An XGBoost-based model for assessment of aortic stiffness from wrist photoplethysmogram. (November 2022)

• Record Type: Journal Article
• Authors: Li, Yunlong; Xu, Yang; Ma, Zuchang; Ye, Yuqi; Gao, Lisheng; Sun, Yining
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Aortic stiffness -- Carotid-femoral pulse wave velocity -- Feature extraction -- Screening -- XGBoost -- Wrist photoplethysmogram; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107128
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Carotid femoral pulse wave velocity (cf-PWV) is the gold standard for non-invasive assessment of aortic stiffness. Photoplethysmogram is an aggregated expression of various physiological processes within the cardiovascular circulation system. The two original indexes extracted from wrist photoplethysmograms are of general value for the assessment of aortic stiffness, especially in mobile health applications. The proposed XGBoost-based model shows good performance in predicting the magnitude of cf-PWV compared with the standard cf-PWV (measured by the Compilor device), which contributes to the early screening of arteriosclerosis in primary healthcare. Abstract: Background and Objective: Carotid-femoral pulse wave velocity (cf-PWV) is the gold standard for non-invasive 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.