Study on Depression Classification Based on Electroencephalography Data Collected by Wearable Devices

Abstract

Depression has become a disease, which may threaten millions of families’ well-being. The current method of screening depression is subjective, labor-consuming and costly. Study on Electroencephalogram (EEG) has become a new direction to explore an objective, low-cost and accurate method to detect depression. In this paper, three-electrode EEG data of 158 subjects (90 depressed and 68 normal control) in resting state, and under audio stimulation (positive and negative) were collected and processed. After feature selection using Sequential Floating Forward Selection (SFFS), four popular classification methods were applied and classification accuracies were verified using 10-fold cross validation. Results have shown the accuracy of classification will be improved when male and female are classified separately. The highest accuracy of male and female classification are 91.98%, 79.76%, respectively, compare to 77.43% when the classification is processed as gender-free. The effective depressive features of male and female are also different, which may be caused by the differences of brain structure. This research suggests a possible pervasive method of depression classification for future clinical application.