Advertisement

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
Article
  • 36 Downloads

Abstract

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.

Keywords

Cardiopulmonary function monitoring MEWMA Principal component test Simulation method 

References

  1. 1.
    Zhang Junxing (1992) Application of microcomputer in detecting cardiopulmonary function of human body. Microcomput Inf 3:37–40Google Scholar
  2. 2.
    Feng Huiping (2012) Application of six minute walking test in cardiopulmonary function test of patients with chronic obstructive pulmonary disease. Jilin Med J 33(17):3711–3712Google Scholar
  3. 3.
    Yuan Hang (2017) Study on the detection of heart and lung function of athletes aerobic training. Comput Simul 34(1):368–371Google Scholar
  4. 4.
    Liu Taohuan (2014) Application of principal component analysis in disease detection. J Shaoyang Univ (Nat Sci Edit) 11(1):11–15Google Scholar
  5. 5.
    Lucas James M, Saccucci Michael S (1990) Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 32(1):1–12CrossRefGoogle Scholar
  6. 6.
    Lowry Cynthia A, Woodall William H, Champ Charles W et al (1992) A multivariate exponentially weighted moving average control chart. Technometrics 1(34):46–53CrossRefGoogle Scholar
  7. 7.
    Zeng Chao, Liu Jian, Li Rong et al (2014) Body assembly quality control based on MEWMA control chart. China Mech Eng 25(5):692–697Google Scholar
  8. 8.
    Fuzhou Du, Tang Xiaoqing, Sun Jing (2006) ARL computation and parameters optimization for MEWMA control chart based on the Markov chain. J Beijing Univ Aeronaut Astronaut 32(8):974–978Google Scholar
  9. 9.
    Bo Shijun (2009) Fitness sports to enhance the human heart and lung function. Fight Sports Forum 1(5):66–67Google Scholar
  10. 10.
    Gao Huixuan (2005) Multivariate statistical analysis. Beijing University Press, Beijing, pp 14–15Google Scholar
  11. 11.
    Lai Xin, Yau Kelvin, Liu Liu (2017) Competing risk model with bivariate random effects for clustered survival data. Comput Stat Data Anal 112:215–223CrossRefGoogle Scholar
  12. 12.
    Liu Liu, Xuemin Zi, Jian Zhang (2015) Nonparametric adaptive CUSUM procedures with Markovian mean estimation. Appl Stat Manag 34(3):463–475 (Chinese with English abstract)CrossRefGoogle Scholar
  13. 13.
    Zhou Maoyuan, Liu Liu, Geng Wei, Zhou Jie (2015) Multivariate control chart based on multivariate smirnov test. Commun Stat Simul Comput 44(6):1600–1611CrossRefGoogle Scholar

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

Personalised recommendations