Avent R K, Charlton J D
Massachusetts Institute of Technology, Lincoln Laboratory, Lexington.
Crit Rev Biomed Eng. 1990;17(6):621-59.
Information gained through early detection of patient vital sign changes typically can be used to anticipate future difficulties. Detection of these changes through monitoring, however, can be difficult. Many of the monitored processes are random in nature. For that reason simple threshold algorithms exhibit a high incidence of false alarms, which can decrease the operator's confidence in the monitor. Many problems associated with threshold-based biological monitors can be alleviated by introducing statistical detection techniques. The purpose of this review is to develop and critique the major trend detection algorithms used in biological monitors. This review contains an evolution of trend detection through current state-of-the-art algorithms.
通过早期检测患者生命体征变化获得的信息通常可用于预测未来的困难。然而,通过监测来检测这些变化可能很困难。许多被监测的过程本质上是随机的。因此,简单的阈值算法会出现很高的误报率,这会降低操作人员对监测器的信心。通过引入统计检测技术,可以缓解许多与基于阈值的生物监测器相关的问题。本综述的目的是开发和评估生物监测器中使用的主要趋势检测算法。这篇综述包含了从当前最先进的算法看趋势检测的演变。