Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients.
Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients.
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DOI：
10.1073/pnas.2026610118
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发表时间：
2021-05-11
影响因子：
11.1
通讯作者：
Rogers JA
中科院分区：
文献类型：
--
作者：
Ni X;Ouyang W;Jeong H;Kim JT;Tzaveils A;Mirzazadeh A;Wu C;Lee JY;Keller M;Mummidisetty CK;Patel M;Shawen N;Huang J;Chen H;Ravi S;Chang JK;Lee K;Wu Y;Lie F;Kang YJ;Kim JU;Chamorro LP;Banks AR;Bharat A;Jayaraman A;Xu S;Rogers JA
对健康状况的持续测量可用于指导患者的护理和控制传染病的传播。传统的监测系统无法部署在医院环境之外，现有的可穿戴设备无法捕获关键的呼吸生物标志物。本文描述了一种自动化无线设备和一种数据分析方法，可以克服这些限制，为COVID-19患者、一线医护人员和其他高风险人员量身定制。生命体征和呼吸活动（如咳嗽）可以揭示感染的早期体征，并量化对治疗的反应。在临床和家庭环境中对COVID-19患者进行的长期试验证明了这项技术的转化价值。在全球COVID-19大流行的背景下，持续监测疾病关键生理参数的能力从未像现在这样重要。柔软的皮肤安装式电子器件结合了高带宽、小型化的运动传感器，能够以高保真度和对环境噪声的免疫力对核心生命体征（心率、呼吸率和体温）和未充分探索的生物标志物（咳嗽计数）的机械声（MA）特征进行数字无线测量。本文总结了将此类MA传感器与云数据基础设施以及一组基于数字过滤和卷积神经网络的分析方法相集成的努力，用于监测医院和家庭中患病和健康个体的COVID-19感染。独特的功能是定量测量咳嗽和其他声音事件，作为疾病和传染性的指标。系统的成像研究证明了咳嗽、说话和大笑的时间和强度与总液滴产生之间的相关性，作为疾病传播概率的近似指标。这些传感器沿着部署在住院和家庭环境中的COVID-19患者以及健康对照人员身上，连续记录咳嗽频率和强度，并沿着其他生物识别技术的集合。结果表明，在疾病恢复过程中，咳嗽频率和强度呈衰减趋势，但患者人群之间存在很大差异。该方法创造了研究个人和不同人口群体之间生物识别模式的机会。
Continuous measurements of health status can be used to guide the care of patients and to manage the spread of infectious diseases. Conventional monitoring systems cannot be deployed outside of hospital settings, and existing wearables cannot capture key respiratory biomarkers. This paper describes an automated wireless device and a data analysis approach that overcome these limitations, tailored for COVID-19 patients, frontline health care workers, and others at high risk. Vital signs and respiratory activity such as cough can reveal early signs of infection and quantitate responses to therapeutics. Long-term trials on COVID-19 patients in clinical and home settings demonstrate the translational value of this technology. Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.
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影响因子：
18.6
作者：
Gravina, Raffaele;Alinia, Parastoo;Fortino, Giancarlo
通讯作者：
Fortino, Giancarlo
DOI：
10.1109/jbhi.2013.2239303
发表时间：
2013-05-01
影响因子：
7.7
作者：
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DOI：
10.1073/pnas.2012156117
发表时间：
2020-10-13
影响因子：
11.1
作者：
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通讯作者：
Stone, Howard A.
影响因子：
4.6
作者：
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通讯作者：
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