Annals of Data Science

, Volume 5, Issue 2, pp 293–299 | Cite as

Cardiopulmonary Function Monitoring Based on MEWMA Control Chart

  • Hongxia Zhang
  • Liu Liu
  • Jin Yue
  • Xin Lai


According to the characteristics of parameters of cardiopulmonary function diversity and change slowly in pathology, we apply the multivariate exponentially weighted moving average (MEWMA) control chart to monitor the state of lungs. This paper aimed at five indicators of cardiopulmonary function, using principal component test to diagnose whether it is from the multivariate normal distribution, Clearing the relationship model of control line and weight coefficient of MEWMA control graph, and drawing the control diagram for monitoring. The process stay in control state before 103 observations, however, beyond the control limit from the 104 observation statistics and give an alarm. This means that there is a problem with the cardiopulmonary starting on the 103rd sample. Control chart has a good warning function because it can raise the alarm before cardiopulmonary function has a big problem. Using MEWMA control chart for monitoring can reduce the cost of medical examination and frequency, it can improve the hospital resource utilization rate and confirm the case. Thus we can avoid missing the best treatment time.


Cardiopulmonary function monitoring MEWMA Principal component test Simulation method 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sichuan Normal UniversityChengduChina
  2. 2.Xi’an Jiao Tong UniversityXi’anChina

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