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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1222898/full |
| _version_ | 1797768538399703040 |
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| author | Adriano Tramontano Oscar Tamburis Oscar Tamburis Salvatore Cioce Salvatore Venticinque Mario Magliulo |
| author_facet | Adriano Tramontano Oscar Tamburis Oscar Tamburis Salvatore Cioce Salvatore Venticinque Mario Magliulo |
| author_sort | Adriano Tramontano |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T20:54:50Z |
| format | Article |
| id | doaj.art-1d14e54aa9b144f299139cdbdf60133a |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-03-12T20:54:50Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-1d14e54aa9b144f299139cdbdf60133a2023-07-31T16:25:31ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2023-07-01510.3389/fdgth.2023.12228981222898Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluationAdriano Tramontano0Oscar Tamburis1Oscar Tamburis2Salvatore Cioce3Salvatore Venticinque4Mario Magliulo5Institute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, ItalyInstitute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, ItalyDepartment of Veterinary Medicine and Animal Productions, University of Naples “Federico II”, Naples, ItalyInstitute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, ItalyDepartment of Engineering, University of Campania “Luigi Vanvitelli”, Aversa (CE), ItalyInstitute of Biostructures and Bioimaging, National Research Council (IBB–CNR), Naples, ItalyMedical 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.https://www.frontiersin.org/articles/10.3389/fdgth.2023.1222898/fulleHealthbpmphotoplethysmographyballistocardiographycomputer architecturesignal processing |
| spellingShingle | Adriano Tramontano Oscar Tamburis Oscar Tamburis Salvatore Cioce Salvatore Venticinque Mario Magliulo Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation Frontiers in Digital Health eHealth bpm photoplethysmography ballistocardiography computer architecture signal processing |
| 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 | eHealth bpm photoplethysmography ballistocardiography computer architecture signal processing |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1222898/full |
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