Year: 2022
Author: Jianhong Chen, Wenrui Hao, Pengtao Sun, Lian Zhang
Communications in Computational Physics, Vol. 31 (2022), Iss. 4 : pp. 1114–1133
Abstract
Blood pressure (BP) has been identified as one of the main factors in cardiovascular disease and other related diseases. Then how to accurately and conveniently measure BP is important to monitor BP and to prevent hypertension. This paper proposes an efficient BP measurement model by integrating a fluid-structure interaction model with the photoplethysmogram (PPG) signal and developing a data-driven computational approach to fit two optimization parameters in the proposed model for each individual. The developed BP model has been validated on a public BP dataset and has shown that the average prediction errors among the root mean square error (RMSE), the mean absolute error (MAE), the systolic blood pressure (SBP) error, and the diastolic blood pressure (DBP) error are all below 5 mmHg for normal BP, stage I, and stage II hypertension groups, and, prediction accuracies of the SBP and the DBP are around 96% among those three groups.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2021-0135
Communications in Computational Physics, Vol. 31 (2022), Iss. 4 : pp. 1114–1133
Published online: 2022-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 20
Keywords: Blood pressure prediction fluid-structure interaction PPG.