在COVID-19患者中推断的呼吸得分从炼诱导的呼吸障碍中学习
Zijing Zhang1, Jianlin Zhou1, Thomas B Conroy1
1School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
可穿戴式传感器可以通过分析呼吸模式,客观地评估COVID-19患者的呼吸障碍. 这项技术为监测呼吸道疾病提供了一种可靠的方法,特别是当自我报告很困难时.
科学领域:
- 生物医学工程 生物医学工程
- 呼吸系统医学 呼吸系统医学
- 医疗保健中的人工智能
背景情况:
- 呼吸不良是COVID-19等呼吸系统疾病中常见的主观症状.
- 目前的临床评估依赖于自我报告,这容易导致偏见和频繁监测的局限性.
- 客观,持续评估呼吸障碍是有效的患者管理所需的.
研究的目的:
- 评估可穿戴传感器的使用,以评估COVID-19患者的呼吸得分.
- 为了确定一个学习模型,训练在健康受试者的生理诱导性呼吸障碍,可以预测COVID-19患者的呼吸障碍.
- 为了使呼吸状况的持续,客观监测.
主要方法:
- 使用非侵入性可穿戴呼吸传感器收集持续的呼吸数据.
- 在12名COVID-19患者身上记录了夜间呼吸波形.
- 对13名健康受试者进行了比较基准,这些受试者患有运动诱导性呼吸障碍;使用32名健康受试者的数据开发了一个学习模型.
主要成果:
- 在COVID-19患者的呼吸系统特征和诱导性呼吸障碍的健康受试者之间发现了很高的相似性.
- 该学习模型成功地推断出与正常呼吸相比,COVID-19患者的高度相关的呼吸得分.
- 在12-16小时内对呼吸得分的持续评估是可行的.
结论:
- 可穿戴式传感器技术可以为COVID-19患者提供呼吸障碍的客观测量.
- 基于健康受试者的学习模型可以有效地评估COVID-19患者的呼吸障碍.
- 该系统为监测呼吸系统疾病提供了有价值的工具,有助于早期干预和改善结果,特别是在非沟通患者中.
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