Depression Identification Using Asymmetry Matrix and Machine Learning Algorithms

Developing an efficient and reliable method for the identification of depression has high importance. The aim of this paper is to propose an approach for depression diagnosis using an interhemispheric asymmetry matrix and machine learning algorithms.

First, EEG signal was acquired from 24 depressed patients and 24 healthy subjects. The EEG signal was acquired from participants for 5 minutes in eyes-closed (EC) and 5 minutes in eyes-open (EO) condition. After preprocessing data, interhemispheric asymmetry for absolute and relative powers of theta and beta frequency bands, theta-to-alpha power ratio, and IAF features were computed. Then, the proposed asymmetry matrix is used as a feature for statistical and classification analysis. In this paper, classification was performed using a support vector machine (SVM), logistic regression, and multi-layer perceptron (MLP). 

The results demonstrated that central and temporal theta absolute power, central and temporal individual alpha frequency (IAF) asymmetries in EC condition and occipital beta absolute power, temporal theta relative power, temporal theta-to-alpha power ratio, and temporal IAF asymmetries in EO condition have significant differences between depressed and healthy groups. Findings show that beta absolute power asymmetry in the occipital region and EO condition is a good biomarker for depression identification with 77.1% accuracy using Gaussian SVM classifier.

The results of this study show performance of proposed asymmetry matrix features in depression detection. Findings show that beta absolute power asymmetry in the occipital region and EO condition is a good biomarker for depression identification.