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Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something w...

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Autores principales: Hamoud, Batol, Kashevnik, Alexey, Othman, Walaa, Shilov, Nikolay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959092/
https://www.ncbi.nlm.nih.gov/pubmed/36850349
http://dx.doi.org/10.3390/s23041753
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author Hamoud, Batol
Kashevnik, Alexey
Othman, Walaa
Shilov, Nikolay
author_facet Hamoud, Batol
Kashevnik, Alexey
Othman, Walaa
Shilov, Nikolay
author_sort Hamoud, Batol
collection PubMed
description One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone.
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spelling pubmed-99590922023-02-26 Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation Hamoud, Batol Kashevnik, Alexey Othman, Walaa Shilov, Nikolay Sensors (Basel) Article One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone. MDPI 2023-02-04 /pmc/articles/PMC9959092/ /pubmed/36850349 http://dx.doi.org/10.3390/s23041753 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hamoud, Batol
Kashevnik, Alexey
Othman, Walaa
Shilov, Nikolay
Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title_full Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title_fullStr Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title_full_unstemmed Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title_short Neural Network Model Combination for Video-Based Blood Pressure Estimation: New Approach and Evaluation
title_sort neural network model combination for video-based blood pressure estimation: new approach and evaluation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9959092/
https://www.ncbi.nlm.nih.gov/pubmed/36850349
http://dx.doi.org/10.3390/s23041753
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AT shilovnikolay neuralnetworkmodelcombinationforvideobasedbloodpressureestimationnewapproachandevaluation