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A Pervasive Approach to EEG-Based Depression Detection

文献类型：期刊论文

作者Cai, Hanshu6; Han, Jiashuo6; Chen, Yunfei6; Sha, Xiaocong6; Wang, Ziyang6; Hu, Bin6,7,8; Yang, Jing1; Feng, Lei2; Ding, Zhijie3; Chen, Yiqiang4
刊名COMPLEXITY
出版日期2018
页码13
ISSN号1076-2787
DOI10.1155/2018/5238028
英文摘要Nowadays, depression is the world's major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls) subjects, was constructed. The electroencephalogram (EEG) signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. The classifiers' performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN) had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.
资助项目National Basic Research Program of China (973 Program)[2014CB744600] ; National Natural Science Foundation of China[61210010] ; National Natural Science Foundation of China[61632014] ; MOST[2013DFA11140]
WOS研究方向Mathematics ; Science & Technology - Other Topics
语种英语
WOS记录号WOS:000425404200001
出版者WILEY-HINDAWI
源URL[http://119.78.100.204/handle/2XEOYT63/6118]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Bin
作者单位1.Lanzhou Univ, Hosp 2, Dept Child Psychol, Lanzhou, Gansu, Peoples R China
2.Capital Med Univ, Beijing Anding Hosp, Beijing, Peoples R China
3.Third Peoples Hosp Tianshui City, Tianshui, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
5.ETH, Comp Syst Inst, Zurich, Switzerland
6.Lanzhou Univ, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Gansu, Peoples R China
7.Chinese Acad Sci, Shanghai Inst Biol Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China
8.Capital Med Univ, Beijing Inst Brain Disorders, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Hanshu,Han, Jiashuo,Chen, Yunfei,et al. A Pervasive Approach to EEG-Based Depression Detection[J]. COMPLEXITY,2018:13.
APA Cai, Hanshu.,Han, Jiashuo.,Chen, Yunfei.,Sha, Xiaocong.,Wang, Ziyang.,...&Gutknecht, Jurg.(2018).A Pervasive Approach to EEG-Based Depression Detection.COMPLEXITY,13.
MLA Cai, Hanshu,et al."A Pervasive Approach to EEG-Based Depression Detection".COMPLEXITY (2018):13.

入库方式： OAI收割

来源：计算技术研究所

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