A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, Makkah Province, Saudi Arabia
Electrical and Computer Engineering
Electrical and Computer Engineering Program
Estimation, Modeling and ANalysis Group
KAUST Grant NumberBAS/1/1627-01- 01
Permanent link to this recordhttp://hdl.handle.net/10754/681140
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AbstractIn 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.
CitationGarcia, 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
SponsorsResearch 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).
Conference/Event name2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)