Unobtrusive neonatal patient monitoring using video and pressure data

使用视频和压力数据进行不引人注目的新生儿患者监测

• 批准号: 543940-2019
• 负责人: Green, James
• 金额: $ 2.48万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=685407
• 关键词: Unobtrusive; neonatal; patient; monitoring; using

Our research aims to improve patient care in the neonatal intensive care unit (NICU) through the development of novel unobtrusive sensors and algorithms. Over the past 2.5 years, with NSERC CRD support, we have collected a unique, rich, multi-sensor patient dataset from the NICU at the Children's Hospital of Eastern Ontario (CHEO). Through continued collaboration with IBM's Centre for Advanced Studies and clinicians at CHEO, we will now mine these data using machine learning and computer vision to create novel non-contact patient monitoring systems to augment patient care in the NICU. Our original NSERC CRD grant funded the collection of a unique dataset from 35 neonates spanning different patient masses and levels of care (bed types). Data were collected from a pressure-sensitive mat (PSM) - a first for the neonatal population; an Intel RealSense RGB-D camera that measures colour, near-infrared, and depth data; and gold-standard physiologic data from the patient monitor with wired electrodes. In addition, we have developed a custom tablet app such that a bed-side researcher was able to annotate all events of clinical interest simultaneously with the sensor data collection. These data are unique, in that we have multiple sensor data streams collected simultaneously with gold standard physiologic data and clinical event annotations. The present research will develop and evaluate machine learning and computer vision algorithms for estimating physiologic signals, such as respiration rate, from non-contact sensors. Our Temporal Event Annotation (TEA) framework will be extended and released as an open-source project to enable other research groups to automatically generate native data collection apps based solely on a description of the event types to be captured. Lastly, patient movement will also be detected and characterized, with the ultimate goal to reduce false alarms resulting from motion artifacts.

我们的研究旨在通过开发新颖的不显眼的传感器和算法来改善新生儿重症监护病房（NICU）的患者护理。在过去的两年半里，在NSERC CRD的支持下，我们从东安大略省儿童医院（CHEO）的新生儿重症监护室收集了一个独特、丰富、多传感器的患者数据集。通过与IBM高级研究中心和CHEO临床医生的持续合作，我们现在将使用机器学习和计算机视觉来挖掘这些数据，以创建新的非接触式患者监测系统，以增强新生儿重症监护室的患者护理。我们最初的NSERC CRD拨款资助了35个新生儿的独特数据集的收集，这些新生儿跨越了不同的患者群体和护理水平（床位类型）。数据收集自压敏垫(PSM) -新生儿人口的第一个；可测量颜色、近红外和深度数据的英特尔RealSense RGB-D摄像头；还有来自病人监护仪的标准生理数据。此外，我们还开发了一个定制的平板电脑应用程序，这样床边的研究人员就可以在收集传感器数据的同时注释所有临床感兴趣的事件。这些数据是独一无二的，因为我们有多个传感器数据流，同时收集了黄金标准的生理数据和临床事件注释。目前的研究将开发和评估机器学习和计算机视觉算法，用于估计来自非接触式传感器的生理信号，如呼吸速率。我们的时间事件注释（TEA）框架将作为一个开源项目进行扩展和发布，使其他研究小组能够仅根据要捕获的事件类型描述自动生成本地数据收集应用程序。最后，患者的运动也将被检测和表征，最终目标是减少由运动伪影引起的误报。