一种新的方法来确定从外围测量的中心压力波形,使用基于富里埃的机器学习
Arian Aghilinejad1, Alessio Tamborini1, Morteza Gharib1
1Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, United States.
Artificial intelligence in medicine
|June 26, 2024
概括
一种新的基于富里埃的机器学习 (F-ML) 方法,通过辐射动脉测量准确地估计了中央大动脉压力. 这种非侵入性技术可以通过可穿戴设备改善血液动力学监测.
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
- 医疗保健中的机器学习
背景情况:
- 辐射平面化度测量是血液动力学监测的一个关键的非侵入性方法.
- 精确的中央大动脉压力估计从辐射测量需要强大的数学模型.
- 可穿戴医疗保健电子设备越来越多地使用辐射压力传感器.
研究的目的:
- 开发和验证一种新的基于福里埃的机器学习 (F-ML) 方法.
- 为了准确地将非侵入性的辐射压力波形转移到中心压力波形.
- 提高可穿戴设备用于中央血液动力学评估的实用性.
主要方法:
- 开发了一个基于富里埃的机器学习 (F-ML) 转移函数.
- 该方法使用Framingham心脏研究 (2640名参与者) 的辐射和状动脉压力记录进行了测试.
- 通过将F-ML估计与测量的中心压力特征相关联并评估波形形态的准确性来评估性能.
主要成果:
- F-ML估计显示高相关性与测量的静缩 (r=0.97),静缩 (r=0.99) 和平均 (r=0.99) 血压.
- 布兰德-阿尔特曼分析表明偏差可以忽略不计,误差限制在5.4mmHg.
- 在重建中心压力波形状时,F-ML方法实现了5.5%的平均规范化根平均平方误差,优于传统方法.
结论:
- 拟议的F-ML转移函数从辐射测量中提供准确的中央压力波形估计.
- 与现有技术相比,这种方法显著改善了中央压力波形形态的重建.
- F-ML方法有可能扩大在中央血液动力学评估中使用非侵入性设备的潜力.
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