不稳定的心跳对心跳缩血压趋势的可靠估计使用光电显微镜语境周期
Xinyi Huang1, Xianbin Zhang1, Richard Millham2
1School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
一个新的深度学习模型准确地估计了使用光聚光学 (PPG) 信号的缩血压 (SBP) 趋势. 这种先进的方法通过捕捉复杂的血压波动来改善心血管疾病风险监测.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
- 人工智能在医学中的应用
背景情况:
- 高血压和血压变化 (BPV) 是心血管疾病 (CVD) 的重要危险因素.
- 单通道光电脉冲图 (PPG) 为日常血压 (BP) 监测提供了一种有前途的方法.
- 从PPG估计BP趋势是具有挑战性的,因为复杂的时间依赖和持续波动.
研究的目的:
- 开发一种新的深度学习模型,用于准确的节奏对节奏缩性血压 (SBP) 趋势估计.
- 解决传统方法在捕捉复杂的BP波动模式在不同的时间跨度上的局限性.
主要方法:
- 提出了一个两阶段的深度学习架构,具有新的"上下文循环"输入结构.
- 第一个阶段使用并行ResU块进行细粒度特征提取.
- 第二阶段使用具有相对位置编码 (RPE) 的变压器层来捕捉周期间的相互作用和时间依赖.
主要成果:
- 该模型的平均绝对误差 (MAE) 为3.186 mmHg,序列相关性 (Rseq) 为0.743.
- 它在稳定和突然的BP波动状态中表现强,表现优于基线模型.
- 该方法符合AAMI标准,并获得BHS级A,表明其可靠性很高.
结论:
- 拟议的深度学习模型为PPG的节奏对节奏SBP趋势估计提供了强大的解决方案.
- 这种方法显示出可靠的日常健康监测和心血管疾病风险评估的巨大潜力.
- 新的架构和输入结构有效地捕捉了复杂的BP动态.
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