An Improved Empirical Mode Decomposition of Electroencephalogram Signals for Depression Detection

作者：Jian Shen, Xiaowei Zhang, Gang Wang, Zhijie Ding, Bin Hu · 发表于：IEEE Transactions on Affective Computing · 年份：2019 · DOI：10.1109/taffc.2019.2934412 · 被引用次数：156 · 研究领域：EEG and Brain-Computer Interfaces、Heart Rate Variability and Autonomic Control、ECG Monitoring and Analysis

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 c...