Neonatal heart rate prediction.
Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.
Abdel-Rahman
Yumna
Y
School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada. abdelryh@mcmaster.ca
Jeremic
Aleksander
A
Tan
Kenneth
K
D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D008991:Monitoring, Physiologic / Q000379:methods*; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted