GHC-net:一种基于格拉米角场的混合CNN,用于使用PPG信号进行无袖式血压分类
Yan Cheng1, Tianju Li2, Zhichao Zhang3
1College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China; Zhuhai Fudan Innovation Institute, Zhuhai 519031, China.
Computers in biology and medicine
|March 15, 2026
概括
这项研究引入了一种使用格拉米安角场 (GAF) 来分类血压 (BP) 的新方法. 这种方法实现了高精度,为基于家庭的心血管疾病监测提供了潜力.
科学领域:
- 生物医学工程 生物医学工程
- 心血管疾病研究研究
- 信号处理 信号处理
背景情况:
- 无袖式血压 (BP) 监测使用光电脉冲图 (PPG) 对于预防心血管疾病至关重要.
- 传统的时间特征提取方法在捕获复杂的PPG信号动态方面存在局限性.
研究的目的:
- 为无袖BP分类提出一种新的编码策略.
- 通过先进的深度学习技术,提高BP监测的准确性和效率.
主要方法:
- 使用格拉米安角场 (GAF) 编码将1D PPG信号转换为2D矩阵.
- 开发了一个深度神经网络,GHC-Net,结合了混合扩展卷积 (HDC) 和剩余有效通道注意力 (RECA) 模块.
- 使用GAF编码的PPG数据进行三重血压分类 (高血压,高血压,高血压).
主要成果:
- 在公开的PPG-BP数据集上获得了76.39%的准确性和76.68%的F1分数.
- 在私人数据集上获得了92.66%的准确性和93.33%的F1评分.
- 证明了该模型能够捕捉多个尺度的时空相关性和跨道依赖性的能力.
结论:
- 拟议的GAF编码和GHC-Net提供了一种高效准确的自动化BP分类方法.
- 这种技术显示出在早期高血压检测和监测中家庭护理应用的巨大潜力.
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