目的研究采用更多的导联进行心律失常分析对减少ICU误报警的价值,为缓解ICU报警疲劳问题提供思路。方法使用迈瑞监护心电多导算法分别配置为单导分析、两导分析和四导分析模式,按照科室的报警设置,记录3种分析模式下ICU科室3 d 5床患者...目的研究采用更多的导联进行心律失常分析对减少ICU误报警的价值,为缓解ICU报警疲劳问题提供思路。方法使用迈瑞监护心电多导算法分别配置为单导分析、两导分析和四导分析模式,按照科室的报警设置,记录3种分析模式下ICU科室3 d 5床患者的报警,然后人工确认所有报警的正误。结果采用单导联(II导)分析产生的心律失常误报警比例达到36.6%,两导联(II/V导)分析所有心律失常错误报警率达到26.5%,而采用四导联(I/II/III/V导)分析心律失常错误报警率只有3.4%。结论采用四导联进行心律失常分析相比目前临床上常用的单导联和两导联分析,显著减少了误报警数量,有助于缓解ICU科室报警疲劳问题。展开更多
Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on s...Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.展开更多
文摘目的研究采用更多的导联进行心律失常分析对减少ICU误报警的价值,为缓解ICU报警疲劳问题提供思路。方法使用迈瑞监护心电多导算法分别配置为单导分析、两导分析和四导分析模式,按照科室的报警设置,记录3种分析模式下ICU科室3 d 5床患者的报警,然后人工确认所有报警的正误。结果采用单导联(II导)分析产生的心律失常误报警比例达到36.6%,两导联(II/V导)分析所有心律失常错误报警率达到26.5%,而采用四导联(I/II/III/V导)分析心律失常错误报警率只有3.4%。结论采用四导联进行心律失常分析相比目前临床上常用的单导联和两导联分析,显著减少了误报警数量,有助于缓解ICU科室报警疲劳问题。
基金National Natural Foundation of China (No.60421002, No.70471052)
文摘Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.
