A Pervasive Approach to EEG-Based Depression Detection

doi:10.1155/2018/5238028

	A Pervasive Approach to EEG-Based Depression Detection
	Cai, Hanshu 1; Han, Jiashuo 1; Chen, Yunfei 1; Sha, Xiaocong 1; Wang, Ziyang 1; Hu, Bin 1,2,3; Yang, Jing 4; Feng, Lei 5; Ding, Zhijie 6; Chen, Yiqiang 7; Gutknecht, Jurg 8
	2018
发表期刊	COMPLEXITY
ISSN	1076-2787
页码	13
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.
DOI	10.1155/2018/5238028
收录类别	SCI
语种	英语
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
WOS类目	Mathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS记录号	WOS:000425404200001
出版者	WILEY-HINDAWI
引用统计	被引频次：149[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/6119
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, Sch Informat Sci & Engn, Gansu Prov Key Lab Wearable Comp, Lanzhou, Gansu, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Biol Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai, Peoples R China 3.Capital Med Univ, Beijing Inst Brain Disorders, Beijing, Peoples R China 4.Lanzhou Univ, Hosp 2, Dept Child Psychol, Lanzhou, Gansu, Peoples R China 5.Capital Med Univ, Beijing Anding Hosp, Beijing, Peoples R China 6.Third Peoples Hosp Tianshui City, Tianshui, Peoples R China 7.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 8.ETH, Comp Syst Inst, Zurich, Switzerland
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.