,
and t-SNE visualizations validate the model's proficiency in distinguishing AD
patterns. Overall, this research contributes to the advancement of AD detection
through innovative Deep Learning approaches, highlighting the crucial role of
semi-supervised learning in addressing data limitations.
