基于深度学习的非接触式IPPG信号血压测量研究研究
Hanquan Cheng1, Jiping Xiong1, Zehui Chen1
1College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321000, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种新型的深度学习模型,用于使用成像光电缩学 (IPPG) 信号进行准确的,非接触式血压监测. 开发的系统和模型实现了高准确度,符合可靠健康评估的国际标准.
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
- 心血管监测 心血管监测
背景情况:
- 准确的血压监测对于心血管健康管理至关重要.
- 传统的方法可能是侵入性的或不方便的.
- 采用光电解剖学的非接触方法有希望,但需要强大的信号处理和准确的估计模型.
研究的目的:
- 开发一种非接触式的,基于摄像机的系统,用于获取成像光电显微镜 (IPPG) 信号.
- 构建IPPG和血压数据的第一个开源数据集 (IPPG-BP).
- 提出一个多阶段的深度学习模型,从IPPG信号中准确估计血压.
主要方法:
- 设计了一个基于摄像头的系统,用于在环境光下非接触式IPPG信号采集.
- 创建了IPPG-BP数据集,将IPPG信号与血压测量相结合.
- 开发了一种使用卷积神经网络 (CNN) 和双向门式循环网络 (Bi-GRU) 的多阶段深度学习模型.
主要成果:
- 拟议的多阶段深度学习模型实现了符合BHS和AAMI国际标准的血压估计结果.
- 该系统简化了非接触式脉冲波信号采集,并降低了成本.
- 该模型自动提取特征,并集成波形形态,以提高准确性.
结论:
- 开发的系统和深度学习模型为人类血压监测提供了准确和方便的方法.
- IPPG-BP数据集有助于进一步研究非接触式心血管监测.
- 这种方法减少了手工工作量,并提高了血压估计的精度,与现有的技术相比.
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