A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization

Abstract
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.

Citation
Garcia, J. M. V., Bahloul, M. A., & Laleg-Kirati, T.-M. (2022). A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871031

Acknowledgements
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) with the Base Research Fund (BAS/1/1627-01- 01).

Publisher
IEEE

Conference/Event Name
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

DOI
10.1109/embc48229.2022.9871031

Additional Links
https://ieeexplore.ieee.org/document/9871031/

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