Memorias de investigación
Artículos en revistas:
Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
Año:2010
Áreas de investigación
  • Salud
Datos
Descripción
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.
Internacional
Si
JCR del ISI
Si
Título de la revista
Diabetes Technology AND Therapeutics
ISSN
1520-9156
Factor de impacto JCR
2,62
Información de impacto
Volumen
12
DOI
10.1089/dia.2009.0076
Número de revista
12
Desde la página
81
Hasta la página
88
Mes
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Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB