使用序贯蒙特卡罗方法估计脉压变异性。

Pulse pressure variation estimation using a sequential Monte Carlo method.

作者信息

Kim Sunghan, Aboy Mateo, McNames James

机构信息

Biomedical Signal Processing Laboratory at Portland State University, Oregon, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5713-6. doi: 10.1109/IEMBS.2009.5332663.

DOI:10.1109/IEMBS.2009.5332663

PMID:19963913

Abstract

We describe a novel automatic algorithm to continuously estimate the pulse pressure variation (PPV) index from arterial blood pressure (ABP) signals. The algorithm utilizes our recently developed sequential Monte Carlo method (SMCM) based on a maximum A-Posterior adaptive marginalized particle filter (MAM-PF). The PPV index is one of most specific and sensitive dynamic indicators of fluid responsiveness in mechanically ventilated patients. We report the assessment results of the proposed algorithm on real ABP signals.

摘要

我们描述了一种新颖的自动算法，用于从动脉血压（ABP）信号中持续估计脉压变异（PPV）指数。该算法利用了我们最近基于最大后验自适应边缘化粒子滤波器（MAM-PF）开发的序贯蒙特卡罗方法（SMCM）。PPV指数是机械通气患者液体反应性最具特异性和敏感性的动态指标之一。我们报告了该算法对真实ABP信号的评估结果。