Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease
Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease
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DOI：
10.3389/fpsyt.2020.00255
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发表时间：
2020-04-07
影响因子：
4.7
通讯作者：
Takahashi, Tetsuya
中科院分区：
文献类型：
--
作者：
Nobukawa, Sou;Yamanishi, Teruya;Takahashi, Tetsuya
脑电图（EEG）作为一种潜在的阿尔茨海默病（AD）的诊断方法已被研究了很长时间。AD的病理进展导致皮质分离。这些断开可能表现为功能连接的改变，通过不同大脑区域之间的同步程度来衡量，以及由广泛的大脑区域之间的相互作用产生的复杂行为的改变。最近，机器学习方法，如聚类算法和分类方法，已被用来检测功能连接中与疾病相关的变化，并对这些变化的特征进行分类。虽然脑电信号的复杂性也可以反映AD相关的变化，但很少有机器学习研究关注复杂性的变化。因此，在这项研究中，我们使用不同的机器学习方法比较了EEG信号检测AD特征的能力，其中一种方法侧重于功能连接性，另一种侧重于信号复杂性。我们检查了功能连接，估计在健康老年参与者[健康对照（HC）]和AD患者的EEG信号的相位滞后指数（PLI）。我们使用多尺度熵估计信号复杂度。利用支持向量机，我们比较了AD的识别精度的基础上在每个频段的功能连接和复杂性组件。此外，我们评估了同步和复杂性之间的关系。α、β和γ带的功能连接性的识别准确度显著高（AUC 1.0），并且复杂性的识别准确度足够高（AUC 0.81）。此外，功能连接和复杂性之间的关系表现出各种时间尺度和区域特定的依赖性在HC参与者和AD患者。因此，功能连接性和复杂性的结合可能反映了AD复杂的病理过程。将这两种机器学习方法结合应用于神经生理学数据，可以为健康大脑和病理条件下的神经网络过程提供新的理解。
Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among wide-spread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-and-regional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods to neurophysiological data may provide a novel understanding of the neural network processes in both healthy brains and pathological conditions.