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.

翻译：阿尔茨海默病（AD）与睡眠障碍密切相关，睡眠模式紊乱常先于轻度认知障碍（MCI）和早期AD出现。本研究探索了通过多导睡眠图（PSG）获取的睡眠相关脑电图（EEG）信号在AD早期检测中的应用潜力。鉴于临床场景中数据有限的特性，我们主要聚焦于半监督深度学习技术在EEG信号分类中的运用。研究方法包括测试并比较半监督SMATE与TapNet模型（以监督XCM模型和无监督隐马尔可夫模型（HMM）为基准）的性能表现。研究强调了时空分析能力的重要性，并对各睡眠阶段进行了独立分析。结果表明，SMATE在有效利用有限标记数据方面具有显著优势，在所有睡眠阶段均保持稳定指标，其监督形式准确率达90%。对比分析显示，SMATE性能优于TapNet和HMM，而XCM在监督场景中表现突出，准确率范围达92-94%。这些发现揭示了半监督模型在AD早期检测中的潜力，特别是在克服标记数据稀缺挑战方面。消融实验证实了时空特征提取在半监督预测性能中的关键作用，t-SNE可视化验证了模型区分AD模式的能力。总体而言，本研究通过创新的深度学习方法推动了AD检测技术的发展，凸显了半监督学习在应对数据局限性中的核心作用。