兰州大学机构库 >信息科学与工程学院
Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement
Li, Mi1,2; Cao, Lei1,2; Zhai, Qian3,4; Li, Peng1,2; Liu, Sa1,2; Li, Richeng1,2; Feng, Lei3,4; Wang, Gang3,4; Hu, B(胡斌)1,5; Lu, Shengfu1,2
2020
Source PublicationComplexity   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN10990526
EISSN1099-0526
Volume2020
page numbers9
AbstractThis 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.
© 2020 Mi Li et al.
PublisherWILEY-HINDAWI
DOI10.1155/2020/4174857
Indexed BySCIE ; EI
Language英语
Funding ProjectBeijing University of Technology[] ; Beijing Outstanding Talent Training Foundation[] ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support[] ; National Natural Science Foundation of China[61602017 61420106005]
WOS Research AreaMathematics ; Science & Technology - Other Topics
WOS SubjectMathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS IDWOS:000509900000001
PublisherHindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States
EI Accession Number20200508109259
EI KeywordsClassification (of information) ; Eye movements ; Feature extraction ; Learning systems ; Principal component analysis
EI Classification NumberInformation Theory and Signal Processing:716.1 ; Mathematical Statistics:922.2
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/416787
Collection信息科学与工程学院
Corresponding AuthorLu, Shengfu
Affiliation
1.Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
2.Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing; 100124, China
3.Natl. Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
4.Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
5.Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Recommended Citation
GB/T 7714
Li, Mi,Cao, Lei,Zhai, Qian,et al. Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement[J]. Complexity,2020,2020.
APA Li, Mi.,Cao, Lei.,Zhai, Qian.,Li, Peng.,Liu, Sa.,...&Lu, Shengfu.(2020).Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement.Complexity,2020.
MLA Li, Mi,et al."Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement".Complexity 2020(2020).
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