降低重症监护在线监测系统的假警报：两种信号提取算法的评估。
Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms.
机构信息
Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany.
出版信息
Comput Math Methods Med. 2011;2011:143480. doi: 10.1155/2011/143480. Epub 2011 Feb 27.
Abstract
Online-monitoring systems in intensive care are affected by a high rate of false threshold alarms. These are caused by irrelevant noise and outliers in the measured time series data. The high false alarm rates can be lowered by separating relevant signals from noise and outliers online, in such a way that signal estimations, instead of raw measurements, are compared to the alarm limits. This paper presents a clinical validation study for two recently developed online signal filters. The filters are based on robust repeated median regression in moving windows of varying width. Validation is done offline using a large annotated reference database. The performance criteria are sensitivity and the proportion of false alarms suppressed by the signal filters.
摘要
在线监测系统在重症监护病房受到高比例的假阈值警报的影响。这些是由无关的噪声和异常值在测量时间序列数据中引起的。通过在线将相关信号与噪声和异常值分离，可以降低高误报率，从而使信号估计值而不是原始测量值与报警限制进行比较。本文介绍了最近开发的两种在线信号滤波器的临床验证研究。这些滤波器基于在变化宽度的移动窗口中进行稳健的重复中位数回归。验证是使用大型带注释的参考数据库离线进行的。性能标准是灵敏度和信号滤波器抑制的假警报比例。
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