A learning-based image processing approach for pulse wave velocity estimation using spectrogram from peripheral pulse wave signals: An in silico study - Vargas, Juan M.; Bahloul, Mohamed A.; Laleg-Kirati, Taous-Meriem (Frontiers in Physiology, 2023)
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.