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题名: EEG-based mild depressive detection using feature selection methods and classifiers.
其他题名: EEG-based mild depressive detection using feature selection methods and classifiers
作者: Li, Xiaowei; Hu, B(胡斌); Sun, Shuting; Cai, Hanshu
收录类别: SCIE ; EI ; PubMed ; MEDLINE
出版日期: 2016-11
刊名: Computer methods and programs in biomedicine
卷号: 136, 页码:151-161
出版者: ELSEVIER
出版地: CLARE
英文摘要: Copyright 2016 Elsevier Ireland Ltd. All rights reserved.BACKGROUND AND OBJECTIVE: Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection.METHODS: An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features.RESULTS: Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively.CONCLUSIONS: Classification results obtained by GSW+KNN are encouraging and better than previously published results. In the spatial distribution of features, we find that left parietotemporal lobe in beta EEG frequency band has greater effect on mild depression detection. And fewer EEG channels (FP1, FP2, F3, O2 and T3) combined with linear features may be good candidates for usage in portable systems for mild depression detection.
作者部门: School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
学科分类: Computer Science; Engineering; Medical Informatics
文章类型: Article
所属项目编号: This work was supported by the National Basic Research Program of China (973 Program) (No.2014CB744600), the National Natural Science Foundation of China (grant No.60973138, grant No.61003240), the International Cooperation Project of Ministry of Science and Technology (No.2013DFA11140), the National Basic Research Program of China (973 Program) (No.2011CB711000).
所属项目名称: 国家自然科学基金项目 ; 国家重点基础研究发展计划以及国家重大科学研究计划(973计划) ; 国家国际科技合作专项
项目资助者: NSFC ; MOE
语种: 英语
DOI: 10.1016/j.cmpb.2016.08.010
ISSN号: 1872-7565
WOS记录号: WOS:000385330400016
EI记录号: 20163702790497
PM记录号: 27686712
IR记录号: 27686712
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内容类型: 期刊论文
URI标识: http://ir.lzu.edu.cn/handle/262010/182824
Appears in Collections:信息科学与工程学院_期刊论文

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Recommended Citation:
Li, Xiaowei,Hu, Bin,Sun, Shuting,et al. EEG-based mild depressive detection using feature selection methods and classifiers.[J]. Computer methods and programs in biomedicine,2016,136:151-161.
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