School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China.
J Neural Eng. 2022 May 11;19(3). doi: 10.1088/1741-2552/ac697d.
Electroencephalography (EEG) is a potential source of downstream biomarkers for the early diagnosis of Alzheimer's disease (AD) due to its low-cost, noninvasive, and portable advantages. Accurately detecting AD-induced patterns from EEG signals is essential for understanding AD-related neurodegeneration at the EEG level and further evaluating the risk of AD at an early stage. This paper proposes a deep learning-based, functional explanatory framework that probes AD abnormalities from short-sequence EEG data.The framework is a learning-based automatic detection system consisting of three encoding pathways that analyze EEG signals in frequency, complexity, and synchronous domains. We integrated the proposed EEG descriptors with the neural network components into one learning system to detect AD patterns. A transfer learning-based model was used to learn the deep representations, and a modified generative adversarial module was attached to the model to overcome feature sparsity. Furthermore, we utilized activation mapping to obtain the AD-related neurodegeneration at brain rhythm, dynamic complexity, and functional connectivity levels.The proposed framework can accurately (100%) detect AD patterns based on our raw EEG recordings without delicate preprocessing. Meanwhile, the system indicates that (a) the power of different brain rhythms exhibits abnormal in the frontal lobes of AD patients, and such abnormality spreads to central lobes in the alpha and beta rhythms, (b) the difference in nonlinear complexity varies with the temporal scales, and (c) all the connections of pair-wise brain regions except bilateral temporal connectivity are weak in AD patterns. The proposed method outperforms other related methods in detection performance.We provide a new method for revealing abnormalities and corresponding localizations in different feature domains of EEG from AD patients. This study is a significant foundation for our future work on identifying individuals at high risk of AD at an early stage.
脑电图（EEG）具有低成本、非侵入性和便携性等优势，因此有可能成为阿尔茨海默病（AD）早期诊断的下游生物标志物。从 EEG 信号中准确检测 AD 引起的模式对于理解 EEG 水平上与 AD 相关的神经退行性变以及进一步评估 AD 的早期风险至关重要。本文提出了一种基于深度学习的功能解释框架，从短序列 EEG 数据中探测 AD 异常。该框架是一个基于学习的自动检测系统，由三个编码途径组成，分别分析 EEG 信号的频率、复杂度和同步域。我们将提出的 EEG 描述符与神经网络组件集成到一个学习系统中，以检测 AD 模式。采用基于迁移学习的模型来学习深度表示，并附加一个修改后的生成对抗模块来克服特征稀疏性。此外，我们利用激活映射来获得与脑节律、动态复杂性和功能连接水平相关的 AD 相关神经退行性变。
所提出的框架可以基于我们的原始 EEG 记录准确地（100%）检测 AD 模式，而无需进行精细的预处理。同时，该系统表明：（a）AD 患者的不同脑节律的功率在额叶中表现异常，这种异常在 alpha 和 beta 节律中扩散到中央叶；（b）非线性复杂度的差异随时间尺度而变化；（c）AD 模式中除双侧颞部连接外，所有脑区之间的连接都较弱。与其他相关方法相比，所提出的方法在检测性能方面表现更好。我们提供了一种新的方法，可以从 AD 患者的 EEG 不同特征域中揭示异常和相应的定位。这项研究为我们未来在早期识别 AD 高危个体的工作奠定了重要基础。
