MEMS指尖应变斑膜学用于无袖估计血压
IEEE journal of biomedical and health informatics
|March 5, 2024
概括
这项研究引入了一种使用指尖紧张斑膜学 (SPG) 的无袖血压 (BP) 估计方法. 这种新的技术显示了对不同个体的准确和强大的血压监测的承诺.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 可穿戴技术可穿戴技术
背景情况:
- 传统的血压 (BP) 测量依赖于基于袖子的设备,这可能是繁和不舒服的.
- 人们越来越需要使用非侵入性,无袖式的方法来持续监测血压.
- 通常用于可穿戴设备的光学传感器,在肤色较深的个体中,在准确性方面面临挑战.
研究的目的:
- 开发和验证一种无袖式方法,用于使用指尖应变密集图 (SPG) 估计血压 (BP).
- 探索机器学习,特别是XGboost在将SPG信号与BP参数相关联方面的有效性.
- 引入和评估用于增强SPG信号分析的新型"几何特征".
主要方法:
- 使用定制微电机械系统 (MEMS) 应变传感器来捕获指尖SPG.
- 训练了一个XGboost回归器,使用来自SPG的特征,包括心脏时间间隔,振幅,统计属性,人口统计和新的几何特征.
- 通过使用32名健康受试者进行5倍交叉验证 (5-CV) 和离开受试者交叉验证 (LSOCV) 来验证模型.
主要成果:
- 在5-CV.中,缩血压 (SBP) 的平均绝对误差 (MAE) 为5.45 mmHg,缩血压 (DBP) 的平均误差为3.88 mmHg.
- 在LSOCV中,SBP的MAE为16.81mmHg,DBP的MAE为8.16mmHg.
- 在不同的血压水平和体重指数 (BMI) 范围内表现出强度,MAE分别在[1.28,4.28] mmHg和[2.64,7.52] mmHg范围内.
结论:
- 指尖SPG显示出作为准确BP估计的无袖方法的巨大潜力.
- 开发的功能集,包括新的几何特征,有效地捕捉了SPG动态,用于BP预测.
- 这种方法为光学传感器提供了一个有前途的替代方案,特别是在改善不同肤色的可访问性方面.
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