SCH: EXP: Collaborative Research: Design of a wearable biosensor system with wireless network for the remote detection of life threatening events in neonates

在美国，八分之一的婴儿早产。这些高危婴儿不仅需要在新生儿重症监护病房 (NICU) 中而且还需要在家庭环境中对其生理机能进行专门监测。他们容易出现呼吸暂停（呼吸暂停）、心动过缓（心跳缓慢）和缺氧（氧饱和度下降），从而危及生命。该项目旨在开发一种具有无线网络的生物传感器系统，用于远程检测和预测此类危及婴儿生命的事件。拟议的研究超越了传统的健康监测系统，将身体传感器网络（BSN）与专门针对单个婴儿生理机能定制的先进信号处理方法相结合，以准确检测和预测危及生命事件的前兆。拟议的研究可以通过无线生物传感器系统对婴儿的非侵入式动态健康监测产生重大影响，该系统将轻型传感器解决方案集成到用于监测生理学的传感、通信和计算中。该项目开发的系统框架、理论、模型和代码可供研究人员和工程师用来评估婴儿监测应用的性能。项目还包括：（1）向学术界和业界传播项目信息和知识； (2) 让本科生、研究生和医学生，特别是女性和少数族裔参与拟议的研究； (3)开发新课程和修改现有课程。目前新生儿重症监护室使用的生理监测系统由附着在婴儿身上的相对较大的传感器组成，然后通过多条电线连接到数据采集系统。这些传感器和电线阻碍了临床护理。此外，由于尺寸和成本的原因，现有系统无法用于家庭环境。虽然 NICU 监测系统中存在大量的生理信号，但对于护理早产儿的临床医生来说，确定病理状态具有挑战性，因为没有可用的方法将这些信号转化为有效的指标来定义病理。这项拟议研究的主要目的是探索是否可以构建带有可穿戴生物传感器和无线网络的专用紧凑型设备，用于检测和预测新生儿重症监护病房和家庭环境中婴儿的危及生命的事件。第二个目标是探索是否可以开发提供实时心肺风险指数的计算工具来协助临床医生进行新生儿护理。具体来说，该项目是开发一个综合系统，涉及四个重要组成部分：（1）开发可以附着在非常小且脆弱的婴儿身上的微型生物传感器； （2）开发具有高效通信协议的无线设备，可以传输来自生物传感器的生理信号； (3) 开发有效的信号处理算法，可以从生物传感器数据中提取有用的信息，用于风险分层和危及生命事件的预测（数据到知识到决策），以及 (4) 在 NICU 的现实生活环境中测试和验证系统。该项目中提出的方法最终可以开发出一种用于远程检测婴儿危及生命事件的医疗设备，并为一般医疗保健监测应用的可穿戴无线生物传感器系统的设计提供指导。

In the United States, one in eight infants is born prematurely. These high risk infants require specialized monitoring of their physiology not only in Neonatal Intensive Care Units (NICU) but also in home environments. They are prone to apnea (pause in breathing), bradycardia (slowness of heart) and hypoxia (oxygen de-saturation), which are life threatening. This project aims at developing a biosensor system with wireless network for the remote detection and anticipation of such life threatening events in infants. The proposed research goes beyond traditional health monitoring systems by incorporating body sensor networks (BSN) along with advanced signal processing approaches, tailored specifically to an individual infant's physiology, to accurately detect and anticipate precursors of life threatening events. The proposed research can have a significant impact on non-intrusive ambulatory health monitoring for infants through a wireless biosensor system that integrates lightweight sensor solutions into the sensing, communication, and computing for monitoring physiology. The system framework, theories, models, and code developed by this project can be used by researchers as well as engineers to evaluate the performance of infant monitoring applications. The project also includes: (1) disseminating the project information and knowledge to the academic community and industry; (2) engaging undergraduate, graduate and medical students, especially women and minorities, into the proposed research; and (3) developing new courses and revising the existing courses. The current physiological monitoring systems used in NICU consist of relatively large sensors attached to the infants, which are then connected to a data acquisition system with multiple wires. These sensors along with the wires are a hindrance to the clinical care. In addition, the existing system cannot be used for home environments because of the size and cost. While there is an abundance of physiological signals streaming across NICU monitoring systems, it is challenging for clinicians caring for preterm infants to determine pathological states, as there is no method available to translate these signals into validated indices to define pathology. The primary objective of this proposed research is to explore whether a dedicated compact device with wearable biosensors along with wireless networks can be built for the detection and anticipation of life threatening events in infants in both NICU and home environments. The secondary objective is to explore whether computational tools that provide real-time indices of cardio-respiratory risk can be developed to assist clinicians for neonatal care. Specifically, the project is to develop a comprehensive system, involving four important components: (1) development of miniature biosensors that can be attached to infants who are very small and vulnerable; (2) development of wireless devices with efficient communication protocols that can transmit the physiological signals from the biosensors; (3) development of efficient signal processing algorithms that can extract useful information from the biosensor data for risk stratification and anticipation of life threatening events (data to knowledge to decisions) and (4) testing and validation of the systems in real life environment at NICU. The proposed approaches in the project can eventually lead to a medical device for the remote detection of life threatening events in infants and also provide guidelines for the design of wearable wireless biosensor systems for healthcare monitoring applications in general.