基于PPG的连续BP波形估计使用偏化的注意引导条件对抗学习模型
IEEE journal of biomedical and health informatics
|October 13, 2023
概括
这项研究引入了PPG2BP-cGAN,这是一种新的AI模型,使用光聚光显微镜 (PPG) 信号来准确估计血压 (BP) 波形. 该方法在连续,无袖的血压监测方面表现出卓越的性能.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心血管生理学心血管生理学
背景情况:
- 血压 (BP) 波形分析提供了重要的心血管洞察力.
- 准确的血压监测对于诊断和管理心血管疾病至关重要.
- 目前用于测量血压的方法可以是侵入性的或间歇性的.
研究的目的:
- 开发一种新的以注意力为导向的条件生成对抗网络 (cGAN) 来估计来自光电聚光学 (PPG) 信号的BP波形.
- 评估PPG2BP-cGAN模型的性能与最先进的技术相比.
- 评估模型在连续,无袖的BP波形测量方面的潜力.
主要方法:
- 一个名为PPG2BP-cGAN的条件生成对抗网络 (cGAN) 设计了基于UNet3+的生成器,结合了时空注意力和PatchGAN区分器.
- 发电机使用了全尺寸的跳过连接和修改的极化自我注意模块.
- 该模型在683名受试者的数据集上进行了预训练,并在一个独立的公共数据集上进行了验证.
主要成果:
- 与现有的最先进的方法相比,PPG2BP-cGAN模型展示了优越的BP波形预测性能.
- 在MIMIC数据集上,该模型实现了3.54的根平均平方误差 (RMSE) 和2.86.8的平均绝对误差 (MAE).
- 静心和静心血压估计误差符合AAMI标准 (静心:0.72 ± 4.34 mmHg;静心:0.41 ± 2.48 mmHg).
结论:
- 拟议的PPG2BP-cGAN方法为估计PPG信号的BP波形提供了一个高度准确和可靠的方法.
- 该模型在独立数据集上的表现凸显了其在非侵入性,持续的BP监测方面的潜力.
- 这项技术可以显著推进心血管健康监测和管理领域.
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