使用独立组件和多分辨率分析估计自由移动用户的呼吸速率
Karla M Reyes Leiva1, Matous Vondal1, Miao Yang2
1Faculty of electrical engineering and computer science, VSB Technical University of Ostrava, Czech Republic.
Computers in biology and medicine
|August 27, 2025
概括
这项研究提出了一种新的基于雷达的方法,使用独立组件分析 (ICA) 和实证波形转换 (EWT) 来准确测量呼吸速率 (RR),即使在自由运动中. 这种新的框架有效地提取生命信号, 在现实世界中表现出高度准确性.
科学领域:
- 生物医学工程
- 信号处理
- 健康监测技术
背景情况:
- 非侵入性健康监测对于持续,舒适的生命体征跟踪至关重要.
- 无接触雷达的重要信号传感面临着噪音信号和主体运动的挑战.
- 在自由移动条件下精确地从雷达信号中提取生理数据仍然是一个重大障碍.
研究的目的:
- 通过雷达信号引入和验证一个新的呼吸率估计框架.
- 在自由移动的对象中解决准确的生命信号提取的挑战.
- 在现实,动态环境中提高呼吸率估计的准确性.
主要方法:
- 开发了一种结合独立组件分析 (ICA) 和实证波形转换 (EWT) 的新框架.
- 使用ICA自动从雷达信号中选择生理上相关的组件.
- EWT 作为适应性多分辨率分析 (MRA) 用于信号分解和重建以增强峰值检测.
主要成果:
- 拟议的ICA-EWT框架证明了在自由运动条件下估计呼吸速率 (RR) 的有效性.
- 皮尔森相关系数显示估计和参考RR之间存在很强的相关性 (r=0.94).
- 布兰德-阿尔特曼分析显示,平均误差为每分钟0.41次.
结论:
- 在现实环境中,ICA-EWT框架对准确,非侵入性呼吸速率估计具有显著的前景.
- 这种方法即使在被试自由移动时也有效, 克服了当前基于雷达的系统的主要局限性.
- 未来的改进可能包括优化雷达位置和减轻信号干扰以进一步提高准确性.
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