Cargando…

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Pav...

Descripción completa

Detalles Bibliográficos
Autores principales: Tramontano, Adriano, Tamburis, Oscar, Cioce, Salvatore, Venticinque, Salvatore, Magliulo, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424792/
https://www.ncbi.nlm.nih.gov/pubmed/37583833
http://dx.doi.org/10.3389/fdgth.2023.1222898
_version_ 1785089740221972480
author Tramontano, Adriano
Tamburis, Oscar
Cioce, Salvatore
Venticinque, Salvatore
Magliulo, Mario
author_facet Tramontano, Adriano
Tamburis, Oscar
Cioce, Salvatore
Venticinque, Salvatore
Magliulo, Mario
author_sort Tramontano, Adriano
collection PubMed
description Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.
format Online
Article
Text
id pubmed-10424792
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-104247922023-08-15 Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation Tramontano, Adriano Tamburis, Oscar Cioce, Salvatore Venticinque, Salvatore Magliulo, Mario Front Digit Health Digital Health Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight. Frontiers Media S.A. 2023-07-31 /pmc/articles/PMC10424792/ /pubmed/37583833 http://dx.doi.org/10.3389/fdgth.2023.1222898 Text en © 2023 Tramontano, Tamburis, Cioce, Venticinque and Magliulo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Digital Health
Tramontano, Adriano
Tamburis, Oscar
Cioce, Salvatore
Venticinque, Salvatore
Magliulo, Mario
Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title_full Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title_fullStr Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title_full_unstemmed Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title_short Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
title_sort heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10424792/
https://www.ncbi.nlm.nih.gov/pubmed/37583833
http://dx.doi.org/10.3389/fdgth.2023.1222898
work_keys_str_mv AT tramontanoadriano heartrateestimationfromballistocardiogramsignalsprocessingvialowcosttelemedicinearchitecturesacomparativeperformanceevaluation
AT tamburisoscar heartrateestimationfromballistocardiogramsignalsprocessingvialowcosttelemedicinearchitecturesacomparativeperformanceevaluation
AT ciocesalvatore heartrateestimationfromballistocardiogramsignalsprocessingvialowcosttelemedicinearchitecturesacomparativeperformanceevaluation
AT venticinquesalvatore heartrateestimationfromballistocardiogramsignalsprocessingvialowcosttelemedicinearchitecturesacomparativeperformanceevaluation
AT magliulomario heartrateestimationfromballistocardiogramsignalsprocessingvialowcosttelemedicinearchitecturesacomparativeperformanceevaluation