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Non-Invasive Blood Pressure Sensing via Machine Learning

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring cont...

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Detalles Bibliográficos
Autores principales: Attivissimo, Filippo, D’Alessandro, Vito Ivano, De Palma, Luisa, Lanzolla, Anna Maria Lucia, Di Nisio, Attilio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574845/
https://www.ncbi.nlm.nih.gov/pubmed/37837172
http://dx.doi.org/10.3390/s23198342
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author Attivissimo, Filippo
D’Alessandro, Vito Ivano
De Palma, Luisa
Lanzolla, Anna Maria Lucia
Di Nisio, Attilio
author_facet Attivissimo, Filippo
D’Alessandro, Vito Ivano
De Palma, Luisa
Lanzolla, Anna Maria Lucia
Di Nisio, Attilio
author_sort Attivissimo, Filippo
collection PubMed
description In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.
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spelling pubmed-105748452023-10-14 Non-Invasive Blood Pressure Sensing via Machine Learning Attivissimo, Filippo D’Alessandro, Vito Ivano De Palma, Luisa Lanzolla, Anna Maria Lucia Di Nisio, Attilio Sensors (Basel) Article In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard. MDPI 2023-10-09 /pmc/articles/PMC10574845/ /pubmed/37837172 http://dx.doi.org/10.3390/s23198342 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
Attivissimo, Filippo
D’Alessandro, Vito Ivano
De Palma, Luisa
Lanzolla, Anna Maria Lucia
Di Nisio, Attilio
Non-Invasive Blood Pressure Sensing via Machine Learning
title Non-Invasive Blood Pressure Sensing via Machine Learning
title_full Non-Invasive Blood Pressure Sensing via Machine Learning
title_fullStr Non-Invasive Blood Pressure Sensing via Machine Learning
title_full_unstemmed Non-Invasive Blood Pressure Sensing via Machine Learning
title_short Non-Invasive Blood Pressure Sensing via Machine Learning
title_sort non-invasive blood pressure sensing via machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574845/
https://www.ncbi.nlm.nih.gov/pubmed/37837172
http://dx.doi.org/10.3390/s23198342
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