Fractality analysis of frontal brain in major depressive disorder
Fractality analysis of frontal brain in major depressive disorder
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DOI：
10.1016/j.ijpsycho.2012.05.001
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发表时间：
2012-08-01
影响因子：
3
通讯作者：
Adeli, Amir
中科院分区：
文献类型：
--
作者：
Ahmadlou, Mehran;Adeli, Hojjat;Adeli, Amir
近年来，人们用线性方法研究了诊断为重度抑郁障碍(MDD)患者的额部脑电，但没有基于非线性方法。本文用小波-混沌方法和Katz和Higuchi的分维(KFD和HFD)作为非线性和复杂性的度量，对MDD患者的额脑进行了研究。利用小波滤波器组将健康成人和MDD患者的额叶脑电分解为5个子带，并计算了带限及其5个子带的频域分布。然后，利用Va和t检验，比较MDD组和健康组EEG全频段和子频段左右额叶的HFDs和KFDs，以找出两组之间最有意义的FD值。最后，将发现的FDs用作分类器的输入，即增强型概率神经网络(EPNN)，以区分MDD和健康脑电。HFD的结果显示，在β和伽马子频段中，MDD组的左、右和整个额叶的复杂性高于非MDD组。此外，在区分MDD和非MDD参与者方面，β频段的HFD比伽马频段的HFD更具区分性，而KFD没有显示出任何有意义的差异。对于MDD和非MDD脑电，基于左侧、右侧和整个额叶Beta子波的HFDs分类准确率高达91.3%。然而，这项研究的发现应该被认为是试探性的，因为作者可以获得的数据有限。(C)2012爱思唯尔B.V.保留所有权利。
EEGs of the frontal brain of patients diagnosed with major depressive disorder (MDD) have been investigated in recent years using linear methods but not based on nonlinear methods. This paper presents an investigation of the frontal brain of MDD patients using the wavelet-chaos methodology and Katz's and Higuchi's fractal dimensions (KFD and HFD) as measures of nonlinearity and complexity. EEGs of the frontal brain of healthy adults and MDD patients are decomposed into 5 EEG sub-bands employing a wavelet filter bank, and the FDs of the band-limited as well as those of their 5 sub-bands are computed. Then, using the AND VA statistical test, HFDs and KFDs of the left and right frontal lobes in EEG full-band and sub-bands of MDD and healthy groups are compared in order to discover the FDs showing the most meaningful differences between the two groups. Finally, the discovered FDs are used as input to a classifier, enhanced probabilistic neural network (EPNN), to discriminate the MDD from healthy EEGs. The results of HFD show higher complexity of left, right and overall frontal lobes of the brain of MDD compared with non-MDD in beta and gamma sub-bands. Moreover, it is observed that HFD of the beta band is more discriminative than HFD of the gamma band for discriminating MDD and non-MDD participants, while the KFD did not show any meaningful difference. A high accuracy of 91.3% is achieved for classification of MDD and non-MDD EEGs based on HFDs of left, right, and overall frontal brain beta sub-band. The findings of this research, however, should be considered tentative because of limited data available to the authors. (C) 2012 Elsevier B.V. All rights reserved.