数据知识共同驱动的基于特征的预测模型,通过光聚体素学来评估血压
Qingfeng Tang1, Chao Tao2, Xin Li3
1Digital and Intelligent Health Research Center, Anqing Normal University, Anqing 246133, China; School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Computers in biology and medicine
|August 31, 2024
概括
来自光电血压学 (PPG) 的新数据知识联合驱动功能 (DKCF) 显示出强大的血压预测准确性. 这种方法为非侵入性健康评估和监测提供了一个有希望的方法.
科学领域:
- 生物医学工程 生物医学工程
- 生理信号处理 生理信号处理
- 机器学习用于医疗保健
背景情况:
- 摄影脉冲图 (PPG) 的知识特征 (KF) 对于血压预测至关重要.
- 现有的KF经常忽视PPG的全球特征,限制了预测准确度.
研究的目的:
- 开发一种新的特征提取方法,用于使用PPG增强血压预测.
- 评估数据知识共同驱动特征 (DKCF) 与传统方法的有效性.
主要方法:
- 使用功能数据分析 (FDA) 来提取数据特征 (DF).
- 通过将FDA与KF约束相结合,提出了DKCF.
- 随机森林,AdaBoost,梯度提升,SVM和DNN模型被用于比较.
主要成果:
- 在数据集1中,DKCF实现了缩血压 (SBP) 和腹缩血压 (DBP) 的最低平均绝对误差 (MAE).
- 在数据集2中,DKCF证明了SBP预测的最小MAE和DBP的第二小MAE.
结论:
- 来自PPG的低维DKCF与血压有显著的相关性.
- DKCF为全面的健康评估提供了一个潜在的非侵入性指标.
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