监护信息重构方法研究  

Study on Reconstruction of Monitoring Information

作　　者：邹焱飚[1] 林兆花[1] 

机构地区：[1]华南理工大学机械工程学院,广州510640

出　　处：《系统仿真学报》2008年第6期1636-1638,共3页Journal of System Simulation

基　　金：粤港关键领域重点突破项目(20054982304);广东省科技攻关项目(2004B10201010);华南理工大学自然科学基金(B01-E5050810)

摘　　要：监护信息系统中通常采用系统建模的方法对监护数据进行分析处理、并识别异常状况。通常这些模型是在模型结构确定的条件下,应用监护数据辨识获得。实际检测获得的监护数据通常包含大量异常值,这会严重降低模型辨识的准确性。提出了一种监护信息系统中异常值分析处理方法。应用Hampel辨识器算法,识别监护数据中异常值出现的位置;并采用kalman滤波器算法的方法对于出现异常值的数据点进行数据重构,实现监护信息系统中出现异常值分析处理。通过应用PhysioNet生物医学信号研究资源中的两组数据集,包括心率和中心静脉压,进行实验研究,结果表明此方法对监护数据异常值分析和处理中取得很好的效果。Model-based methods are used for data analysis and detection abnormal status in monitoring information system. Often, these models are obtained by fitting convenient model structures to observed data. Real measurement data records frequently contain outliers, which can badly degrade the results of an empirical model identification procedure. Analysis methods for outliers were proposed. First, the outliers were located based on Hampel identifier; and second. the methods based on kalman filter were used to compute the predictions correctly for dealing with outliers. Some real datasets including heart rate, central venous pressure, and oxygen saturation of blood from PhysioNet were used for test. The results show that the methods described are often extremely effective in practice for outliers＇ analysis for monitoring information system.

关 键 词：监护信息系统 异常点 Hampel辨识器 KALMAN滤波器 预报 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]