A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG
A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG
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DOI：
10.1016/j.neuroimage.2009.06.056
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发表时间：
2010-01-01
期刊：
影响因子：
5.7
通讯作者：
Cichocki, A.
中科院分区：
文献类型：
--
作者：
Dauwels, J.;Vialatte, F.;Cichocki, A.
众所周知，阿尔茨海默病（AD）患者的EEG信号通常不如年龄匹配的对照受试者同步。然而，这种影响并不总是容易察觉的。对于处于症状前期的患者尤其如此，通常称为轻度认知障碍（MCI），在此期间，神经元变性在临床症状出现之前发生。在本文中，各种同步措施的背景下，AD诊断，包括相关系数，均方和相位相干性，格兰杰因果关系，相位同步指数，信息理论的分歧措施，状态空间为基础的措施，和最近提出的随机事件同步措施进行了研究。EEG数据的实验表明，这些措施中的许多是强相关（或反相关）的相关系数，因此，提供很少的补充信息，脑电同步。仅与相关系数弱相关的度量包括相位同步指数、格兰杰因果度量和随机事件同步度量。此外，这三种同步性测量方法互不相关，因此，它们似乎都捕捉到了一种特定的相互依赖关系。对于手头的数据集，只有两个同步性测量能够令人信服地区分MCI患者与年龄匹配的对照患者，即，格兰杰因果关系（特别是全频定向传递函数）和随机事件同步。这两个指标被用作区分MCI患者和年龄匹配的对照受试者的特征，得出的留一分类率为83%。通过添加来自EEG的互补特征，可以进一步提高分类性能;这种方法可能最终导致用于MCI和AD的可靠的基于EEG的诊断工具。(C)2009 Elsevier Inc. All rights reserved.
It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as mild cognitive impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD. (C) 2009 Elsevier Inc. All rights reserved.