Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
Year:2010
Research Areas
  • Health
Information
Abstract
Background and Aims: 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 paper proposes a new on-line 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 minutes, 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 (9 subjects using the Medtronic Guardian and 6 subjects using the Abbott Navigator). Three different PH are used, i.e. 15, 30 and 45 minutes. The NNM accuracy has been estimated by using the root mean square error (RMSE) and prediction delay.
International
Si
JCR
Si
Title
Diabetes Technology AND Therapeutics
ISBN
1520-9156
Impact factor JCR
2,62
Impact info
Volume
12
10.1089/dia.2009.0076
Journal number
12
From page
81
To page
88
Month
ENERO
Ranking
Participants
  • Autor: Enrique Javier Gomez Aguilera (UPM)
  • Autor: Maria Elena Hernando Perez (UPM)
  • Autor: Mª Carmen Pérez Gandía (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
S2i 2019 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)