title: Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.
abstract: BACKGROUND
  Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems.        Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia.
  
  
  METHODS
  A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems.        The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated.
  
  
  RESULTS
  Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum        of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min.
  
  
  CONCLUSIONS
  The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance.
authors: Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, Illinois 60616, USA.
authors: Elif Seyma
authors: ES
authors: Bayrak
authors: Kamuran
authors: K
authors: Turksoy
authors: Ali
authors: A
authors: Cinar
authors: Lauretta
authors: L
authors: Quinn
authors: Elizabeth
authors: E
authors: Littlejohn
authors: Derrick
authors: D
authors: Rollins
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis; D007332:Insulin Infusion Systems; D016018:Least-Squares Analysis; D008954:Models, Biological; D019397:Pancreas, Artificial; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose