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一种用于计算心血管内在频率的机器学习方法
Rashid Alavi1, Qian Wang2, Hossein Gorji3
1Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, United States of America.
PloS one
|October 26, 2023
概括
一种新的机器学习 (ML) 方法使用Feedforward神经网络 (FNN) 来高效地计算来自带波形的内在频率 (IF) 参数. 这种方法克服了计算瓶,实现了实时心血管分析.
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
- 机器学习应用 机器学习应用
背景情况:
- 心血管波形分析提供了重要的健康见解.
- 内在频率 (IF) 方法从动脉压力波形中提取生理数据.
- 目前的IF计算方法面临着计算复杂性,限制实时使用.
研究的目的:
- 开发一种基于机器学习 (ML) 的方法来从单个带波形中确定内在频率 (IF) 参数.
- 为了克服IF计算的传统L2优化解决方案的计算局限性.
- 为了实现IF方法在临床环境中的实时应用.
主要方法:
- 一个顺序减少的前神经网络 (FNN) 模型被用来将带波形映射到IF参数中.
- 该方法包括数据预处理,模型培训和严格的模型评估,使用临床和合成数据.
- 临床数据来源于亨廷顿医学研究院 (HMRI) 的iPhone心脏研究和弗雷明汉心脏研究 (FHS).
主要成果:
- 基于FNN的IF方法与标准L2优化方法 (R≥0.93,P≤0.005) 显示出非常强的相关性.
- 模型性能独立于测量装置和设备的采样率.
- ML方法成功地避免了传统的IF计算中固有的非凸L2最小化问题.
结论:
- 拟议的基于ML的方法提供了一个高效和准确的替代方法,用于从带波形计算IF参数.
- 这种方法促进了IF方法用于心血管分析的实际实时部署.
- 基于FNN的IF模型显示了不同测量设备和采样率的稳定性.
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