Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation

Francesca Trenta, Francesca Trenta, Francesco Rundo, Francesco Rundo, Roberto Leotta, Roberto Leotta, Sebastiano Battiato, Sebastiano Battiato

Abstract

In medical field, many cardiovascular and correlated diseases can be early treated by monitoring and analyzing the subject’s blood pressure (BP). However, the measurement of blood pressure requires the use of invasive medical and health equipment, including the classical sphygmomanometer or the digital pressure meter. In this paper, we proposed an innovative algorithmic pipeline to properly estimate the systolic and diastolic blood pressure of a subject through the visio-reconstruction of the PhotoPlethysmoGraphic (PPG) signal. By means of an innovative method of face-motion magnification through Deep Learning, it is possible to visio-reconstruct specific points of the PPG signal in order to extract features related to the pressure level of the analyzed subject. The proposed approach can be used effectively in healthcare facilities for the fast and noninvasive monitoring of the pressure level of subjects or in other similar applications. We compared our results using a classic cuff-less blood pressure device with encouraging results that reach 92% in accuracy.

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Paper Citation


in Harvard Style

Trenta F., Rundo F., Leotta R. and Battiato S. (2021). Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation. In Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-511-1, pages 75-80. DOI: 10.5220/0010380900750080


in Bibtex Style

@conference{improve21,
author={Francesca Trenta and Francesco Rundo and Roberto Leotta and Sebastiano Battiato},
title={Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation},
booktitle={Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2021},
pages={75-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010380900750080},
isbn={978-989-758-511-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Deep Visio-PhotoPlethysmoGraphy Reconstruction Pipeline for Non-invasive Cuff-less Blood Pressure Estimation
SN - 978-989-758-511-1
AU - Trenta F.
AU - Rundo F.
AU - Leotta R.
AU - Battiato S.
PY - 2021
SP - 75
EP - 80
DO - 10.5220/0010380900750080