不确定性量化无袖血压与深度证据回归模型的不确定性量化
概括
这项研究引入了一种新的无袖血压估计方法,使用深度证据回归和合规预测来提供可靠的不确定性间隔以进行准确的测量.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 无袖式血压 (BP) 估计由于固有的不确定性而面临准确性和可靠性的挑战.
- 现有的模型难以满足精确血压测量的临床要求.
研究的目的:
- 开发一种用于量化无袖BP估计不确定性的新方法.
- 为可靠的血压测量生成统计严格的不确定性间隔 (UI).
主要方法:
- 开发了一种深度证据回归 (DER) 模型,使用光多图 (PPG) 信号来估计血压.
- 经验不确定性与符合性预测相结合,创建了BP估计的UI.
- 该方法在使用连续Finpres BP作为参考对37名受试者进行了验证.
主要成果:
- 拟议的方法实现了缩血压 (SBP) 的平均绝对差异为5.56 mmHg,缩血压 (DBP) 的平均绝对差异为3.18 mmHg.
- 生成的UI显示了高覆盖率,采集了94.8%的参考SBP和95.9%的DBP.
结论:
- 这种新方法有效量化了无袖BP估计的不确定性.
- 这种方法显示了使更可靠,更准确的无袖BP监测成为可能的巨大潜力.
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