The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy
The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy
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DOI：
10.1016/j.artmed.2007.02.006
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发表时间：
2007-06-01
影响因子：
7.5
通讯作者：
Grossi, Enzo
中科院分区：
文献类型：
--
作者：
Buscema, Massimo;Rossini, Paolo;Grossi, Enzo
目的：本文提出了一种新的方法--隐函数压扁时间法(IFAST)，创新性地利用特殊类型的人工神经网络(ANN)将脑电(EEG)数据的时间序列压缩成空间不变量。本研究的目的是评估这种并行和非线性脑电分析技术在区分轻度认知障碍(MCI)和阿尔茨海默病(AD)患者方面的潜力，并与标准和高级非线性技术进行比较。本研究的主要目的是验证假设，即当脑电电压的空间成分是ANN提取的属性时，MCI和AD的自动分类可以合理地正确。方法和材料：记录180例AD患者和115例MCI患者的静息闭眼脑电数据。利用人工神经网络的IFAST Step-Wise过程提取脑电压值的空间成分。神经网络用于分类的输入数据不是脑电数据，而是训练用于再现记录的脑电轨迹的非线性自联想神经网络的连接权。这些权值代表了头皮表面脑电模式特有的空间特征的良好模型。基于这些参数的分类是二进制的(MCI与AD)，并由有监督的ANN执行。其中一半用于人工神经网络训练，另一半用于自动分类阶段(测试)。结果：AD和MCI的最佳区分结果达到92.33%。与文献中最好的基于盲源分离和小波预处理的方法的比较结果为80.43%(P<；0.001)。结论：实验结果证实了利用神经网络提取静态脑电信号的空间信息含量可以正确地自动分类脑梗塞和阿尔茨海默病的工作假设，为整合脑电信号的时空信息含量的研究奠定了基础。(C)2007 Elsevier B.V.保留所有权利。
Objective: This paper presents the results obtained with the innovative use of special types of artificial neural networks (ANNs) assembled in a novel methodology named IFAST (implicit function as squashing time) capable of compressing the temporal sequence of etectroencephalographic (EEG) data into spatial invariants. The aim of this study is to assess the potential of this parallel and nonlinear EEG analysis technique in distinguishing between subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients with a high degree of accuracy in comparison with standard and advanced nonlinear techniques. The principal aim of the study was testing the hypothesis that automatic classification of MCI and AD subjects can be reasonably correct when the spatial content of the EEG voltage is property extracted by ANNs.Methods and material: Resting eyes-closed EEG data were recorded in 180 AD patients and in 115 MCI subjects. The spatial content of the EEG voltage was extracted by IFAST step-Wise procedure using ANNs. The data input for the classification operated by ANNs were not the EEG data, but the connections weights of a nonlinear auto-associative ANN trained to reproduce the recorded EEG tracks. These weights represented a good model of the peculiar spatial features of the EEG patterns at scalp surface. The classification based on these parameters was binary (MCI versus AD) and was performed by a supervised ANN. Half of the EEG database was used for the ANN training and the remaining half was utilised for the automatic classification phase (testing).Results: The best results distinguishing between AD and MCI reached to 92.33%. The comparative results obtained with the best method so far described in the literature, based on blind source separation and Wavelet pre-processing, were 80.43% (P < 0.001).Conclusion: The results confirmed the working hypothesis that a correct automatic classification of MCI and AD subjects can be obtained extracting spatial information content of the resting EEG voltage by ANNs and represent the basis for research aimed at integrating spatial and temporal information content of the EEG. (c) 2007 Elsevier B.V. All rights reserved.