AUTHOR=Tramontano Adriano , Tamburis Oscar , Cioce Salvatore , Venticinque Salvatore , Magliulo Mario 

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

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 5 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1222898

DOI=10.3389/fdgth.2023.1222898

ISSN=2673-253X

ABSTRACT=Medical Devices (MDs) have been designed for monitoring patients' parameters in many sectors. Nonetheless, despite being very 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 the present paper a TM solution is proposed for monitoring patients' heart rate (HR) during sleep. A Patient Remote 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 twomonth period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a Photoplethysmography (PPG) signal as gold standard, to set forth the feasibility of the solution via the estimation of HR values from the collected BCG signals. To this purpose, two among the most performing approaches for HR estimation from BCG signal, one algorithmic and another based on a Convolutional Neural Network, 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 average 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, low packet loss ratio, restrained elaboration time of the collected biomedical big-data, low-cost deployment and a positive feedback from the users, witness about the robustness, reliability and applicability of the proposed TM 2 This is a provisional file, not the final typeset article solution. In the light of this, further steps will be planned to fulfill new targets, such as the evaluation of respiratory rate (RR), and the patterns assessment from subjects' movements overnight.