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型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 2.5 年里，在 NSERC CRD 的支持下，我们从东安大略儿童医院 (CHEO) 的 NICU 收集了独特、丰富的多传感器患者数据集。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，以创建新型非接触式患者监护系统，以增强 NICU 中的患者护理。我们最初的 NSERC CRD 拨款资助收集了来自 35 名新生儿的独特数据集，涵盖不同的患者群体和护理水平（床位类型）。数据是从压力敏感垫（PSM）收集的，这对于新生儿群体来说是首次；英特尔实感 RGB-D 摄像头，可测量颜色、近红外和深度数据；以及来自带有有线电极的患者监护仪的金标准生理数据。此外，我们还开发了一款定制平板电脑应用程序，以便床边研究人员能够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，因为我们同时收集了多个传感器数据流以及黄金标准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，用于估计非接触式传感器的生理信号，例如呼吸率。我们的时间事件注释（TEA）框架将作为开源项目进行扩展和发布，以使其他研究小组能够仅根据要捕获的事件类型的描述自动生成本机数据收集应用程序。最后，还将检测和表征患者的运动，最终目标是减少运动伪影导致的误报。