Tramontano Adriano, Tamburis Oscar, Cioce Salvatore, Venticinque Salvatore, Magliulo Mario
Institute of Biostructures and Bioimaging, National Research Council (IBB-CNR), Naples, Italy.
Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy.
Front Digit Health. 2023 Jul 31;5:1222898. doi: 10.3389/fdgth.2023.1222898. eCollection 2023.
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.
医疗设备（MDs）已被设计用于监测多个领域患者的参数。尽管如此，尽管它们性能高且可靠，但往往价格昂贵且具有侵入性。此外，医疗设备几乎仅用于可控的医院环境。在临床领域开辟技术创新之路，目前一个非常活跃的研究方向是探讨能否依靠非医疗级低成本设备，开发旨在无创收集数据、信号和图像的无人值守远程医疗（TM）解决方案。在本文中，提出了一种用于监测患者睡眠期间心率（HR）的远程医疗解决方案。部署了一个远程患者监测系统（RPMS），该系统配备了智能腰带，腰带上装有用于心冲击图（BCG）信号采样的压力传感器。然后对24名志愿者进行了为期2个月的现场试验，这些志愿者还同意佩戴能够产生光电容积脉搏波描记图（PPG）信号的手指脉搏血氧仪作为金标准，通过从收集的BCG信号中估计心率值来检验该解决方案的可行性。为此，从文献中检索了两种用于从BCG信号估计心率的性能最佳的方法，一种是算法方法，另一种基于卷积神经网络（CNN），并针对与远程医疗相关的用例进行了更新。最后，根据患者层面的平均绝对误差（MAE）评估心率估计性能。从文献（可控环境）中获得的结果分别比实验（远程医疗环境）中获得的结果高出29%（MAE = 4.24对5.46，算法方法）和52%（MAE = 2.32对3.54，基于CNN的方法）。尽管如此，低丢包率、所收集生物医学大数据的处理时间有限、低成本部署以及用户的积极反馈，证明了所提出的远程医疗解决方案的稳健性、可靠性和适用性。有鉴于此，将计划采取进一步措施以实现新目标，例如评估呼吸率（RR）以及参与者夜间运动模式评估。
