Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement
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...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4174857 |
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| author | Mi Li Lei Cao Qian Zhai Peng Li Sa Liu Richeng Li Lei Feng Gang Wang Bin Hu Shengfu Lu |
| author_facet | Mi Li Lei Cao Qian Zhai Peng Li Sa Liu Richeng Li Lei Feng Gang Wang Bin Hu Shengfu Lu |
| author_sort | Mi Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba0a174a57574eafbf173eb432250a75 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ba0a174a57574eafbf173eb432250a752025-02-03T01:01:30ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/41748574174857Method of Depression Classification Based on Behavioral and Physiological Signals of Eye MovementMi Li0Lei Cao1Qian Zhai2Peng Li3Sa Liu4Richeng Li5Lei Feng6Gang Wang7Bin Hu8Shengfu Lu9Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaDepartment of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaThis 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.http://dx.doi.org/10.1155/2020/4174857 |
| spellingShingle | Mi Li Lei Cao Qian Zhai Peng Li Sa Liu Richeng Li Lei Feng Gang Wang Bin Hu Shengfu Lu Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement Complexity |
| title | Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement |
| title_full | Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement |
| title_fullStr | Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement |
| title_full_unstemmed | Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement |
| title_short | Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement |
| title_sort | method of depression classification based on behavioral and physiological signals of eye movement |
| url | http://dx.doi.org/10.1155/2020/4174857 |
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