使用身体体重-脂肪量表来测量基于边缘计算系统的无袖血压
Shing-Hong Liu1, Bo-Yan Wu1, Xin Zhu2
1Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究开发了一个边缘计算系统,用于无袖式血压测量,使用弹性心电图和阻抗复合图信号. 该系统在开发板上实现准确的实时血压估计,增强移动健康应用程序.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 精确的血压 (BP) 监测对于管理心血管疾病,糖尿病和脏疾病至关重要,特别是在老年人中.
- 无袖式BP测量技术提供了增强的用户舒适性和便利性.
- 弹性心电图 (BCG) 和阻抗整体图 (IPG) 是用于无袖血压估计的新兴生理信号.
研究的目的:
- 开发和验证一个边缘计算系统,用于实时无袖血压测量.
- 整合BCG和IPG信号采集,处理,特征提取和基于机器学习的BP估计.
- 在一个嵌入式平台 (STM32F756ZG NUCLEO) 上实现系统,用于移动健康应用.
主要方法:
- 采用定制的体重-脂肪尺度获得BCG和IPG信号.
- 信号处理包括过,细分和脉冲传输时间 (PTT) 的提取.
- 一个XGBoost机器学习模型,为边缘部署优化了超参数,使用基于校准和无校准的功能估计了BP.
主要成果:
- 边缘计算系统显示了缩血压 (SBP) 和腹缩血压 (DBP) 估计的低误差率:分别为2.2 ± 10.9 mmHg和1.87 ± 6.79 mmHg.
- 这些结果与基于服务器的计算可比,表明在设备上测量BP的可行性.
- 该系统的性能验证了其在移动医疗设备中集成的潜力.
结论:
- 边缘计算能够实时使用嵌入式系统上的BCG和IPG信号进行无袖血压测量.
- 拟议的方法增强了用于连续血压监测的体重-脂肪量表的实用性.
- 这项技术对推进移动健康和远程患者护理具有重大前景.
相关概念视频
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01:23Assessment of blood pressure in brachial artery(two-step method)
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01:28Special considerations while measuring blood pressure
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