使用机器学习模型,通过弹性心电图和阻抗囊图估计无袖血压
Shing-Hong Liu1, Yao Sun2, Bo-Yan Wu1
1Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City, Taiwan.
Frontiers in digital health
|March 10, 2025
概括
这项研究开发了一种无袖式血压测量方法,使用体重-脂肪尺度的弹性心脏图和阻抗复合图. 该技术实现了高精度估计心压和腹压血压,为移动健康应用铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 血压 (BP) 监测对于管理高血压和心血管疾病至关重要.
- 传统的袖子式血压计与移动健康 (mHealth) 应用不兼容.
- 无袖式血压测量为连续和方便的监测提供了一个有希望的替代方案.
研究的目的:
- 开发和验证无袖血压估计方法,使用重量-脂肪尺度的弹性心脏图 (BCG) 和阻抗复合图 (IPG).
- 利用深度学习和机器学习进行准确的信号质量分类和BP参数提取.
- 评估将该技术集成到移动健康设备中的可行性.
主要方法:
- 为了BCG和IPG信号质量分类,使用了一种组合1D卷积神经网络 (1D CNN) 和门式循环单元 (GRU) 的堆叠模型.
- 脉冲传输时间 (PTT) 参数从BCG和IPG信号中提取.
- 采用随机森林 (RF) 和XGBoost模型,使用PTT参数和心率 (HR) 来估计缩性血压 (SBP) 和腹缩性血压 (DBP).
主要成果:
- 信号质量分类实现了0.989.9的准确性.
- 五次交叉验证产生了高的皮尔森相关系数:SBP的0.953 ± 0.007和DBP的0.935 ± 0.007.
- 在XGBoost模型中,平均绝对差异较小:SBP的3.54 ± 0.34 mmHg,DBP的2.57 ± 0.17 mmHg.
结论:
- 拟议的方法显著提高了无袖血压测量的准确性.
- 将其集成到体重-脂肪计量表中,为mHealth应用提供了一个不受限制的设备.
- 这项技术有可能改善远程患者监测和心血管健康的管理.
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