在长时间热/冷液体给药期间,面部远程光容积描记术用于持续心率、卒中量和全身血管阻力监测

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mahdi Momeni;Sophie Wuthe;Michaela Bitten Mølmer;Emilie Löbner Svendsen;Mikkel Brabrand;Peter Biesenbach;Daniel Teichmann
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引用次数: 0

摘要

血流动力学参数——脑卒中量(SV)、全身血管阻力(SVR)和心率(HR)——对临床监测至关重要,特别是在急诊和重症监护环境中。本研究评估了一种使用成像光容积脉搏波(iPPG)的无创、非接触监测方法的可行性,该方法采用基于摄像机的光容积脉搏波和MATLAB实现的信号处理管道。16名健康志愿者在欧登塞大学医院接受温($37~^{\circ}$ C)和冷($15~^{\circ}$ C)林格氏乳酸静脉滴注,共收集25段长时间录像(2500分钟)。为了保证数据的可靠性,对视频质量和头部运动进行了系统分析。使用平面正交皮肤(POS)方法进行HR估计的平均绝对误差(avAE)为4.28 bpm,最佳精度为2.18 bpm,而CHROM方法的性能相似(平均误差为4.27 bpm,最佳精度为2.55 bpm)。SV和SVR与参考测量值(分别为${r} ={0.571}$和${r} ={0.596}$)在5个感兴趣区域表现出中度相关性。SV和SVR的最佳相关性分别为${r} ={0.846}$和${r} ={0.873}$,表明具有很强的非接触监测潜力。这些结果表明,iPPG可以在液体治疗期间提供实时心血管信息,在远程医疗和移动医疗中具有潜在的应用前景。然而,为了有力地推广本研究的结果,应该考虑个体间可变性和纳入不同患者群体的需求等局限性。这项研究首次证明了iPPG在长时间紧急输液中的可行性,为在动态临床环境中的进一步研究铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Remote Photoplethysmography for Continuous Heart Rate, Stroke Volume, and Systemic Vascular Resistance Monitoring During Prolonged Warm/Cold Fluid Bolus Administration
Hemodynamic parameters—stroke volume (SV), systemic vascular resistance (SVR), and heart rate (HR)—are critical for clinical monitoring, particularly in emergency and critical care settings. This study evaluates the feasibility of a noninvasive, noncontact monitoring approach using imaging photoplethysmography (iPPG), which applies camera-based PPG and a signal processing pipeline implemented in MATLAB. A total of 25 prolonged video recordings (2500 min) were collected from 16 healthy volunteers at Odense University Hospital, while they received intravenous infusions of warm ( $37~^{\circ }$ C) and cold ( $15~^{\circ }$ C) Ringer’s lactate. To ensure data reliability, video quality and head motion were systematically analyzed. HR estimation using the plane-orthogonal-to-skin (POS) method achieved an average absolute error (avAE) of 4.28 bpm, with the best accuracy of 2.18 bpm, while the CHROM method yielded similar performance (4.27-bpm average error and 2.55-bpm best accuracy). SV and SVR demonstrated moderate correlation with reference measures ( ${r} = {0.571}$ and ${r} = {0.596}$ , respectively) across five regions of interest. The best correlations for SV and SVR were ${r} = {0.846}$ and ${r} = {0.873}$ , respectively, indicating strong potential for accurate noncontact monitoring. These results suggest that iPPG can provide real-time cardiovascular insights during fluid therapy, with potential applications in telemedicine and mobile health. However, to robustly generalize the findings of this study, limitations such as interindividual variability and the need to include diverse patient populations should be considered. This study provides the first demonstration of iPPG feasibility for prolonged emergency fluid administration, paving the way for further research in dynamic clinical environments.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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