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首页> 外文期刊>IEEE sensors journal >Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach
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Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach

机译：多元多尺度方法对阿尔茨海默氏病脑电图复杂性的熵测量

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摘要

Alzheimer's disease (AD) impact is rapidly growing in western countries. The unavoidable progression of the disease, call for reliable ways to diagnose the AD in its early stages. Recently, it has been shown that the electroencephalography (EEG) complexity analysis could be used to predict the conversion from mild cognitive impairment (MCI) to AD. Despite the EEG analysis does not achieve yet the required clinical performance in terms of both sensitivity and specificity to be accepted as a clinically reliable technique of screening, the researchers count on the easiness and the non-invasiveness of the EEG measuring system. The aim of this paper is to analyze the efficacy of entropic complexity measures as a possible bio-marker to distinguish among the brain states related to the AD patients and MCI subjects from normal healthy elderly. The research is carried out on an experimental database. Three different emerging measures of complexity are compared, namely, permutation entropy, sample entropy, and Lempel–Ziv complexity. Because time series derived from biological systems show structures on multiple spatial-temporal scales and there exists a significant inter-channel correlation among the EEG channels, a multiscale multivariate approach is also implemented. Limited to the analyzed data, the results show that the severity of the AD reflects in the EEG dynamic complexity leaving the hope of early diagnosis based on simple EEG.
机译：在西方国家，阿尔茨海默氏病（AD）的影响正在迅速增长。这种疾病不可避免的发展，要求在早期阶段诊断AD的可靠方法。最近，研究表明脑电图（EEG）复杂度分析可用于预测轻度认知障碍（MCI）向AD的转化。尽管脑电图分析在灵敏度和特异性方面均未达到所需的临床表现，但仍被认为是临床上可靠的筛查技术，但研究人员仍依靠脑电图测量系统的简便性和无创性。本文的目的是分析熵复杂性措施作为一种可能的生物标记物的功效，以区分与正常健康老年人和AD患者和MCI受试者有关的大脑状态。该研究是在实验数据库上进行的。比较了三种不同的新兴复杂性度量，即置换熵，样本熵和Lempel-Ziv复杂性。由于从生物系统获得的时间序列在多个时空尺度上显示出结构，并且在EEG通道之间存在显着的通道间相关性，因此也实现了多尺度多变量方法。限于所分析的数据，结果表明AD的严重程度反映在EEG的动态复杂性上，给基于简单EEG的早期诊断带来希望。

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