title: Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
abstract: OBJECTIVE
  Continuous glucose monitoring (CGM) devices could be useful for real-time management of diabetes therapy. In particular, CGM information could be used in real time to predict future glucose levels in order to prevent hypo-/hyperglycemic events. This article proposes a new online method for predicting future glucose concentration levels from CGM data.
  
  
  METHODS
  The predictor is implemented with an artificial neural network model (NNM). The inputs of the NNM are the values provided by the CGM sensor during the preceding 20 min, while the output is the prediction of glucose concentration at the chosen prediction horizon (PH) time. The method performance is assessed using datasets from two different CGM systems (nine subjects using the Medtronic [Northridge, CA] Guardian and six subjects using the Abbott [Abbott Park, IL] Navigator. Three different PHs are used: 15, 30, and 45 min. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay.
  
  
  RESULTS
  The RMSE is around 10, 18, and 27 mg/dL for 15, 30, and 45 min of PH, respectively. The prediction delay is around 4, 9, and 14 min for upward trends and 5, 15, and 26 min for downward trends, respectively. A comparison with a previously published technique, based on an autoregressive model (ARM), has been performed. The comparison shows that the proposed NNM is more accurate than the ARM, with no significant deterioration in the prediction delay.
  
  
  CONCLUSIONS
  The proposed NNM is a reliable solution for the online prediction of future glucose concentrations from CGM data.
authors.affiliation: Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain. cperez@gbt.tfo.upm.es
authors.forename: C
authors.initials: C
authors.lastname: Pérez-Gandía
authors.forename: A
authors.initials: A
authors.lastname: Facchinetti
authors.forename: G
authors.initials: G
authors.lastname: Sparacino
authors.forename: C
authors.initials: C
authors.lastname: Cobelli
authors.forename: E J
authors.initials: EJ
authors.lastname: Gómez
authors.forename: M
authors.initials: M
authors.lastname: Rigla
authors.forename: A
authors.initials: A
authors.lastname: de Leiva
authors.forename: M E
authors.initials: ME
authors.lastname: Hernando
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques; D001786:Blood Glucose / Q000032:analysis*; D004867:Equipment Design; D006801:Humans; D006863:Hydrogen-Ion Concentration; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests
chemical_list: D001786:Blood Glucose