Real-Time Psychological Stress Detection According to ECG Using Deep Learning
文献类型：期刊论文
| 作者 | Zhang, Pengfei1,2; Li, Fenghua3; Zhao, Rongjian1,2; Zhou, Ruishi1,2; Du, Lidong2; Zhao, Zhan2; Chen, Xianxiang2; Fang, Zhen1,2 |
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 11期号:9页码:18 |
| 关键词 | psychological stress deep learning CNN BiLSTM real-time |
| DOI | 10.3390/app11093838 |
| 通讯作者 | Zhao, Rongjian(zhaorij@aircas.ac.cn) ; Fang, Zhen(zfang@mail.ie.ac.cn) |
| 英文摘要 | Today, excessive psychological stress has become a universal threat to humans. That stress can heavily affect work and study when a person repeatedly is exposed to high stress. If that exposure is long enough, it can even cause cardiovascular disease and cancer. Therefore, both monitoring and managing of stress is imperative to reduce the bad outcomes from excessive psychological stress. Conventional monitoring methods firstly extract the characteristics of the RR interval of an electrocardiogram (ECG) from a time domain and a frequency domain, then use machine learning models, like SVM, random forest, and decision tree, to distinguish the level of that stress. The biggest limitation of using these methods is that at least one minute of ECG data and other signals are indispensable to ensure the high accuracy of the results. This will greatly affect the real-time application of the models. To satisfy real-time detection of stress with high accuracy, we proposed a framework based on deep learning technology. The proposed monitoring framework is based on convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM). To evaluate the performance of this network, we conducted the experiments applying conventional methods. The data for the 34 subjects were collected on the server platform created by the group at the Institute of Psychology of the Chinese Academy of Sciences and our group. The accuracy of the proposed framework was up to 0.865 on three levels of stress using a 10 s ECG signal, a 0.228 improvement compared with conventional methods. Therefore, our proposed framework is more suitable for real-time applications |
| WOS关键词 | DISORDERS |
| 资助项目 | National Key Research and Development Project[2018YFC2001101] ; National Key Research and Development Project[2018YFC2001802] ; National Key Research and Development Project[2020YFC2003703] ; National Key Research and Development Project[2020YFC1512304] ; National Natural Science Foundation of China[62071451] ; CAMS Innovation Fund for Medical Sciences[2019-I2M-5-019] |
| WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000649928900001 |
| 出版者 | MDPI |
| 源URL | [http://ir.psych.ac.cn/handle/311026/39357]  |
| 专题 | 心理研究所_健康与遗传心理学研究室 |
| 通讯作者 | Zhao, Rongjian; Fang, Zhen |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100000, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100000, Peoples R China 3.Chinese Acad Sci, Inst Psychol, Beijing 100000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Pengfei,Li, Fenghua,Zhao, Rongjian,et al. Real-Time Psychological Stress Detection According to ECG Using Deep Learning[J]. APPLIED SCIENCES-BASEL,2021,11(9):18. |
| APA | Zhang, Pengfei.,Li, Fenghua.,Zhao, Rongjian.,Zhou, Ruishi.,Du, Lidong.,...&Fang, Zhen.(2021).Real-Time Psychological Stress Detection According to ECG Using Deep Learning.APPLIED SCIENCES-BASEL,11(9),18. |
| MLA | Zhang, Pengfei,et al."Real-Time Psychological Stress Detection According to ECG Using Deep Learning".APPLIED SCIENCES-BASEL 11.9(2021):18. |
入库方式： OAI收割
来源：心理研究所
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