在运动期间用无袖的血压计估计血压.
Kenta Hayashi1, Yuka Maeda1, Takumi Yoshimura2
1Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一种无袖血压 (BP) 监测方法,该方法使用机器学习和光斑血图 (PPG) 信号. 这种新的方法可以达到与传统的袖口设备相提并论的准确性,即使是在运动期间.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 医疗保健中的机器学习
背景情况:
- 精确的血压 (BP) 测量对健康至关重要,通常使用袖子式设备.
- 现有的无袖BP监测器在准确性和长期可靠性方面面临挑战.
- 机器学习提供了从生理信号改善无袖血压估计的潜力.
研究的目的:
- 开发和验证在体力活动期间估计血压 (BP) 的无袖方法.
- 为了利用机器学习和光电脉冲图 (PPG) 信号进行准确的,非侵入性的血压监测.
- 根据BP测量设备的监管标准评估拟议方法的性能.
主要方法:
- 采用光电血图 (PPG) 信号来估计血压 (BP).
- 应用信号质量指数 (skewness) 和特征选择算法 (RReliefF) 进行可靠的信号处理.
- 使用长期短期记忆 (LSTM) 神经网络进行BP预测.
- 在休息,运动和恢复阶段对17名参与者进行了实验.
主要成果:
- 拟议的无袖方法在BP估计中实现了0.32 ± 7.76 mmHg的平均误差.
- 开发的方法的准确性满足了基于袖口的血压计所需的标准.
- 在20分钟的运动方案中证明了可靠的血压估计.
结论:
- 开发的基于LSTM的无袖血压监测系统提供了与传统方法相比精确的测量.
- 这项技术有可能显著改善患者的舒适度和医疗保健结果.
- 拟议的方法适用于各种监测环境,包括临床,家庭和体育环境.
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