基于深度学习的血压估计,使用多通道光电脉冲图和指压与注意力机制
Jehyun Kyung1, Joon-Young Yang1, Jeong-Hwan Choi1
1Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
Scientific reports
|June 8, 2023
概括
这项研究引入了一种新型的无袖血压 (BP) 估计系统,使用多通道光电血压图 (PPG) 信号和渐进式指压. 该系统通过使用深度学习注意力机制来选择最佳的PPG通道来提高准确性,减少与位置相关的错误.
科学领域:
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械
- 信号处理 信号处理
背景情况:
- 无袖式血压 (BP) 监测使用光电心电图 (PPG) 信号是一个活跃的研究领域.
- 由于手指位置和PPG信号质量的变化,现有的方法经常存在错误.
- 无袖振荡计方法需要强大的信号采集,以确保准确的BP估计.
研究的目的:
- 开发一种新的无袖血压估计系统.
- 为了提高血压测量的稳定性,防止手指定位造成的错误.
- 为了提高准确性,利用多通道PPG信号和深度学习.
主要方法:
- 开发了一种传感器,可以同时捕获多通道PPG和强力信号在广的视野中.
- 在PPG信号采集过程中,应用了渐进式指纹压力.
- 建议使用带有注意力机制的深度学习算法来选择最佳的PPG通道来估计BP.
主要成果:
- 拟议的系统显示了0.43±9.35 mmHg的静脉血压 (SBP) 和0.21 ±7.72 mmHg的静脉血压 (DBP) 的低误差率.
- 实验显示了基于PPG测量位置的显著性能差异.
- 多通道方法与注意力机制改善了对指头位置错误的稳定性.
结论:
- 开发的多通道PPG传感器和深度学习算法为准确和强大的无袖血压监测提供了有前途的方法.
- 注意力机制有效地识别最佳的PPG通道,减轻位置依赖的错误.
- 进一步研究最佳的传感器放置和信号处理是必要的临床翻译.
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