A Case-Based Reasoning Model for Depression Based on Three-Electrode EEG Data

Abstract

Depression, threatening the well-being of millions, has become one of the major diseases in the past decade. However, the current method of diagnosing depression is questionnaire-based interviews, which is labor-intensive and highly dependent on doctors' experience. Thus, objective and cost-efficient methods are needed. In this paper, we present a case-based reasoning model for identifying depression. Electroencephalography data were collected using a portable three-electrode EEG device, and then processed to remove artifacts and extract features. We applied multiple classifiers. The best performing k-Nearest Neighbor (KNN) was selected as the evaluation function to select the effective features which were then used to create the case base. Based on the weight set of standard deviations, the similarity was calculated using normalized Euclidean distance to get the optimal recognition rate of depression. The accuracy of optimal similarity identification of patients with depression was 91.25 percent, which was improved compared to the accuracy using KNN classifier (81.44 percent) or previously reported classifiers. Thus, we provide a novel pervasive and effective method for automatic detection of depression.