title: Real-time improvement of continuous glucose monitoring accuracy: the smart sensor concept.
abstract: OBJECTIVE
  Reliability of continuous glucose monitoring (CGM) sensors is key in several applications. In this work we demonstrate that real-time algorithms can render CGM sensors smarter by reducing their uncertainty and inaccuracy and improving their ability to alert for hypo- and hyperglycemic events.
  
  
  METHODS
  The smart CGM (sCGM) sensor concept consists of a commercial CGM sensor whose output enters three software modules, able to work in real time, for denoising, enhancement, and prediction. These three software modules were recently presented in the CGM literature, and here we apply them to the Dexcom SEVEN Plus continuous glucose monitor. We assessed the performance of the sCGM on data collected in two trials, each containing 12 patients with type 1 diabetes.
  
  
  RESULTS
  The denoising module improves the smoothness of the CGM time series by an average of ∼57%, the enhancement module reduces the mean absolute relative difference from 15.1 to 10.3%, increases by 12.6% the pairs of values falling in the A-zone of the Clarke error grid, and finally, the prediction module forecasts hypo- and hyperglycemic events an average of 14 min ahead of time.
  
  
  CONCLUSIONS
  We have introduced and implemented the sCGM sensor concept. Analysis of data from 24 patients demonstrates that incorporation of suitable real-time signal processing algorithms for denoising, enhancement, and prediction can significantly improve the performance of CGM applications. This can be of great clinical impact for hypo- and hyperglycemic alert generation as well in artificial pancreas devices.
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Stefania
authors.initials: S
authors.lastname: Guerra
authors.forename: Yoeri M
authors.initials: YM
authors.lastname: Luijf
authors.forename: J Hans
authors.initials: JH
authors.lastname: DeVries
authors.forename: Julia K
authors.initials: JK
authors.lastname: Mader
authors.forename: Martin
authors.initials: M
authors.lastname: Ellmerer
authors.forename: Carsten
authors.initials: C
authors.lastname: Benesch
authors.forename: Lutz
authors.initials: L
authors.lastname: Heinemann
authors.forename: Daniela
authors.initials: D
authors.lastname: Bruttomesso
authors.forename: Angelo
authors.initials: A
authors.lastname: Avogaro
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D012984:Software
chemical_list: D001786:Blood Glucose