A Preliminary Study of a Machine Learning Model for Estimating Depression Severity Using EEG

Abstract

Recent years, many EEG-based machine learning methods have been studied to provide objective and accurate assistance in the diagnosis of depression. Many studies have proposed machine learning methods for binary classification of depression and healthy controls. However, there are issues that are not considered in binary classification. For example, the risk of suicide differs between mild and severe depression. In this study, we construct a classification model of depression severity by machine learning using an EEG dataset and discuss the results. An open dataset consisting of EEG data from 116 individuals was used as the analysis data. Depression severity in the dataset was classified into four categories: normal, mild, moderate, and severe. An index extracted from the alpha wave of electrode Fp2 was selected as a feature with reference to related studies. A four-class classification of depression severity was performed using a random forest. The classification accuracy was 62.01%.