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An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram

文献类型：期刊论文

作者Li, Yunlong1,2; Xu, Yang1; Ma, Zuchang1; Ye, Yuqi1,2; Gao, Lisheng1; Sun, Yining1
刊名COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
出版日期2022-11-01
卷号226
ISSN号0169-2607
关键词Aortic stiffness Carotid-femoral pulse wave velocity Feature extraction Screening XGBoost Wrist photoplethysmogram
DOI10.1016/j.cmpb.2022.107128
通讯作者Xu, Yang(yxu@hfcas.ac.cn)
英文摘要Background and Objective: Carotid-femoral pulse wave velocity (cf-PWV) is the gold standard for noninvasive assessment of aortic stiffness. Photoplethysmography used in wearable devices provides an indirect measurement method for cf-PWV. This study aimed to construct a cf-PWV prediction method based on the XGBoost algorithm and wrist photoplethysmogram (wPPG) for the early screening of arteriosclerosis in primary healthcare. Methods: Data from 210 subjects were used for modeling, and 100 subjects were used as an external validation set. The wPPG pulse waves were filtered by discrete wavelet transform, and various features were extracted from each waveform, including two original indexes. The extraction rate (ER) and Pearson P were calculated to evaluate the applicability of each feature for model training. The magnitude of cf-PWV was predicted by an XGBoost-based model using the selected features and basic physiological parameters (age, sex, height, weight and BMI). The level of aortic stiffness was classified by a 3-classification strategy according to the standard cf-PWV (measured by the Complior device). Bland-Altman plot, Pearson correlation analysis, and accuracy tested performance from two aspects: predicting the magnitude of cf-PWV and classifying the level of aortic stiffness. Results: In the external validation set (n = 100, age range 22-79), 97 subjects obtained features (ER = 97%). The predicted cf-PWV was significantly correlated with the standard cf-PWV (r = 0.927, P < 0.001). The accuracy (AC) of the 3-classification was 85.6%. The interrater agreement for assessing aortic stiffness was at least substantial (quadratically weighted Kappa = 0.833). Conclusions: The multi-parameter fusion cf-PWV prediction method based on the XGBoost algorithm and wPPG pulse wave analysis proves the feasibility of atherosclerosis screening in wearable devices. (C) 2022 Elsevier B.V. All rights reserved.
WOS关键词PULSE-WAVE VELOCITY ; CARDIOVASCULAR RISK-FACTORS ; EXPERT CONSENSUS DOCUMENT ; ARTERIAL STIFFNESS ; INDEPENDENT PREDICTOR ; HYPERTENSIVE PATIENTS ; CONTOUR ANALYSIS ; ASCENDING AORTA ; BLOOD-PRESSURE ; VOLUME PULSE
资助项目National Key Research and Development Program of China ; Science and Technology Service Network Initiative of Chi- nese Academy of Sciences ; Natural Science Foundation of Anhui Province ; [2020YFC2005603] ; [KFJ-STS-ZDTP-079] ; [2108085MF199]
WOS研究方向Computer Science ; Engineering ; Medical Informatics
语种英语
出版者ELSEVIER IRELAND LTD
WOS记录号WOS:000866229800006
资助机构National Key Research and Development Program of China ; Science and Technology Service Network Initiative of Chi- nese Academy of Sciences ; Natural Science Foundation of Anhui Province
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/129488]  
专题中国科学院合肥物质科学研究院
通讯作者Xu, Yang
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yunlong,Xu, Yang,Ma, Zuchang,et al. An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,226.
APA Li, Yunlong,Xu, Yang,Ma, Zuchang,Ye, Yuqi,Gao, Lisheng,&Sun, Yining.(2022).An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,226.
MLA Li, Yunlong,et al."An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 226(2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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