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Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test

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  • Merav Catalogna
  • Eyal Cohen
  • Sigal Fishman
  • Zamir Halpern
  • Uri Nevo
  • Eshel Ben-Jacob

Abstract

Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.

Suggested Citation

  • Merav Catalogna & Eyal Cohen & Sigal Fishman & Zamir Halpern & Uri Nevo & Eshel Ben-Jacob, 2012. "Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
  • Handle: RePEc:plo:pone00:0044587
    DOI: 10.1371/journal.pone.0044587
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    Cited by:

    1. Casagranda, Ivo & Costantino, Giorgio & Falavigna, Greta & Furlan, Raffaello & Ippoliti, Roberto, 2016. "Artificial Neural Networks and risk stratification models in Emergency Departments: The policy maker's perspective," Health Policy, Elsevier, vol. 120(1), pages 111-119.
    2. Erxu Pi & Nitin Mantri & Sai Ming Ngai & Hongfei Lu & Liqun Du, 2013. "BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.

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