A multiple linear regression model using semi-classical signal analysis successfully estimated carotid-to-femoral pulse wave velocity from a single non-invasive peripheral pulse wave in-silico.
A multiple linear regression model using semi-classical signal analysis can feasibly estimate carotid-to-femoral pulse wave velocity from a single non-invasive peripheral pulse wave in silico.
In this paper, a multiple linear regression model for estimating the Carotid-to-femoral pulse wave velocity (cf-PWV) from a single non-invasive peripheral pulse wave, namely blood pressure or photoplethysmography, is proposed. The training and testing datasets were extracted from in-silico, publicly available, pulse waves and hemodynamics data. The proposed model relies on a preprocessing and features extraction steps, which are performed using a semi-classical signal analysis (SCSA) method. The obtained results provide more evidence for the feasibility of machine learning and the SCSA method as a smart tool for the efficient assessment of the cf-PWV.
Garcia et al. (Mon,) conducted a other in Carotid-to-femoral pulse wave velocity estimation. Multiple linear regression model with semi-classical signal analysis (SCSA) was evaluated on Carotid-to-femoral pulse wave velocity (cf-PWV) estimation. A multiple linear regression model using semi-classical signal analysis successfully estimated carotid-to-femoral pulse wave velocity from a single non-invasive peripheral pulse wave in-silico.