通过机器学习进行非侵入性血压传感.
Filippo Attivissimo1, Vito Ivano D'Alessandro1, Luisa De Palma1
1Department of Electrical and Information Engineering, Polytechnic University of Bari, 70125 Bari, Italy.
这项研究开发了机器学习模型,以非侵入性地使用光聚缩学信号来估计血压. 极端梯度提升模型实现了高精度,符合血压监测的医疗标准.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 非侵入性血压监测对于远程医疗至关重要.
- 光电显微镜 (PPG) 信号为估计血压提供了一个潜在的非侵入性来源.
- 现有的方法需要进一步改进,以提高临床准确性和远程医疗应用.
研究的目的:
- 开发和验证用于使用PPG信号的非侵入性血压估计的机器学习模型.
- 评估极端梯度提升 (XGBoost) 和神经网络 (NN) 模型的性能.
- 确保开发的模型符合既定的医疗器械标准.
主要方法:
- 从PPG信号中提取了使用最大重叠离散波形变换 (MODWT) 的新特性.
- 使用最小冗余最大相关性 (MRMR) 算法选择的最佳特征.
- 训练并比较了XGBoost和NN回归模型,用于估计静缩血压 (SBP) 和静缩血压 (DBP).
主要成果:
- 对于SBP和DBP估计,XGBoost模型表现出比NN模型更高的准确性.
- 获得了SBP的5.67 mmHg和DBP的3.95 mmHg的根平均平方误差 (RMSE).
- XGBoost模型的SBP估计性能超过了现有的文献基准.
结论:
- 开发的XGBoost回归模型通过PPG信号准确地估计血压.
- 该模型符合医学仪器进步协会 (AAMI) 和英国高血压协会 (BHS) A级标准的严格要求.
- 这种方法适合整合到远程医疗保健监测系统中.
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