Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

doi:10.3390/bioengineering10040400

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet
	Qin, Caijie 1,3; Li, Yong 1; Liu, Chibiao 1; Ma, Xibo 2,3
刊名	BIOENGINEERING-BASEL
	2023-04-01
卷号	10 期号:4 页码:14
关键词	blood pressure continuous prediction ResNet34 multi-scale feature extraction channel attention
DOI	10.3390/bioengineering10040400
通讯作者	Liu, Chibiao(lcbsmc@163.com) ; Ma, Xibo(xibo.ma@ia.ac.cn)
英文摘要	Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).
WOS研究方向	Biotechnology & Applied Microbiology ; Engineering
语种	英语
出版者	MDPI
WOS记录号	WOS:000977463000001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53245]
专题	多模态人工智能系统全国重点实验室
通讯作者	Liu, Chibiao; Ma, Xibo
作者单位	1.Sanming Univ, Inst Informat Engn, Sanming 365004, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, CBSR&NLPR, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Qin, Caijie,Li, Yong,Liu, Chibiao,et al. Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet[J]. BIOENGINEERING-BASEL,2023,10(4):14.
APA	Qin, Caijie,Li, Yong,Liu, Chibiao,&Ma, Xibo.(2023).Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet.BIOENGINEERING-BASEL,10(4),14.
MLA	Qin, Caijie,et al."Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet".BIOENGINEERING-BASEL 10.4(2023):14.