title: Professional continuous glucose monitoring in subjects with type 1 diabetes: retrospective hypoglycemia detection.
abstract: BACKGROUND
  An important task in diabetes management is detection of hypoglycemia. Professional continuous glucose monitoring (CGM), which produces a glucose reading every 5 min, is a powerful tool for retrospective identification of unrecognized hypoglycemia. Unfortunately, CGM devices tend to be inaccurate, especially in the hypoglycemic range, which limits their applicability for hypoglycemia detection. The objective of this study        was to develop an automated pattern recognition algorithm to detect hypoglycemic events in retrospective, professional CGM.
  
  
  METHODS
  Continuous glucose monitoring and plasma glucose (PG) readings were obtained from 17 data sets of 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. The CGM readings were automatically classified into a hypoglycemic group and a nonhypoglycemic group on the basis of different features from CGM readings        and insulin injection. The classification was evaluated by comparing the automated classification with PG using sample-based and event-based sensitivity and specificity measures.
  
  
  RESULTS
  With an event-based sensitivity of 100%, the algorithm produced only one false hypoglycemia detection.        The sample-based sensitivity and specificity levels were 78% and 96%, respectively.
  
  
  CONCLUSIONS
  The automated pattern recognition algorithm provides a new approach for detecting unrecognized hypoglycemic events in professional CGM data. The tool may assist physicians and diabetologists in conducting a more thorough evaluation of the diabetes patient's glycemic control and in initiating necessary measures for improving glycemic control.
authors: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. mhj@hst.aau.dk
authors: Morten Hasselstrøm
authors: MH
authors: Jensen
authors: Toke Folke
authors: TF
authors: Christensen
authors: Lise
authors: L
authors: Tarnow
authors: Zeinab
authors: Z
authors: Mahmoudi
authors: Mette Dencker
authors: MD
authors: Johansen
authors: Ole Kristian
authors: OK
authors: Hejlesen
mesh_terms: D000328:Adult; D000465:Algorithms; D001331:Automation; D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose