开发个性化的多类分类模型,以检测与身体或认知工作负载相关的血压变化
Andrea Valerio1, Danilo Demarchi1, Brendan O'Flynn2
1Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究开发了一种个性化的机器学习模型,使用光电流图信号来跟踪血压. 该模型实现了高精度,证明了其用于连续血压监测的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 机器学习应用 机器学习应用
背景情况:
- 持续监测血压对于理解调节机制至关重要.
- 个性化机器学习为跟踪血压波动提供了一种新的方法.
- 摄像力发电学 (PPG) 信号包含用于生理评估的有价值数据.
研究的目的:
- 开发一个个性化的机器学习模型,用于血压跟踪.
- 根据训练集大小和个性化来评估模型的表现.
- 评估模型在不同工作负载下检测脉冲波形变化的能力.
主要方法:
- 从手臂动脉和数字动脉中提取数据驱动的光电脉冲图特征.
- 使用一个随机森林分类器训练了28个健康受试者的数据.
- 研究了不同训练集大小和个性化水平的模型行为.
主要成果:
- 总的准确率达到了95.1%,精度达到了95.2%,回忆率达到了95%,F1得分达到了95.4%.
- 当30%的脉冲波形与来自五个来源受试者的数据相结合时,观察到最优的性能.
- 在训练前阶段,可以在认知和身体压力下区分节拍对节拍脉冲波形形态.
结论:
- 一个个性化的机器学习模型可以使用PPG功能有效地跟踪血压.
- 将特定主题数据与外部数据相结合,可以提高模型的准确性和稳定性.
- 开发的系统显示了对非侵入性,持续的血压监测和工作负载评估的承诺.
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