Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals | |
Qin, Caijie1,2,3; Wang, Xiaohua4; Xu, Guangjun5; Ma, Xibo2,3,6
| |
| Source Publication | BIOMED RESEARCH INTERNATIONAL
 |
| ISSN | 2314-6133 |
| 2022-10-01 | |
| Volume | 2022Pages:16 |
| Corresponding Author | Ma, Xibo(xibo.ma@ia.ac.cn) |
| Abstract | Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies. |
| DOI | 10.1155/2022/8094351 |
| WOS Keyword | PHOTOPLETHYSMOGRAPHY ; WAVE ; PREDICTION ; ACCURACY |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research Programs of China[2016YFA0100900] ; National Key Research Programs of China[2016YFA0100902] ; Chinese National Natural Science Foundation[82090051] ; Chinese National Natural Science Foundation[81871442] ; Youth Innovation Promotion Association CAS[Y201930] ; Educational Research Project for Young and Middle-aged Teachers in Fujian Province[JT180513] ; Scientific Research and Development Fund project of Sanming University[B201824] ; Fujian Key Lab of Agriculture IOT Application ; IOT Application Engineering Research Center of Fujian Province Colleges and Universities ; Digital Fujian Research Institute for Industrial Energy Big Data ; Laboratory for the Analysis and Application of Industry Big Data |
| Funding Organization | National Key Research Programs of China ; Chinese National Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Educational Research Project for Young and Middle-aged Teachers in Fujian Province ; Scientific Research and Development Fund project of Sanming University ; Fujian Key Lab of Agriculture IOT Application ; IOT Application Engineering Research Center of Fujian Province Colleges and Universities ; Digital Fujian Research Institute for Industrial Energy Big Data ; Laboratory for the Analysis and Application of Industry Big Data |
| WOS Research Area | Biotechnology & Applied Microbiology ; Research & Experimental Medicine |
| WOS Subject | Biotechnology & Applied Microbiology ; Medicine, Research & Experimental |
| WOS ID | WOS:000868606200002 |
| Publisher | HINDAWI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50303 |
| Collection | 模式识别国家重点实验室_生物识别与安全技术 |
| Corresponding Author | Ma, Xibo |
| Affiliation | 1.Sanming Univ, Inst Informat Engn, Sanming, Peoples R China 2.Chinese Acad Sci, Inst Automation, CBSR, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automation, NLPR, Beijing, Peoples R China 4.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 2, Dept Nephrol, Beijing, Peoples R China 5.Agr Bank China, Data Ctr, Beijing 100049, Peoples R China 6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences; Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences; Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Qin, Caijie,Wang, Xiaohua,Xu, Guangjun,et al. Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals[J]. BIOMED RESEARCH INTERNATIONAL,2022,2022:16. |
| APA | Qin, Caijie,Wang, Xiaohua,Xu, Guangjun,&Ma, Xibo.(2022).Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals.BIOMED RESEARCH INTERNATIONAL,2022,16. |
| MLA | Qin, Caijie,et al."Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals".BIOMED RESEARCH INTERNATIONAL 2022(2022):16. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment