ePrints

Investigation on Pulse Wave Forward Peak Detection and Its Applications in Cardiovascular Health

Lookup NU author(s): Dr Dingchang Zheng

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

IEEEObjective: The contours of the pulse wave vary greatly, which affect the accuracy of pulse wave peak detection and the reliability of subsequent peak-based cardiovascular health analyses. We proposed an algorithm to reliably detect the peak of forward pulse wave (forward peak) and proposed to use it for improving the accuracy in cardiovascular health analysis. Methods: A method based on Gaussian fitting was proposed to detect the forward peak. Then, the forward peak was utilized for instantaneous heart rate (HR), heart rate variability (HRV), and augmentation index (a cardiovascular risk marker reflecting arterial stiffness) estimations. The accuracy of HR/HRV obtained by forward peak was compared with that obtained by other photoplethymogram (PPG) characteristic points previously reported, using electrocardiogram-derived HR/HRV as gold standard. The correlation between augmentation index and age was calculated. The performance was verified using PPG-based pulse wave data with different contours while they were recorded at different locations from subjects with a wide range of age. Results: The proposed forward peak detection method had smaller estimation error when compared with the gold standard, than other PPG characteristic points in estimating HR/HRV. The augmentation index extracted from the proposed forward peak method was significantly correlated with age (p < 0.01). Conclusions: The proposed algorithm can relatively reliably detect the forward peak and has a wide application prospect in cardiovascular health. Significance: Due to the convenience of PPG measurements, this proposed forward peak detection method has the potential to be widely used in the fields of wearable devices and telemedicine.

Publication metadata

Author(s): Wanhua L, Zheng D, Li G, Chen F, Zhou H

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Biomedical Engineering

Year: 2022

Volume: 69

Issue: 2

Pages: 700-709

Print publication date: 01/02/2022

Online publication date: 10/08/2021

Acceptance date: 02/04/2020

ISSN (print): 0018-9294

ISSN (electronic): 1558-2531

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TBME.2021.3103552

DOI: 10.1109/TBME.2021.3103552

Altmetrics

Share