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

Juan Manuel Vargas Garcia; Mohamed A. Bahloul; Taous Meriem Laleg-Kirati

doi:10.1109/EMBC48229.2022.9871031

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

Juan Manuel Vargas Garcia, Mohamed A. Bahloul, Taous Meriem Laleg-Kirati

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English (US)
Title of host publication	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	143-147
Number of pages	5
ISBN (Electronic)	9781728127828
DOIs	https://doi.org/10.1109/EMBC48229.2022.9871031
State	Published - 2022
Event	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom Duration: Jul 11 2022 → Jul 15 2022

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume	2022-July
ISSN (Print)	1557-170X

Conference

Conference	44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/Territory	United Kingdom
City	Glasgow
Period	07/11/22 → 07/15/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Carotid to Femoral Pulse Wave Velocity
Machine learning
Multiple Linear Regression
non-invasive measurements
Semi-Classical Signal Analysis (SCSA) method

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC48229.2022.9871031

Cite this

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. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 (pp. 143-147). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2022-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC48229.2022.9871031

Garcia, Juan Manuel Vargas ; Bahloul, Mohamed A. ; Laleg-Kirati, Taous Meriem. / A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 143-147 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "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.",

keywords = "Carotid to Femoral Pulse Wave Velocity, Machine learning, Multiple Linear Regression, non-invasive measurements, Semi-Classical Signal Analysis (SCSA) method",

author = "Garcia, {Juan Manuel Vargas} and Bahloul, {Mohamed A.} and Laleg-Kirati, {Taous Meriem}",

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year = "2022",

doi = "10.1109/EMBC48229.2022.9871031",

language = "English (US)",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Garcia, JMV, Bahloul, MA & Laleg-Kirati, TM 2022, A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. in 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2022-July, Institute of Electrical and Electronics Engineers Inc., pp. 143-147, 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022, Glasgow, United Kingdom, 07/11/22. https://doi.org/10.1109/EMBC48229.2022.9871031

A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. / Garcia, Juan Manuel Vargas; Bahloul, Mohamed A.; Laleg-Kirati, Taous Meriem.
44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 143-147 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Garcia JMV, Bahloul MA, Laleg-Kirati TM. A Multiple Linear Regression Model for Carotid-to-Femoral Pulse Wave Velocity Estimation Based on Schrodinger Spectrum Characterization. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 143-147. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC48229.2022.9871031

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

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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