兰州大学机构库
Ubiquitous Depression Detection of Sleep Physiological Data by Using Combination Learning and Functional Networks
Zhang, Bingtao1,2,3; Zhou, Wenying2,3; Cai, HS(蔡涵书)3; Su, Yun3,4; Wang, Jinfeng3,5; Zhang, Zhonglin1; Lei, Tao6
2020
Source PublicationIEEE Access   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2169-3536
Volume8Pages:94220-94235
page numbers16
AbstractNowadays, depression has become a common mental disorder with high morbidity and mortality. Due to the limitations of traditional interview-based depression detection, it has become an urgent problem to realize objective, convenient and fast detection. This study is to explore ubiquitous methods of depression detection based on combination learning and functional networks, using sleep physiological data. Sleep physiological data were collected using a portable physiological data instrument, and then preprocess and extract several related features. We applied combination learning to discover the best sleep stage, the optimal features subset, and the most effective classifier, which are hidden behind physiological features, to detect depression. Physiological features in the optimal feature subset based on Euclidean distance are mapped to nodes to construct the functional network. The optimal feature subset was combined with the functional network attributes as the input of the most effective classifier to get the ultimate performance of depression detection. Controlled trials based on ubiquitous sleep physiological data were conducted on different genders. Experiments show that the best results for male and female were derived from slow wave sleep (SWS) and rapid eye movement (REM), with performances of 92.21%; and 94.56%, AUC of 0.944 and 0.971, respectively. Thus, our study may provide an effective and ubiquitous method for detect depression.
KeywordPhysiology Sleep Electrodes Instruments Feature extraction Diseases Data acquisition Ubiquitous depression detection functional networks combination learning sleep physiological data
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI10.1109/ACCESS.2020.2994985
Indexed BySCIE ; SSCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61962034][61941109][61862058] ; Yong Scholar Fund of Lanzhou Jiaotong University[2016004]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000541125000010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/406236
Collection兰州大学
信息科学与工程学院
Corresponding AuthorZhang, Bingtao
Affiliation
1.Lanzhou Jiaotong Univ, Sch Elect & Informat Engn, Lanzhou 730070, Peoples R China
2.Lanzhou Jiaotong Univ, Key Lab Optotechnol & Intelligent Control, Minist Educ, Lanzhou 730070, Peoples R China
3.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
4.Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China
5.Gansu Prov Peoples Hosp, Sleep Med Ctr Gansu Prov, Lanzhou 730000, Peoples R China
6.Shaanxi Univ Sci & Technol, Sch Elect Informat & Artificial Intelligence, Xian 710021, Peoples R China
First Author AffilicationSchool of Information Science and Engineering
Corresponding Author AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Zhang, Bingtao,Zhou, Wenying,Cai, Hanshu,et al. Ubiquitous Depression Detection of Sleep Physiological Data by Using Combination Learning and Functional Networks[J]. IEEE Access,2020,8:94220-94235.
APA Zhang, Bingtao.,Zhou, Wenying.,Cai, Hanshu.,Su, Yun.,Wang, Jinfeng.,...&Lei, Tao.(2020).Ubiquitous Depression Detection of Sleep Physiological Data by Using Combination Learning and Functional Networks.IEEE Access,8,94220-94235.
MLA Zhang, Bingtao,et al."Ubiquitous Depression Detection of Sleep Physiological Data by Using Combination Learning and Functional Networks".IEEE Access 8(2020):94220-94235.
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