Robust Detection of Major Depressive Disorder Using Empirical Mode Decomposition-Based EEG Signal Analysis

Depression is universally known as a mood disorder, which is a serious and common disease that causes moral weakness and suicidal mortality. Most psychophysiologists, sought to use physiological data to infer this psychiatric illness. This work focuses on developing a major depression disorder detection system using EEG signals. In this study, a publicly available dataset is used. 19 channels of EEG signals data are collected from 16 depressive subjects and 16 healthy subjects with closed eyes and subsequently subjected to preprocessing procedures. Using correlation coefficients and standard deviation parameters, just 14 channels are selected. The first minute recording of EEG channels is processed using empirical mode decomposition (EMD) to extract some features, which are then used for the detection of major depressive disorder (MDD). These features are extracted, first by the decomposition of EEG channels into IMFs; then by the extraction of instantaneous amplitude and instantaneous frequency of each IMF. These features show significant differences between MDD subjects and healthy subjects. Using the linear kernel of the support vector machine (SVM), the classification results achieve higher accuracy, after dimensionality reduction with the principal component analysis method. All performance measures including accuracy, precision, sensitivity, specificity, and negative predictive value reach 100%. Thus, the findings confirm the robustness of the proposed system in detecting MDD.