Evaluation of Using a Recurrent Neural Network (RNN) and a Fuzzy Logic Controller (FLC) In Closed Loop System to Regulate Blood Glucose for Type-1 Diabetic Patients

Abstract

Type-1 diabetes is a disease characterized by high blood-glucose level. Using a fully automated closed loop control system improves the quality of life for type1 diabetic patients. In this paper, a scalable closed loop blood glucose regulation system which is tuned to each patient is presented. This control system doesn't need any data entry from the patient. A recurrent neural network (RNN) is used as a nonlinear pred ictor and a fuzzy logic controller (FLC) is used to determine the insulin dosage which is required to regulate the blood glucose level. The insulin infusion is restricted by calculation of insulin on board (IOB) wh ich avoids overdosing of insulin. The performance of the proposed NMPC is evaluated by applying fu ll day meal regime to each patient. The evaluation includes testing in relation to specific life style condition, i.e. fasting, postprandial, fault meal estimat ion, and exercise as a metabolic disturbance. Our simu lation results indicate that, the use of a RNN along with a FLC can decrease the postprandial glucose concentration. The proposed controller can be used in fasting and can avoid severe hypo or hyper-glycemia during fasting. It can also decrease the postprandial g lucose concentration and can dynamically respond to different glycemic challenges. Fig.6: the controlled glucose of patient #1 using the proposed control system for (a) fasting (no meals), (b) scheduled meals, (c) 50% increasing in a meal, (d) 10% increasing in a meal, (e) 10% decreasing a meal, (f) 5% increasing the sensitivity, (g) 40% increasing the sensitivity(increasing the sensitivity). Fig.7: T he controlled glucose for patient #2 using the proposed control system for (a) fasting (no meals), (b) scheduled meals, (c) 50% increasing in the second meal, (d) 10% increasing in the second meal, (e) 10% decreasing in the second meal, (f) 5% increasing in the sensitivity, (g) 40% increasing in the sensitivity

Towards Non-Invasive Extraction and Determination of Blood Glucose Levels

“…The preparation and testing of the RNN and FNN prediction models are performed utilizing glucose measurements from nine subjects and patient data [ 25 ]. Most RNNs and FFNNs are tested to advance the predicted glucose values [ 25 ]. For RNNs, two hidden layers with 20 neurons and 13 neurons gave the best results [ 24 ].…”

“…The method [ 24 ] may be adapted for varied classification weights, membership functions, and nodal network connections, for lifestyle management decision support including fluid recommended intake and suggested activity. Hybrid approaches that include NNs and FLCs offer greater system accuracy for diabetic management: NNs offer utility for adaptation and training of the DSS while the FLC implements the decision and classification logic [ 25 ].…”

Proposing Two Defuzzification Methods based on Output Fuzzy Set Weights

“…In [11] has been described how the development of an (FLC) for a class of industrial Electro hydraulic manipulator enhances the robustness and tracking ability of the controller. In medicine Allam, F. et al [12] used a fuzzy logic controller and a recurrent neural network to determine the insulin dosage in a closed loop blood glucose regulation system that results in decreasing the postprandial glucose concentration. …”

Specification and Verification of Medical Monitoring System Using Petri-nets

“…Grant,[ 21 ] Khooban et al .,[ 22 ] Hari Kumar et al .,[ 23 ] Allam et al . [ 24 ] and Yasini et al . [ 25 ] exploited fuzzy concepts in diabetes system.…”

“…Allam et al . [ 24 ] used: (1) Fuzzy logic controller to determine INS dosage and (2) a recurrent neural network that nonlinearly predicts the effect of each calculated dose on the future of the blood glucose level. They claimed that the prediction leads to excluding severe hyper- and severe hypo-glycemic events.…”