多源因果不变为无袖血压估计基于光电显微镜信号特征的多源因果不变.
Yiliu Xu1, Zhaoming He2, Hao Wang3
1Research Center of Fluid Machinery Engineering & Technology, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究引入了一种新的方法,用于使用光电显微镜 (PPG) 功能准确监测无袖血压 (BP). 该方法提高了个人健康管理的预测准确性和概括性.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 无袖连续血压 (BP) 监测对于个人健康管理至关重要.
- 准确性挑战来自多样化和异质化的生理数据来源.
研究的目的:
- 开发一个多源特征选择框架,用于准确的无袖BP估计.
- 为了利用马尔科夫毯理论和因果不变性来进行强大的特征选择.
主要方法:
- 从三个异构的数据集中提取了218个与BP相关的光聚体显微镜 (PPG) 特性.
- 通过集成马尔科夫毯 (MDSFS-EMB) 算法使用多数据集稳定特征选择构建了一个因果特征集.
- 雇佣的高斯方圆互联信息,用于强大的非线性关系建模.
主要成果:
- MDSFS-EMB算法展示了优越的特征选择效率和预测准确度.
- 选择的特征集在独立的外部验证数据集上显示出强大的概括能力.
- 在多个数据源中创新地探索了PPG特征和BP之间的因果关系.
结论:
- 拟议的框架为无袖血压估计提供了临床适用的方法.
- MDSFS-EMB算法为BP监测提供了一个可适应和准确的解决方案.
- 这项研究通过因果推断来推进基于PPG的BP监测的理解.
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