时间频率机器学习转移函数用于中央压力波形
Soha Niroumandi1, Heng Wei1, Faisal Amlani2
1Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Ave. Room 400, Los Angeles, CA 90089, USA.
European heart journal open
|July 28, 2025
概括
这项研究引入了一种新的混合机器学习模型,从外围测量中准确地重建中央动脉压力波形,改善高血压和心力衰竭的诊断.
科学领域:
- 心血管生理学心血管生理学
- 生物医学工程 生物医学工程
- 机器学习在医学中的应用
背景情况:
- 脉动性血液动力学和压力波形分析对于诊断高血压和心力衰竭至关重要.
- 一般化转移函数 (GTF) 在捕捉中心血液动力学方面存在局限性.
- 为了改善临床评估,需要准确的中央压力波形重建.
研究的目的:
- 开发和验证混合时频,基于机器学习的传输函数,用于从外围测量中重建中心压力波形.
- 克服现有的GTF在捕捉中央脉动性血液动力学方面的局限性.
- 准确捕获基于动脉波的信息,以改善心血管评估.
主要方法:
- 利用里埃波来近似压力波形.
- 采用了一个前神经网络 (FNN),具有自定义的时间域成本函数.
- 通过培训,测试和验证混合FNN模型,使用来自Framingham心脏研究 (6698名参与者) 的数据.
主要成果:
- 与GTF (0.26-0.42) 相比,混合FNN方法在心动脉波形重建 (0.09-0.10) 中实现了显著较低的正常化平均平方误差 (NMSE).
- 与GTF (0.22-0.31) 相比,波长和振幅 (0.79-0.97) 的高相关系数得到证明.
- 显著改善了相似性,形态和基于波的参数之间的相关性.
结论:
- 混合的FNN传输函数可以从外围测量中对中心动脉压力波形进行可靠的计算.
- 这种方法准确地保存了心脏周期内的关键生理序列.
- 为改善心血管疾病的诊断和预后提供了一个有前途的工具.
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