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Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism
Zhang, Pengfei1,2; Li, Fenghua3; Du, Lidong1,4; Zhao, Rongjian1,2; Chen, Xianxiang1,4; Yang, Ting5; Fang, Zhen1,2,4
First AuthorZhang, Pengfei
Correspondent Emailzfang@mail.ie.ac.cn (fang, zhen)
Abstract

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN-BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN-BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN-attention-based BiLSTM.

KeywordECG psychological stress deep learning attention CNN BiLSTM
2021-03-01
Language英语
DOI10.3390/app11062848
Source PublicationAPPLIED SCIENCES-BASEL
Volume11Issue:6Pages:15
Subtype实证研究
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[2018YFC2001101] ; National Natural Science Foundation of China[2018YFC2001802] ; National Natural Science Foundation of China[2020YFC2003703] ; National Natural Science Foundation of China[2020YFC1512304] ; CAMS Innovation Fund for Medical Sciences[62071451] ; [2019-I2M-5-019]
PublisherMDPI
WOS KeywordDISORDERS
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000645715600001
WoS QuartileQ3
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/39298
Collection健康与遗传心理学研究室
Corresponding AuthorFang, Zhen
Affiliation1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100000, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100000, Peoples R China
3.Chinese Acad Sci, Inst Psychol, Beijing 100000, Peoples R China
4.Chinese Acad Med Sci, Personalized Management Chron Resp Dis, Beijing 100000, Peoples R China
5.China Japan Friendship Hosp, Beijing 100000, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Pengfei,Li, Fenghua,Du, Lidong,et al. Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism[J]. APPLIED SCIENCES-BASEL,2021,11(6):15.
APA Zhang, Pengfei.,Li, Fenghua.,Du, Lidong.,Zhao, Rongjian.,Chen, Xianxiang.,...&Fang, Zhen.(2021).Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism.APPLIED SCIENCES-BASEL,11(6),15.
MLA Zhang, Pengfei,et al."Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism".APPLIED SCIENCES-BASEL 11.6(2021):15.
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