利用智能床技术检测COVID-19症状:一个案例研究
Gary Garcia-Molina1,2, Dmytro Guzenko3, Susan DeFranco4
1Sleep Number Labs, 111 N Market St., Suite 500, San Jose, CA, 95113, United States, 1 6085129475.
JMIR AI
|September 22, 2025
概括
智能床可以通过分析睡眠和心肺呼吸系统数据来检测COVID-19症状. 这项技术为早期疾病检测和监测提供了一种非侵入性方法,支持公共卫生监测.
科学领域:
- 数字健康数字健康
- 生物医学工程 生物医学工程
- 传染病监测 传染病监测 传染病
背景情况:
- 像COVID-19这样的病毒感染会影响睡眠和心肺功能.
- 消费者智能床技术使得睡眠和生理信号的不引人注目的,现实世界的监控成为可能.
- 智能床是可穿戴设备和自我报告的替代品,用于客观的数据捕获.
研究的目的:
- 使用智能床上弹性心电图 (BCG) 信号和预测建模来检测和监测COVID-19症状.
- 为了利用客观的,纵向的生物识别数据进行个人级别的疾病追踪.
主要方法:
- 对1725名美国成年人的回顾性分析使用智能床数据 (脉率,呼吸率,睡眠指标).
- 一个两阶段的机器学习管道:症状检测和疾病症状进展模型.
- 使用高斯混合隐藏马尔科夫模型对症状和基线期的内对象比较.
主要成果:
- 该模型在122例阳性病例中的104例中检测到COVID-19症状.
- 在症状期间观察到睡眠和心肺呼吸指标的显著偏差 (AUC=0.80).
- 该模型在识别疾病窗口方面表现出高的歧视性.
结论:
- 智能床为被动,客观,纵向数据收集提供了宝贵的资源.
- 这些发现支持使用智能床位数据和ML用于实时COVID-19检测的可行性.
- 未来的工作包括模型的完善和适用于人口级传染病监测的应用.
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