Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
J Neural Transm (Vienna). 2010 Sep;117(9):1099-109. doi: 10.1007/s00702-010-0450-3. Epub 2010 Aug 17.
A new chaos-wavelet approach is presented for electroencephalogram (EEG)-based diagnosis of Alzheimer's disease (AD) employing a recently developed concept in graph theory, visibility graph (VG). The approach is based on the research ideology that nonlinear features may not reveal differences between AD and control group in the band-limited EEG, but may represent noticeable differences in certain sub-bands. Hence, complexity of EEGs is computed using the VGs of EEGs and EEG sub-bands produced by wavelet decomposition. Two methods are employed for computation of complexity of the VGs: one based on the power of scale-freeness of a graph structure and the other based on the maximum eigenvalue of the adjacency matrix of a graph. Analysis of variation is used for feature selection. Two classifiers are applied to the selected features to distinguish AD and control EEGs: a Radial Basis Function Neural Network (RBFNN) and a two-stage classifier consisting of Principal Component Analysis (PCA) and the RBFNN. After comprehensive statistical studies, effective classification features and mathematical markers were discovered. Finally, using the discovered features and a two-stage classifier (PCA-RBFNN), a high diagnostic accuracy of 97.7% was obtained.
一种新的混沌-小波方法被提出，用于基于脑电图（EEG）的阿尔茨海默病（AD）诊断，该方法采用了图论中最近发展的一个概念，即可视性图（VG）。该方法基于这样的研究思想，即非线性特征在带限 EEG 中可能无法揭示 AD 和对照组之间的差异，但可能在某些子带中表现出明显的差异。因此，使用 EEG 和小波分解产生的 EEG 子带的 VG 来计算 EEG 的复杂性。使用两种方法计算 VG 的复杂性：一种基于图结构的无标度幂律的幂，另一种基于图的邻接矩阵的最大特征值。方差分析用于特征选择。应用两种分类器对所选特征进行区分 AD 和对照组的 EEG：径向基函数神经网络（RBFNN）和由主成分分析（PCA）和 RBFNN 组成的两级分类器。经过全面的统计研究，发现了有效的分类特征和数学标记。最后，使用发现的特征和两级分类器（PCA-RBFNN），获得了 97.7%的高诊断准确性。
