Table 1.
Summary of MDD patient’s clinical characteristics.
Table 2.
Diagnosis information of MDD patients with comorbidities [51].
Fig 1.
The EEG cap from Brain Master Discovery, employed sensors placed according to the internationally recognized 10–20 electrode placement standard.
Fig 2.
Shapes used during the 3-stimulus visual Oddball Task.
Fig 3.
Overview of the ML scheme for EEG data analysis.
Fig 4.
Multi-resolution decomposition of EEG signal (delta and theta bands) into detail and approximate coefficients.
Table 3.
Wavelet Coefficients in the delta and theta frequency bands.
Table 4.
The Matlab code to compute the wavelet coefficients for delta and theta bands.
Table 5.
Pseudo code for feature ranking method.
Table 6.
Sample data.
Table 7.
Intermediate variables values.
Table 8.
Computation of Y = (tp(2:n)+tp(1:n-1))/2.
Table 9.
Computation of X = (fp(2:n)-fp(1:n-1)).
Table 10.
Computation of AUC = sum(Y.*X)-0.5.
Table 11.
A list of discriminating features (Frontal = 9, Temporal = 3, Parietal = 1 and Central = 2).
Fig 5.
Scatter plot representation of first two PCs representing clustering behavior of the treatment responders (R) and non-responders (NR) achieved by kernelized principal component analysis (KPCA).
Fig 6.
Wavelet coefficient based statistical differences between responders and nonresponders (Females Patients Only).
According to the topo plots the left temporal areas showed significant differences during EC and EO conditions. The statistical difference of activation, between R and NR, was found in the frontal and central regions also.
Fig 7.
Wavelet coefficient based statistical differences between responders and nonresponders (Both male and female Patients Only).
During EC, left and right temporal areas as well as the left frontal have shown significant differences. In addition, during EO condition the frontal, right temporal and central and parietal areas were also exhibited significant differences.
Fig 8.
Wavelet coefficient based statistical differences between responders and non-responders (All MDD patients).
The figure shows significance of gender stratification for topographical analysis.
Table 12.
Comparison of classification (R Vs NR) methods among the proposed ML method and the methods presented in the related literature.
Fig 9.
Classification accuracies (Logistic Regression (LR)) as a function of number of features.
Over-fitting can be observed by a decrease in accuracy (more than 15 features) with an increase in the number of features.
Table 13.
Classification (R vs. NR) for EEG Data including Delta and Theta Wavelet coefficients.
Fig 10.
Classification accuracies (Logistic Regression (LR)) as a function of number of features.
Over-fitting can be observed by a decrease in accuracy (more than 15 features) with an increase in the number of features.
Table 14.
Classification (MDD patients vs. healthy controls) for EEG features including Delta and Theta Wavelet coefficients.