Fractality analysis of frontal brain in major depressive disorder

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 ANOVA 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.

► We investigate the frontal brain of major depressive disorder (MDD) patients.
► We use EEGs and the wavelet-chaos methodology and fractal dimensions (FD).
► Discovered HFDs showing meaningful differences between MDD and healthy groups.
► Discovered HFDs are used in a neural network to discriminate the two EEG groups.
► A high accuracy of 91.3% is achieved for classification of MDD and non-MDD EEGs.