Research Publications in journals:
Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring.
Year:2010
Research Areas
- Health
Information
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Abstract
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| 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. | |
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International
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JCR
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Title
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Diabetes Technology AND Therapeutics |
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ISBN
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1520-9156 |
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Impact factor JCR
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2,62 |
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Impact info
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Volume
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12 |
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10.1089/dia.2009.0076 |
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Journal number
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12 |
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From page
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81 |
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To page
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88 |
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Month
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ENERO |
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Ranking
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Open Access publication
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