一种基于面部视频的非接触式血压测量方法
Lifeng Yang1, Shaojie Gu1, Binbin Liu1
1School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Physical and engineering sciences in medicine
|October 20, 2025
概括
这项研究引入了一种非接触式方法,使用面部视频和机器学习来测量血压. 该技术准确估计血压,符合国际医疗器械标准.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
- 人工智能在医学中的应用
背景情况:
- 准确的血压测量对于心血管健康和疾病预防至关重要.
- 光电脉冲图 (PPG) 和机器学习为非侵入性血压监测提供了有前途的途径.
- 目前的方法可能缺乏方便性或需要身体接触.
研究的目的:
- 开发和验证使用面部视频估计血压的非接触方法.
- 为了利用面部远程光电显微镜 (RPPG) 信号和机器学习进行准确的读数.
- 评估该方法是否符合既定医疗标准.
主要方法:
- 从摄像机拍摄的面部视频中提取RPPG信号.
- 应用信号处理技术来提高RPPG的质量.
- 使用极端梯度提升树 (XGBoost) 模型进行血压回归.
主要成果:
- 达到测量误差为4.8893 ± 6.6237 mmHg对于心压和4.0805 ± 5.5821 mmHg对于心压.
- 证明完全符合美国医疗仪器协会 (AAMI) 的标准.
- 根据英国高血压协会 (BHS) 的标准,对静脉缩和腹缩血压预测均获得A级评估.
结论:
- 拟议的非接触式方法提供了精确的血压测量与最小的错误.
- 这种方法为血压监测提供了方便和有效的替代方案.
- 该方法符合AAMI和BHS标准,验证了其临床潜力.
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