title: Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors.
abstract: BACKGROUND
  Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts.
  
  
  METHODS
  A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4(®) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function.
  
  
  RESULTS
  The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model.
  
  
  CONCLUSIONS
  The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Simone
authors.initials: S
authors.lastname: Del Favero
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.affiliation: Department of Information Engineering, University of Padova , Padova, Italy .
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D001786:Blood Glucose / Q000032:analysis*; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D003951:Diagnostic Errors / Q000517:prevention & control*; D057286:Electronic Health Records; D004867:Equipment Design; D004868:Equipment Failure; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000175:diagnosis*; D007003:Hypoglycemia / Q000097:blood / Q000175:diagnosis*; D008390:Markov Chains; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010349:Patient Compliance; D013997:Time Factors
chemical_list: D001786:Blood Glucose