BP-Net: 监测血压的"变化"使用PPG与自我对比的掩护
IEEE journal of biomedical and health informatics
|July 2, 2024
概括
这项研究介绍了BP-Net,这是一种新型的深度学习模型,可以从光聚体图 (PPG) 信号中检测系统性血压 (SBP) 的变化. 在识别显著的SBP波动方面,BP-Net显示了更好的概括性,并超过了现有的方法.
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
- 生理信号处理 生理信号处理
背景情况:
- 从光电血压图 (PPG) 信号估计血压 (BP) 的深度学习模型面临着概括性挑战.
- 持续监测急性血压变化对于临床应用,如高血压紧急情况至关重要.
研究的目的:
- 开发一种新的深度学习方法,使用PPG信号检测随着时间的推移缩血压 (SBP) 的显著变化.
- 为了解决现有的基于PPG的BP估计模型的概括性限制.
主要方法:
- 提出了一种自我对比掩盖 (SCM) 模型,用于PPG信号中的对间时间比较.
- 引入了BP-Net,这是一个训练有素的模型,可以使用PPG检测超过值的SBP升高或下降.
- 评估了BP-Net在未见的对象和不同的数据集上,并将其与现有的基于PPG的BP估计方法进行比较.
主要成果:
- 在未见的受试者中,BP-Net在检测超过值的SBP变化方面取得了75.97%的准确性.
- 该模型在不同数据集中展示了改进的概括性.
- 在检测显著SBP波动方面,BP-Net的表现优于现有的基于PPG的模型.
结论:
- 将BP估计作为检测"变化"而不是绝对值的框架增强了模型的概括性.
- BP-Net提供了一种有前途的新方法,用于使用PPG信号进行临床BP监测.
- 这项工作推进了PPG的使用,以可靠和可通用的BP变化检测.
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