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Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is...

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Autores principales: Vysotskaya, Nastassia, Will, Christoph, Servadei, Lorenzo, Maul, Noah, Mandl, Christian, Nau, Merlin, Harnisch, Jens, Maier, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145629/
https://www.ncbi.nlm.nih.gov/pubmed/37112454
http://dx.doi.org/10.3390/s23084111
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author Vysotskaya, Nastassia
Will, Christoph
Servadei, Lorenzo
Maul, Noah
Mandl, Christian
Nau, Merlin
Harnisch, Jens
Maier, Andreas
author_facet Vysotskaya, Nastassia
Will, Christoph
Servadei, Lorenzo
Maul, Noah
Mandl, Christian
Nau, Merlin
Harnisch, Jens
Maier, Andreas
author_sort Vysotskaya, Nastassia
collection PubMed
description Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of [Formula: see text] mmHg (mean error ± standard deviation) and a diastolic error of [Formula: see text] mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.
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spelling pubmed-101456292023-04-29 Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study Vysotskaya, Nastassia Will, Christoph Servadei, Lorenzo Maul, Noah Mandl, Christian Nau, Merlin Harnisch, Jens Maier, Andreas Sensors (Basel) Article Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of [Formula: see text] mmHg (mean error ± standard deviation) and a diastolic error of [Formula: see text] mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further. MDPI 2023-04-19 /pmc/articles/PMC10145629/ /pubmed/37112454 http://dx.doi.org/10.3390/s23084111 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
Vysotskaya, Nastassia
Will, Christoph
Servadei, Lorenzo
Maul, Noah
Mandl, Christian
Nau, Merlin
Harnisch, Jens
Maier, Andreas
Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title_full Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title_fullStr Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title_full_unstemmed Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title_short Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study
title_sort continuous non-invasive blood pressure measurement using 60 ghz-radar—a feasibility study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10145629/
https://www.ncbi.nlm.nih.gov/pubmed/37112454
http://dx.doi.org/10.3390/s23084111
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