一个双分支框架,用于估计血压使用光电解剖学信号与深度学习和临床先前的生理知识的血压估计
Minghong Qiao1, Li Chang2, Zili Zhou3
1College of Biomedical Engineering, Sichuan University, Chengdu, People's Republic of China.
Physiological measurement
|January 24, 2025
概括
这项研究引入了一种新的双分支人工智能框架,用于准确的无袖血压估计,使用光聚缩学信号,在一天的不同时间显示高精度.
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
- 心血管监测 心血管监测
背景情况:
- 精确的血压 (BP) 监测对于心血管健康管理至关重要.
- 目前基于袖子的方法具有侵入性,并且限制了持续监测.
- 摄影透视图 (PPG) 为BP估计提供了一个非侵入性的替代方案.
研究的目的:
- 开发和验证一种新的双分支深度学习框架,用于使用PPG信号精确,无袖式血压估计.
- 为了结合临床的先前知识和模型血压的日间变化.
- 为了实现系统血压 (SBP) 和透气血压 (DBP) 的准确估计.
主要方法:
- 设计了一个双分支框架,利用来自MobileViTv2和Vgg19骨干的深度PPG功能以及多维波形参数.
- 两个分支的特征被合并,考虑日间BP变化,并为特定时期的SBP/DBP模型使用AutoML.
- 该模型在HRSD数据集上进行了训练,并在MIMIC-IV数据集上进行了验证.
主要成果:
- 在HRSD数据集中,在晚上达到的平均绝对误差 (MAE) 低至2.65 mmHg (SBP) 和2.56 mmHg (DBP).
- 在MIMIC-IV上表现出强大的泛化性能,MAE为4.34 mmHg (SBP) 和3.11 mmHg (DBP).
- 符合医学仪器仪表协会的标准,并达到英国高血压协会标准的A级.
结论:
- 拟议的框架提供了一个准确可靠的非侵入性血压监测技术.
- 这种方法适用于持续的健康监测和心血管疾病预防.
- 深度学习和临床知识的整合提高了无袖血压估计的准确性.
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