基于PSG信号的阿尔茨海默病检测
Alzheimer's disease detection in PSG signals
AI总结：
本研究利用PSG采集的睡眠EEG信号检测早期阿尔茨海默病，比较半监督SMATE、TapNet与监督XCM及无监督HMM，证明SMATE能在标注数据有限时实现稳定且优越的分类表现。
摘要：
阿尔茨海默病（AD）与睡眠障碍之间存在密切关联，其中睡眠模式的紊乱往往先于轻度认知障碍（MCI）和早期AD的发病。本研究深入探讨了利用通过多导睡眠图（PSG）采集的睡眠相关脑电图（EEG）信号进行AD早期检测的潜力。由于临床场景中数据可用性有限，我们的主要重点是探索用于EEG信号分类的半监督深度学习技术。研究方法包括测试和比较半监督SMATE与TapNet模型的性能，并以监督式XCM模型和无监督隐马尔可夫模型（HMM）作为基准。该研究强调了空间和时间分析能力的重要性，并对每个睡眠阶段进行了独立分析。结果证明了SMATE在利用有限标注数据方面的有效性，其在所有睡眠阶段均取得了稳定的指标，并且其监督形式达到了90%的准确率。比较分析表明，SMATE的性能优于TapNet和HMM，而XCM在监督场景中表现出色，准确率范围为92%至94%。这些发现凸显了半监督模型在AD早期检测中的潜力，尤其是在克服与标注数据稀缺相关的挑战方面。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可视化验证了该模型区分AD模式的能力。总体而言，本研究通过创新的深度学习方法为AD检测的进步作出了贡献，强调了半监督学习在解决数据局限性方面的关键作用。
英文摘要：
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.
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