Title :
Neural model of blood glucose level for Type 1 Diabetes Mellitus Patients
Author :
Alanis, Alma Y. ; Sanchez, Edgar N. ; Ruiz-Velazquez, Eduardo ; Leon, Blanca S.
Author_Institution :
CUCEI, Univ. de Guadalajara, Zapopan, Mexico
fDate :
July 31 2011-Aug. 5 2011
Abstract :
This paper presents on-line blood glucose level modeling for Type 1 Diabetes Mellitus (T1DM) patients. The model is developed using a recurrent neural network trained with an extended Kalman filter based algorithm in order to develop an affine model, which captures the nonlinear behavior of the blood glucose metabolism. The goal is to derive an on-line dynamical mathematical model of the T1DM for the response of a patient to meal and subcutaneous insulin infusion. Simulation results are utilized for identification and for testing the applicability of the proposed scheme.
Keywords :
Kalman filters; diseases; medical computing; patient treatment; recurrent neural nets; blood glucose metabolism; extended Kalman filter; neural model; online blood glucose level modeling; online dynamical mathematical model; recurrent neural network; subcutaneous insulin infusion; type 1 diabetes mellitus patients; Artificial neural networks; Blood; Diabetes; Insulin; Mathematical model; Sugar; Vectors; Kalman Filtering; Multilayer Perceptron; Prediction; Recurrent Neural Networks; Type 1 Diabetes Mellitus;
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
978-1-4244-9635-8
DOI :
10.1109/IJCNN.2011.6033474
