A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals
A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals
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DOI：
10.1016/j.jneumeth.2019.04.013
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发表时间：
2019-07-01
影响因子：
3
通讯作者：
Adeli, Hojjat
中科院分区：
文献类型：
--
作者：
Amezquita-Sanchez, Juan P.;Mammone, Nadia;Adeli, Hojjat
背景资料：从轻度认知障碍（MCI）和阿尔茨海默病（AD）患者获得的EEG信号在视觉上是无法区分的。新方法：本文提出了一种新的信号处理技术，即综合多信号分类和经验小波变换相结合的方法来鉴别MCI和AD（MUSIC-EWT），不同的非线性特征，如分形维数（FD）从混沌理论，和分类算法，增强概率神经网络模型的Ahmadlou和Adeli使用的EEG信号。三种不同的FD措施进行了研究：盒维数（BD），Higuchi的FD（HFD），Katz的FD（KFD）沿着与另一种措施的自相似性的信号称为赫斯特指数（HE）。通过对37例MCI和37例AD患者的脑电信号监测，验证了该方法的准确性。与现有方法的比较：将该方法与最近文献中提出的其他方法进行了比较。实验结果表明，结合非线性特征BD和HE的MUSIC-EWT算法，EPNN分类器可用于MCI和AD患者的鉴别诊断，准确率为90.3%。
Background: EEG signals obtained from Mild Cognitive Impairment (MCI) and the Alzheimer's disease (AD) patients are visually indistinguishable.New method: A new methodology is presented for differential diagnosis of MCI and the AD through adroit integration of a new signal processing technique, the integrated multiple signal classification and empirical wavelet transform (MUSIC-EWT), different nonlinear features such as fractality dimension (FD) from the chaos theory, and a classification algorithm, the enhanced probabilistic neural network model of Ahmadlou and Adeli using the EEG signals.Results: Three different FD measures are investigated: Box dimension (BD), Higuchi's FD (HFD), and Katz's FD (KFD) along with another measure of the self-similarities of the signals known as the Hurst exponent (HE). The accuracy of the proposed method was verified using the monitored EEG signals from 37 MCI and 37 AD patients.Comparison with existing methods: The proposed method is compared with other methodologies presented in the literature recently.Conclusions: It was demonstrated that the proposed method, MUSIC-EWT algorithm combined with nonlinear features BD and HE, and the EPNN classifier can be employed for differential diagnosis of MCI and AD patients with an accuracy of 90.3%.