AUTHOR=Vargas Juan M. , Bahloul Mohamed A. , Laleg-Kirati Taous-Meriem 

TITLE=A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study

JOURNAL=Frontiers in Physiology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1100570

DOI=10.3389/fphys.2023.1100570

ISSN=1664-042X

ABSTRACT=Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial
stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential
for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in
the clinical routine, the measurement process of cf-PWV is considered a demanding task for
clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart
non-invasive, and peripheral measurement of cf-PWV could overcome the challenges of the
classical assessment process and improve the quality of patient care. This paper proposes
a novel methodology for the cf-PWV estimation based on the spectrogram from single non
invasive peripheral pulse wave signals (photoplethysmography (PPG) or blood pressure (BP)).
This methodology was tested using three feature extraction methods based on the semi-classical
signal analysis (SCSA) method, the Law’s mask for texture energy extraction, and the central
statistical moments. Finally, each feature method was fed into different machine learning models
for the cf-PWV estimation. The results obtained for each feature method provide evidence of the
capacity of spectrogram representation combined with machine learning models as an intelligent
tool for efficiently assessing the cf-PWV estimation.