CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
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 PublicationBIOMED RESEARCH INTERNATIONAL
ISSN2314-6133
2022-10-01
Volume2022Pages:16
Corresponding AuthorMa, Xibo(xibo.ma@ia.ac.cn)
AbstractObjective. 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.
DOI10.1155/2022/8094351
WOS KeywordPHOTOPLETHYSMOGRAPHY ; WAVE ; PREDICTION ; ACCURACY
Indexed BySCI
Language英语
Funding ProjectNational 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 OrganizationNational 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 AreaBiotechnology & Applied Microbiology ; Research & Experimental Medicine
WOS SubjectBiotechnology & Applied Microbiology ; Medicine, Research & Experimental
WOS IDWOS:000868606200002
PublisherHINDAWI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://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 AuthorMa, Xibo
Affiliation1.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 AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute 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.
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