Recognition of depression patients with electroencephalogram

Abstract

The recognition of patients with depression is a very important problem, and there are few relevant studies at present. As the application of electroencephalogram (EEG) signals becomes mature in clinical diagnosis, the relationship between EEG and depression has been widely concerned. In this study, firstly, EEG signals were analysed, then EEG signals were collected for processing and feature extraction, and depression patients were recognised by the support vector machine (SVM) method. The experimental results demonstrated that SVM showed different accuracy in different features, leads, and wavebands. When all leads were used, the accuracy of SVM was the highest. When power spectral density (PSD) was used as the feature, the accuracy of SVM was higher than 70%, and its accuracy on the β wave was the highest; when activity was used as the feature, the accuracy of SVM was higher than 75%, and its accuracy was the highest on the α wave. The comparison of random forest (RF) and k-nearest neighbour (KNN) demonstrated that SVM showed the highest accuracy. The results show that the EEG signal-based method has a good performance in recognising depression patients and can be popularised and applied in practice.