Depression Identification Using Asymmetry Matrix and Machine Learning Algorithms

Abstract

Purpose: Developing an efficient and reliable method for the identification of depression is highly important. This paper aims to propose an approach for depression diagnosis using an interhemispheric asymmetry matrix and machine learning algorithms. Materials and Methods: First, an 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 Individual Alpha Frequency (IAF) features were computed. Then, the proposed asymmetry matrix is used as a feature for statistical and classification analysis. In this paper, the classification was performed using a Support Vector Machine (SVM), logistic regression, and Multi-Layer Perceptron (MLP). Results: The results demonstrated that central and temporal theta absolute power, central and temporal IAF asymmetries in the EC condition and occipital beta absolute power, temporal theta relative power, temporal theta-to-alpha power ratio, and temporal IAF asymmetries in the 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 the Gaussian SVM classifier. Conclusion: 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.