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Early Warning of Health Condition and Visual Analytics for Multivariable Vital Signs

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
  • Wai Lok Woo
  • BHD Koh
  • Bin Gao
  • EO Nwoye
  • Bo Wei
  • SS Dlay
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Publication date24/04/2020
Host publicationProceedings of the 2020 International Conference on Computing, Networks and Internet of Things
Pages206-211
Number of pages6
ISBN (Electronic)9781450377713
<mark>Original language</mark>English

Publication series

NameACM International Conference Proceeding Series

Abstract

This study develops a multivariable data analysis technique for subjects who are monitored for four vital signs, namely heart rate, respiration rate, blood pressure and body temperature, and proposes a method to provide early warning of abnormal health condition and visual analytics that identifies the contributing factor that causes the early warning. It proposes the use of the deranged values of the vital signs of a collection of subjects to fix a multivariable PCA model and set the control level based on the Hotelling T2 statistics. The test subject is monitored using the model and the control level. The values are deemed abnormal when the multivariable observation exceeds the control limit by two consecutive observations, and an early warning is issued when there are multiple of such detections. The study shows that there is statistical significance between the distribution of the number of detections in subjects who are well enough to be discharged from ICU and those whose conditions are so bad that they did not survive the stay in the ICU.