Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features

Lin，Wan Hua1,2; Chen，Fei2; Geng，Yanjuan1; Ji，Ning1,3; Fang，Peng1; Li，Guanglin1

2021

10.1016/j.bspc.2020.102198

Biomedical Signal Processing and Control   影响因子和分区

1746-8094

1746-8108

Objective: Noninvasive estimation of cuffless and continuous blood pressure (CC-BP) is important for prevention and diagnosis of cardiovascular diseases. Many efforts have been made to estimate CC-BP, but current algorithms still dissatisfy the practical applications due to their limited accuracy and usability. While most previous studies used the features of hybrid pulse arrival time (PAT) and photoplethysmogram (PPG) for CC-BP estimation, this study investigated whether only using PPG features can estimate CC-BP efficiently and accurately. Methods: The PPG signals from 109 patients of the intensive care units were used to extract 65 features for CC-BP estimation. For comparison purpose, two previously reported hybrid feature sets with PAT and PPG indicators were also extracted from the PPG and electrocardiogram. A commonly used multiple linear regression algorithm was adopted for CC-BP estimation. To increase the usability of CC-BP estimation, a feature selection method was developed to choose the most critical and representative subset from the 65 PPG features based on their importance and stability in CC-BP estimation. Results: Our results demonstrated that the accuracy of the CC-BP estimation from 65 PPG features was significantly high in comparison to that of the hybrid PAT and PPG indicators (P < 0.05). When using the subset of the selected critical 13 PPG features, a comparable estimation accuracy could also be achieved as the hybrid PAT and PPG feature sets. Conclusion: The CC-BP estimation algorithm only based on PPG features would provide a convenient way to estimate CC-BP with a comparable accuracy, but personalized calibration should be optimized.

Cuffless and continuous blood pressure (CC-BP) estimation Feature selection Photoplethysmogram (PPG) Unobtrusive

SCI ; EI

英语

其他

National Key R&D Program of China[2019YFC1710400][2019YFC1710402] ; National Natural Science Foundation of China[61901461][81927804] ; China Postdoctoral Science Foundation[2020M672701] ; Shenzhen Governmental Basic Research Grants[JCYJ20170818163724754][SGLH20180625142402055]

Engineering

Engineering, Biomedical

WOS:000591530700010

ELSEVIER SCI LTD

20203909238843

Blood ; Intensive care units ; Feature Selection ; Linear regression

Biological Materials and Tissue Engineering:461.2 ; Biology:461.9 ; Hospitals, Equipment and Supplies:462.2 ; Mathematical Statistics:922.2

2-s2.0-85091341629

Scopus

1.CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Research Center for Neural Engineering,Shenzhen Institutes of Advanced Technology (SIAT),Chinese Academy of Sciences (CAS),and the SIAT Branch,Shenzhen Institute of Artificial Intelligence and Robotics for Society,Shenzhen,518055,China
2.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.College of Medicine and Biological Information Engineering,Northeastern University,Shenyang,110819,China

Lin，Wan Hua,Chen，Fei,Geng，Yanjuan,et al. Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features[J]. Biomedical Signal Processing and Control,2021,63.

Lin，Wan Hua,Chen，Fei,Geng，Yanjuan,Ji，Ning,Fang，Peng,&Li，Guanglin.(2021).Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features.Biomedical Signal Processing and Control,63.

Lin，Wan Hua,et al."Towards accurate estimation of cuffless and continuous blood pressure using multi-order derivative and multivariate photoplethysmogram features".Biomedical Signal Processing and Control 63(2021).