通过毫米波雷达和PPG信号的多模式集成进行无袖连续血压估计
概括
这项研究引入了一种新的无袖血压监测系统,使用毫米波雷达和手指. 混合机器学习方法实现了精确的血压测量,为传统方法提供了舒适的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 心血管监测 心血管监测
- 信号处理 信号处理
背景情况:
- 血压是心血管健康的关键指标.
- 传统的血压测量方法往往不舒服,不适合持续监测.
- 需要准确,非侵入性和持续的血压监测解决方案.
研究的目的:
- 开发和验证使用毫米波雷达的无袖血压测量技术.
- 评估混合机器学习模型用于血压估计的准确性.
- 为了提供一个舒适和连续的血压监测替代方案.
主要方法:
- 使用 24 GHz 毫米波雷达系统,从手指片中获取光聚光学 (PPG) 信号.
- 采用变化模式分解 (VMD) 和有限脉冲响应 (FIR) 过器,从雷达回声中重建心跳波形.
- 开发了一种混合机器学习模型,将长短期记忆 (LSTM) 和支持向量回归 (SVR) 结合起来,用于估计血压,与PPG信号同步.
主要成果:
- 毫米波雷达和指组合显示了精确的血压测量.
- 重建的心跳波形准确地反映了生理变化.
- 与标准方法相比,混合LSTM-SVR模型在估计血压方面显示了可接受的准确性.
结论:
- 开发的基于毫米波雷达的系统为无袖连续血压监测提供了一个有前途的方法.
- 这种混合方法为传统血压测量设备提供了准确而舒适的替代方案.
- 这些发现强调了雷达技术在非侵入性心血管监测中的临床相关性.
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