Classification of Depression Based on Local Binary Pattern and Singular Spectrum Analysis

Abstract

Depression is a common mental disease characterized by significant sadness and feeling blue all the time. At present, most classifications and predictions of depression rely on different characteristics. Comparing with the previous work, we use local binary pattern (LBP) and signal singular spectrum analysis (SSA) technology to extract features from the original signal. Firstly, the LBP signal is obtained by encoding the segmented signal. Then, we use SSA to decompose and reconstruct the LBP signal to remove noise and divide the frequency band. Finally, we feed the data of each frequency band to K-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM) and extreme learning machine (ELM) for classification. The experimental results show that LBP and SSA features achieve the best classification effect on SVM, and the accuracy of beta band is the highest with 99.24% accuracy, 99.34% sensitivity and 99.12% specificity respectively.