MDD diagnosis based on EEG feature fusion and improved feature selection

Abstract

Conventional scale-based major depression disorder (MDD) diagnosis methods are subjective, so it is significant to propose an objective and accurate MDD diagnosis method to assist physicians in diagnosing MDD. This paper proposes an MDD diagnostic method based on electroencephalogram (EEG) feature fusion and improved feature selection. First, seven functional connectivity matrices are extracted and reassembled into a vector to obtain the fusion functional connectivity feature. Then, a feature selection method based on principal component analysis, K-means, and mutual information (PKM) is constructed to optimize the high-dimensional EEG features. Finally, seven classifiers are used for MDD diagnosis. The results show that the proposed method performs better than the existing methods in MDD diagnosis with accuracy, sensitivity, and specificity of 88.73%, 90.67%, and 86%, respectively. Phase lag index (PLI) and phase-locked value (PLV) features, alpha and delta bands contribute significantly to MDD diagnosis. Functional connectivity in the right hemisphere of the brain, particularly in the right temporal and central prefrontal regions with other brain regions, may be beneficial for MDD diagnosis. High-precision MDD diagnosis can be achieved using EEG from only four channel pairs. In summary, this study provides an objective and accurate method for MDD diagnosis.