基于机器学习的VO2估计使用可穿戴的多波长光电显微镜设备
Chin-To Hsiao1, Carl Tong2, Gerard L Coté1,3,4
1Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.
Biosensors
|April 25, 2025
概括
一个新的可穿戴设备使用光电感应和机器学习来估计氧气消耗 (VO2). 这项技术在临床环境之外提供持续,准确的心血管健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 可穿戴技术可穿戴技术
背景情况:
- 氧气消耗 (VO2) 对于评估心血管健康,新陈代谢状态和呼吸功能至关重要.
- VO2是心力衰竭 (HF) 患者的关键预后指标,反映心脏输出 (CO) 和有氧健身.
- 传统的VO2评估方法是繁的,限制了持续监测.
研究的目的:
- 开发和验证一款具有连续VO2估计的机器学习算法的新型手腕佩戴光电显微镜 (PPG) 设备.
- 克服传统的,庞大的VO2评估系统的局限性.
主要方法:
- 一个多波长的PPG可穿戴设备使用五个波长 (670-950纳米) 和酒-兰伯特定律被开发出来.
- 一个机器学习算法结合了PPG数据,包括810nm的同位点,以估计VO2.
- 验证涉及八名使用修改的布鲁斯协议的受试者,将PPG估计值与黄金标准气体分析系统进行比较.
主要成果:
- 基于PPG的VO2估计达到1.66毫升/公斤/分钟的平均绝对误差.
- 该模型与黄金标准测量的相关性很高,达到0.94.9的R2.
- 该设备根据直接组织氧化数据提供了精确的,个性化的VO2估计.
结论:
- 这种新型可穿戴PPG设备为持续和准确的VO2监测提供了临床上可行的解决方案.
- 这项技术可以在专业的临床环境之外进行可访问的心血管评估.
- 这些发现表明,用于个性化医疗的可穿戴健康技术取得了重大进展.
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