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公平的非接触式血压估计,使用成像照相图解质量学
Hongli Fang1, Jiping Xiong1, Linying He1
1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China.
Biomedical optics express
|April 18, 2024
概括
这项研究引入了一种新的非接触式方法,用于通过面部视频来估计血压. 先进的人工智能模型提高了静缩和静缩血压的准确性,增强了远程健康监测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心血管监测 心血管监测
背景情况:
- 高血压的诊断通常需要专门的设备,使长期监测复杂化.
- 现有的非接触式血压估计方法在数据集多样性和适用性方面存在局限性.
- 对于连续血压评估的便携式,非接触式解决方案有很大的需求.
研究的目的:
- 开发一种实用且准确的非接触式血压估计方法.
- 创建一个适用于不同的人口统计 (年龄,种族,肤色) 和现实生活场景的系统.
- 提高当前非接触式血压估计技术的准确性和通用性.
主要方法:
- 面部视频被用来提取成像光斑血图 (IPPG) 信号.
- 应用了四层过过程来精制IPPG信号用于脉冲波分析.
- 开发了一种混合深度学习模型 (CNN+BiLSTM+GRU) 用于血压预测.
主要成果:
- 与现有方法相比,拟议的方法在估计血压方面表现出更高的准确性.
- 系统血压 (SBP) 的平均绝对误差 (MAE) 降低了13.6%.
- 扩张性血压 (DBP) 的MAE降低了16.4%.
结论:
- 开发的非接触式方法为高血压监测提供了有前途的进步.
- 该CNN+BiLSTM+GRU模型提高了基于面部的血压估计的准确性和适用性.
- 这项技术有可能在不同的人群和环境中促进方便的长期血压监测.
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01:30Equipments Used To Measure Blood Pressure
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Direct Method
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01:28Special considerations while measuring blood pressure
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01:17Measurement of Blood Pressure
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