Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement
Mi Li,
Lei Cao,
Qian Zhai,
Peng Li,
Sa Liu,
Richeng Li,
Lei Feng,
Gang Wang,
Bin Hu,
Shengfu Lu
Affiliations
Mi Li
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Lei Cao
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Qian Zhai
The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
Peng Li
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Sa Liu
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Richeng Li
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Lei Feng
The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
Gang Wang
The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
Bin Hu
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Shengfu Lu
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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.
