通过堆叠可变形卷积网络从雷达信号进行非接触血压估计
这项研究介绍了一种使用雷达和深度学习的无接触血压监测系统. 基于雷达的堆叠可变形卷积网络 (RSD-Net) 准确地估计了来自胸部运动的连续血压.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 传统的血压监测是侵入性的,可能会让人感到不舒服.
- 需要非接触式方法来进行连续和方便的生理监测.
- 雷达技术提供了一种非侵入性的方式来捕获微妙的生理信号.
研究的目的:
- 开发和验证用于连续血压估计的非接触式方法.
- 利用深度学习来增强从雷达信号中提取特征.
- 评估用于血压监测的新型深度学习架构的性能.
主要方法:
- 使用24 GHz连续波雷达系统收集胸部微变数据.
- 预处理的雷达信号使用卡尔曼过,多级波段过器和周期性提取.
- 开发了基于雷达的堆叠可变形卷积网络 (RSD-Net),用于特征提取和血压估计的注意力机制.
主要成果:
- 获得了高准确性,皮尔森相关系数为0.838的静缩血压和0.797的静缩血压.
- 报告的平均误差 (ME) 和标准偏差 (SD) 较低: -0.32 ±6.14 mmHg (心缩) 和 -0.20 ±5.50 mmHg (腹缩).
- 废弃研究证实了RSD-Net架构中的单个组件的重要性.
结论:
- 拟议的无接触式基于雷达的系统与RSD-Net有效估计连续血压.
- 该方法在各种生理条件 (休息,瓦萨尔瓦,呼吸暂停,倾斜桌子) 中表现出强度.
- 这项技术对非侵入性,长期的血压监测具有前景.
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Preparation of Equipment:
Equipments Used To Measure Blood Pressure
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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Monitoring Both Arms:
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Assessment of blood pressure in brachial artery(two-step method)
Assessment of blood pressure in brachial artery(one-step method)
Prepare for the Procedure:
