Department of Biostatistics, Institute of Biosciences, São Paulo State University (UNESP), Botucatu, São Paulo, Brazil.
National Institute for Space Research (INPE), Earth System Science Center (CCST), São José dos Campos, São Paulo, Brazil.
PLoS One. 2020 Jun 5;15(6):e0231169. doi: 10.1371/journal.pone.0231169. eCollection 2020.
Known as a degenerative and progressive dementia, Alzheimer's disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here-clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index-showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients' scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.
阿尔茨海默病（AD）被称为一种进行性和退行性痴呆，影响着全球约 2500 万老年人。这种疾病降低了人们的生产力，并限制了他们的日常生活。脑电图（EEG）是一种众所周知的神经生理 AD 生物标志物，它以时间序列的形式记录大脑的电活动，并使用信号处理技术进行分析。EEG 是非侵入性的、低成本的，具有高时间分辨率，并提供有关 AD 中大脑动态的有价值信息。在这里，我们提出了一种基于使用分位数图（QG）对 EEG 数据进行分类的原始方法。QG 将频率、幅度和相关性特征（例如 AD 患者的 EEG 数据）映射到网络的拓扑特征中。这里使用的五个拓扑网络度量——聚类系数、平均跳跃长度、介数中心性、模块性和拉普拉斯 Estrada 指数——表明 QG 模型可以区分健康受试者和 AD 患者，无论是睁眼还是闭眼。QG 方法还指出了哪些通道（对应于患者头皮上的 19 个不同位置）提供了最佳的区分能力。此外，对 delta、theta、alpha 和 beta 波结果的联合分析表明，所有研究中的 AD 患者都表现出明显的疾病症状，并且可能处于疾病晚期，这是预先已知的诊断，得到了我们研究的支持。这里呈现的结果证明了 QG 方法在分析复杂、非线性信号（如 EEG 从 AD 患者生成的信号）方面的有用性。
