使用基于ResNet-BiGRU的单一光电显微信号估计血压
Gongwei Fan1, Yuhao Pang1, Wei Zheng1
112676 College of Ocean, Jiangsu University of Science and Technology , Zhenjiang City, Jiangsu Province, China.
Biomedizinische Technik. Biomedical engineering
|February 20, 2026
概括
一个新的深度学习模型,ResNet-BiGRU,只使用光电脉冲图 (PPG) 信号,准确估计血压 (BP). 这种非侵入性方法显示了可穿戴设备中持续血压监测的前景.
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
- 心血管健康 心血管健康
背景情况:
- 传统的血压 (BP) 监测依赖于间歇性的袖子测量.
- 摄影透视图 (PPG) 为BP估计提供了一个非侵入性的,连续的替代方案.
- 开发准确的,无袖的血压监测对于积极的健康管理至关重要.
研究的目的:
- 开发一个深度学习框架,使用单个PPG信号精确,无袖的BP估计.
- 利用先进的神经网络架构来改善BP监控.
- 在独立的数据集上验证模型的性能.
主要方法:
- 设计了一种混合深度神经网络 (ResNet-BiGRU),集成剩余卷积块和双向封闭循环单元.
- 该模型在包括942名受试者在内的UCI手式血压估计数据集 (MIMIC-II子集) 上进行了训练和验证.
- 数据预处理涉及带宽过 (0.5-8 Hz) 和细分为5秒窗口,与80/20列车验证分割.
主要成果:
- 在 ResNet-BiGRU 模型中,在 UCI 数据集中,实现了低平均绝对误差 (MAE):缩血压 (SBP) 4.78 mmHg,腹缩血压 (DBP) 2.98 mmHg.
- 对VitalDB数据集的外部验证表明,SBP的MAE为8.15 mmHg,DBP的MAE为4.59 mmHg,表现强.
- 这些结果表明无袖式BP估计的高准确度和可靠性.
结论:
- 该ResNet-BiGRU模型提供了准确的,强大的,和可概括的无袖式BP估计.
- 这种深度学习方法显示了将其集成到可穿戴健康监测系统中的巨大潜力.
- 持续的,非侵入性的血压监测可以提高心血管健康的管理.
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