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首页> 外文期刊>IEEE transactions on industrial informatics >Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects
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Brain Network Analysis of Compressive Sensed High-Density EEG Signals in AD and MCI Subjects

机译：AD和MCI受试者中压缩感知的高密度EEG信号的脑网络分析

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摘要

Alzheimer's disease (AD) is a neurodegenerative disorder that causes a loss of connections between neurons. The goal of this paper is to construct a complex network model of the brain-electrical activity, using high-density EEG (HD-EEG) recordings, and to compare the network organization in AD, mild cognitive impaired (MCI), and healthy control (CNT) subjects. The HD-EEG of 16 AD, 16 MCI, and 12 CNT was recorded during an eye-closed resting state. The permutation disalignment index (PDI) was used to describe the dissimilarity between EEG signals and to construct the connection matrices of the network model. The three groups were found to have significantly different (p < 0.001) characteristic path length (lambda), average clustering coefficient (CC), and the global efficiency (GE). This is the first time that HD-EEG signals of AD, MCI, and CNT have been compared and that PDI has been used to discriminate between the three groups. Considering the large amount of data originating from HD-EEG acquisition, compared to standard EEG, the aim of this paper is also to assess that compression did not alter the results of the complex network analysis. Compressive sensing was adopted to compress and reconstruct the HD-EEG signals with minimal information loss, achieving an average structural similarity index of 0.954 (AD), 0.957 (MCI), and 0.959 (CNT). When applied to the reconstructed HD-EEG, complex network analysis provided a substantially unaltered performance, compared to the analysis of the original signals: lambda, CC, and GE of the three groups were indeed still significantly different (p < 0.001).
机译：阿尔茨海默氏病（AD）是一种神经退行性疾病，会导致神经元之间失去连接。本文的目的是使用高密度脑电图（HD-EEG）记录构建复杂的脑电活动网络模型，并比较AD，轻度认知障碍（MCI）和健康控制中的网络组织（CNT）主题。在闭眼休息状态下记录了16 AD，16 MCI和12 CNT的HD-EEG。置换失调指数（PDI）用于描述脑电信号之间的差异，并构建网络模型的连接矩阵。发现这三组的特征路径长度（λ），平均聚类系数（CC）和全局效率（GE）显着不同（p <0.001）。这是第一次比较AD，MCI和CNT的HD-EEG信号，并且使用PDI来区分这三个组。与标准EEG相比，考虑到源自HD-EEG采集的大量数据，本文的目的还在于评估压缩是否不会改变复杂网络分析的结果。采用压缩感测以最小的信息损失来压缩和重建HD-EEG信号，从而实现0.954（AD），0.957（MCI）和0.959（CNT）的平均结构相似性指数。与原始信号的分析相比，当将复杂的网络分析应用于重构的HD-EEG时，其性能基本保持不变：三组的lambda，CC和GE确实仍然存在显着差异（p <0.001）。

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