基于PPG信号,开发一个改进的堆叠U-Net模型,用于基于PPG信号的无袖血压估计
Jenn-Kaie Lain1, Chung-An Wang1, Jun-Hao Xu1
1Department of Electronic EngineeringNational Yunlin University of Science and Technology Douliou 640301 Taiwan.
IEEE open journal of engineering in medicine and biology
|November 12, 2025
概括
这项研究引入了一种增强的深度学习模型,用于使用光聚体图 (PPG) 信号进行准确的无袖血压监测. 这种新的方法实现了高精度,符合严格的医疗标准,用于非侵入性测量.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 医疗信号处理 医疗信号处理
背景情况:
- 准确的血压监测对于心血管健康管理至关重要.
- 目前基于袖口的方法具有侵入性,并且不方便于持续监测.
- 开发非侵入性,无袖式血压估计技术是一个重大的医疗挑战.
研究的目的:
- 介绍一个增强的堆叠U-Net深度学习模型,用于无袖式血压估计.
- 为了提高非侵入性血压测量的准确性,仅使用光电图 (PPG) 信号.
- 通过先进的模型架构来解决系统血压估计方面的挑战.
主要方法:
- 使用了一个增强的堆叠U-Net深度学习架构.
- 集成的速度充血图输入用于改进特征提取.
- 采用添加空间和道注意力机制来改进U-Net的性能.
- 缓解了U-Net架构中的解码器不匹配.
主要成果:
- 获得的平均绝对误差为3.921 mmHg (心缩) 和2.441 mmHg (腹缩).
- 满足了英国高血压协会的A级标准,并符合AAMI准确性标准.
- 超越了现有的仅PPG的光谱时间方法.
- 证明性能与组合PPG和心电图 (ECG) 深度学习模型相提并论.
结论:
- 增强的堆叠U-Net模型为无袖血压监测提供了一个有前途的解决方案.
- 该模型展示了实用,低成本和非侵入性持续血压评估的潜力.
- 这项技术可以显著改善远程患者监测和高血压管理.
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