An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

Abstract

Depression is a mental disorder characterized by persistent low mood that affects a person’s thoughts, behavior, feelings, and sense of well-being. According to the World Health Organization (WHO), depression will become the second major life-threatening illness in 2020. Electroencephalogram (EEG) signals, which reflect the working status of human brain, are regarded as the best physiological tool for depression detection. Previous studies used the Empirical Mode Decomposition (EMD) method, which can deal with the highly complex, nonlinear and non-stationary nature of EEG, to extract features from EEG signals. However, for some special data, the neighboring components extracted through EMD could certainly have sections of data carrying the same frequency at different time durations. Thus, the Intrinsic Mode Functions (IMFs) of the data could be linearly dependent and the features coefficients of expansion based on IMFs could not be extracted, which can make the pre-proposed EMD-based feature extraction method impractical. In order to solve this problem, an improved EMD applying Singular Value Decomposition (SVD)-based feature extraction method was proposed in this study, which can extract the features coefficients of expansion based on all IMFs as accurately as possible, ignoring potentially linear dependence of IMFs. Experiments were conducted on four EEG databases for detecting depression. The improved EMD-based feature extraction method can extract feature from all three channels (Fp1, Fpz, and Fp2) on the four EEG databases. The average classification results of the proposed method on the four EEG databases including depressed patients and healthy subjects reached 83.27, 85.19, 81.98 and 88.07 percent, respectively, which were comparable with the pre-proposed EMD-based feature extraction method.