Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

Peng Li,Shengfu Lu,Bin Hu,Qian Zhai,Lei Cao,Gang Wang,Richeng Li,Lei Feng,Mi Li,Sa Liu

doi:10.1155/2020/4174857

Abstract

This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.

Highlights

Depression is a psychiatric disorder characterized by signi cant and persistent loss of pleasure, anhedonia, and decreased interest
Cao et al classi ed the severe depression patients based on functional connections of resting-state fMRI by feature selection and SVM and obtained 84.21% classi cation accuracy [1]
The depressed patients group is regarded as positive class, while the normal control group is regarded as negative class. e results are analyzed from four aspects: accuracy, specificity, sensitivity, and F1 score

Summary

Introduction

Depression is a psychiatric disorder characterized by signi cant and persistent loss of pleasure, anhedonia, and decreased interest. Qin et al used SVM to identify patients with severe depression based on the di usion tensor imaging (DTI) of resting-state fMRI, and the highest classi cation accuracy was 83.05% [2]. Sato et al classi ed the patients with severe depression based on fMRI signals and achieved an accuracy of 78.26%, sensitivity of 72.00%, and speci city of 85.71% [3]. Bhaumik et al used SVM to classify remitted major depressive patients based on the functional connectivity of resting-state fMRI and achieved an accuracy of 76.1%, sensitivity of 81.5%, and speci city of 68.9% [4]. Schnyer et al used DTI and SVM to identify patients with severe depression, and the highest classi cation accuracy, speci city, and sensitivity were

Methods

Results

Conclusion