Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network

Allam, Fayrouz; Nossair, Zaki B.; Gomma, Hisham; Ibrahim, Ibrahim; Salam, Mona Abd-el

doi:10.1109/icces.2011.6141026

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…Many network architectures for the RNN and the FFNN are tested to optimize the predicted glucose values [1,2]. For the RNN, two hidden layers with 20 neurons in the first hidden layer and 13 in the second hidden layer were found to give the best results.…”

Section: Evaluation Of Prediction Modelsmentioning

confidence: 99%

“…The proposed technique uses a neural network [1,2] as a nonlinear model for prediction of future glucose values and a FL controller to determine the insulin dose required to regulate the blood glucose level, especially after unmeasured meals. The evaluation shows that the use of neural network in predicting future glucose concentration can help to overcome the problem of time lag between the instant of subcutaneously injecting insulin and the instant of insulin interaction with the blood glucose.…”

Section: Introductionmentioning

confidence: 99%

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Blood glucose regulation using a neural network predictor with a fuzzy logic controller

Allam

Nossair

Gomma

et al. 2013

Journal of Intelligent &Amp; Fuzzy Systems

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Current insulin therapy for patients with type 1 diabetes often results in high variability in blood glucose concentration and may cause hyper-and hypoglycemic episodes. Closing the glucose control loop with a fully automated control system improves the quality of life for insulin-dependent patients. This paper presents a nonlinear model predictive control technique for glucose regulation in type 1 diabetic patients. The proposed technique uses a neural network as a nonlinear model for prediction of future glucose values and a fuzzy logic controller (FLC) to determine the insulin dose required to regulate the blood glucose level, especially after unmeasured meals. In the proposed technique, to avoid errors of meal estimation, the patient is not required to enter any data such as the meal time and size which was, in previous systems, necessary to determine the insulin bolus. The use of neural networks in predicting future glucose levels helps the proposed control strategy to handle delays associated with insulin absorption and time-lag between subcutaneous glucose readings and the plasma glucose level. The FLC uses the predicted glucose values to determine the required insulin bolus. A feed forward neural network (FFNN) and a recurrent neural network (RNN) are tested and evaluated as nonlinear glucose prediction models. Simulation results for three meal challenges are demonstrated. our results indicate that, the use of a neural network as a predictor along with a FL controller can decrease the postprandial glucose concentration, avoids hyper glycemia, and dynamically responds to glycemic challenges. The simulation results also indicate that, the use of a RNN in glucose prediction gives better results than the use of a FFNN. The RNN provides much better prediction performance than the FFNN especially at longer prediction horizons.

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Section: Evaluation Of Prediction Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blood glucose regulation using a neural network predictor with a fuzzy logic controller

Allam

Nossair

Gomma

et al. 2013

Journal of Intelligent &Amp; Fuzzy Systems

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“…Yasini et al; [9] proposed a closed-loop control technique which incorporates expert knowledge about treatment of disease by using Mamdani-type FLC to stabilize the blood glucose concentration in normoglycaemic level of 70 mg/d l. This paper presents a closed loop insulin infusion control system using NMPC technique for glucose regulation in type 1 diabetic patients. The proposed technique uses a recurrent neural network (RNN) [10,11] as a nonlinear model for predict ion of future glucose values, and the FLC to determine the insulin dose required to regulate the blood glucose level, especially after un measured meals. The output of the FLC is scaled according to the patient's sensitivity parameters.…”

Section: Introductionmentioning

confidence: 99%

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

Allam¹,

Nossair²,

Gomma³

et al. 2012

IJISA

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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

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“…A high-order AR model was further studied, 8,9 but the prediction performance was not satisfactory. Allam et al 12 proposed a radial-basis-function neural network model to predict subcutaneous glucose concentrations. Some more studies in this field can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Several specific data-driven methods have been used for BG prediction, including time series analysis, 8 regression prediction, 5,9 gray system, 10 expert system, 11 artificial neural networks, 12 and support vector machine. 13 Researchers have performed several studies on glucose prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Wang

Wu²,

Mo³

2013

Diabetes Technology & Therapeutics

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Background: Blood glucose (BG) prediction plays a very important role in daily BG management of patients with diabetes mellitus. Several algorithms, such as autoregressive (AR) models and artificial neural networks, have been proposed for BG prediction. However, every algorithm has its own subject range (i.e., one algorithm might work well for one diabetes patient but poorly for another patient). Even for one individual patient, this algorithm might perform well during the preprandial period but poorly during the postprandial period. Materials and Methods: A novel framework was proposed to combine several BG prediction algorithms. The main idea of the novel framework is that an adaptive weight is given to each algorithm where one algorithm's weight is inversely proportional to the sum of the squared prediction errors. In general, this framework can be applied to combine any BG prediction algorithms. Results: As an example, the proposed framework was used to combine an AR model, extreme learning machine, and support vector regression. The new algorithm was compared with these three prediction algorithms on the continuous glucose monitoring system (CGMS) readings of 10 type 1 diabetes mellitus patients; the CGMS readings of each patient included 860 CGMS data points. For each patient, the algorithms were evaluated in terms of root-mean-square error, relative error, Clarke error-grid analysis, and J index. Of the 40 evaluations, the new adaptive-weighted algorithm achieved the best prediction performance in 37 (92.5%). Conclusions: Thus, we conclude that the adaptive-weighted-average framework proposed in this study can give satisfactory predictions and should be used in BG prediction. The new algorithm has great robustness with respect to variations in data characteristics, patients, and prediction horizons. At the same time, it is universal.

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Prediction of subcutaneous glucose concentration for type-1 diabetic patients using a feed forward neural network

Cited by 13 publications

References 11 publications

Blood glucose regulation using a neural network predictor with a fuzzy logic controller

Blood glucose regulation using a neural network predictor with a fuzzy logic controller

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

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

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