Nonlinear gain in online prediction of blood glucose profile in type 1 diabetic patients

Estrada, Giovanna Castillo; Re, Luigi del; Renard, Éric

doi:10.1109/cdc.2010.5717390

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…-autoregressive with exogenous input models exploiting information on CHO and insulin therapy 11,12 -autoregressive with moving average with exogenous inputs models accounting for food intake, physical activity, emotional stimuli, and lifestyle; 13 physical activity and insulin on board information; 14 and insulin and CHO information 15 -latent variable-based predictors 16 -random forests, support vector-based algorithms, and…”

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confidence: 99%

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How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Zecchin

Facchinetti

Sparacino

et al. 2016

J Diabetes Sci Technol

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Background: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. Methods: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. Results: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. Conclusions: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

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mentioning

confidence: 99%

“…- autoregressive with exogenous input models exploiting information on CHO and insulin therapy 11,12…”

mentioning

confidence: 99%

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Zecchin

Facchinetti

Sparacino

et al. 2016

J Diabetes Sci Technol

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“…𝑦 𝐴 (𝑡) = −𝑎 1 𝑦(𝑡 − 1) − ⋯ − 𝑎 𝑛𝑎 𝑦(𝑡 − 𝑛𝑎) + 𝑏 0 𝑢(𝑡) + 𝑏 1 𝑢(𝑡 − 1) + ⋯ + 𝑏 𝑛𝑏 𝑢(𝑡 − 1) + 𝑒(𝑡) (23) Next, a regression matrix, 𝜑, is created that consists of the inputs and the outputs as:…”

Section: B Steiglitz-mcbride Methodsmentioning

confidence: 99%

“…The second approach is the block structure system identification, such as the autoregressive integrated moving average [21], best linear approximation [22], normalized least mean square [23], polynomial non-linear state-space [24], Bayesian nonlinear stochastic [25], blind nonlinear identification [26], nonlinear least square [27] and nonlinear linear fractional representation [22], [24], [27], [28]. Moreover, there are review papers that compare the results of these prediction methods from different points of view such as the accuracy, sensitivity, and prediction horizon [3], [6], [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Aljamaan

Al-Naib

2022

IEEE Access

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Predicting the blood glucose level of type 1 diabetes mellitus of patients could prevent hypo/hyperglycemia incidents that are threats for the patients with this disease. A nonlinear system identification approach is proposed in this work to develop a mathematical model, which can be used to predict the blood glucose level over a given period with high accuracy. More specifically, the Hammerstein Box-Jenkins model is used to approximate the system, where two infinite impulse response filters represent the linear and noise processes, and a polynomial basis function represents the nonlinearity. The proposed identification method is based on the Steiglitz-McBride approach to predict the model parameters. Moreover, a simulation software licensed by the USA Food and Drug Administration that simulates the dynamics of the glucose-insulin system metabolism inside the body, called Type I Diabetes Metabolic Simulator (T1DMS), was used to generate the data. Thirty subjects of different age groups were considered, and the data was generated for a week with a sample per minute, i.e. 302430 data points. This data was then processed using a developed MATLAB code to predict the blood glucose level. Various scenarios were established to validate the proposed approach. The simulations showed very promising results with a very low average root mean square error of 1 mg/dl, which is seven times less compared to other prediction techniques. Other cost functions have also been used and they showed very good results. In the future, this approach can be embedded in closed-loop continuous blood glucose monitoring systems in order to give alerts to the patients and help in calculating the needed insulin dose.INDEX TERMS Blood glucose level; Box-Jenkins model; Hammerstein model; nonlinear system identification; Steiglitz-McBride approach; T1DMS simulator software; Type 1 diabetes.

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“…Patient-specific ARX models both batch-wise and recursively identified from nine patients data records by Finan et al [39] showed a mean 30-min prediction error RMSE of 26 mg/dL. An ARX model with a nonlinear forgetting factor scaled according to the glucose range was considered in CastilloEstrada et al [11,12], and a 45-min prediction horizon showed good results. A feedforward neural network (NN) was exploited in [13] and tested on 10 real datasets, incorporating, in addition to CGM data, other inputs such as SMBG readings, information on insulin, meal, hypo-and hyperglycemia symptoms, lifestyle, activity and emotions and predict glucose values up to 75 min.…”

Section: Introductionmentioning

confidence: 91%

Subspace-based linear multi-step predictors in type 1 diabetes mellitus

Cescon

Johansson

Renard

2015

Biomedical Signal Processing and Control

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Nonlinear gain in online prediction of blood glucose profile in type 1 diabetic patients

Cited by 12 publications

References 17 publications

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Prediction of Blood Glucose Level Using Nonlinear System Identification Approach

Subspace-based linear multi-step predictors in type 1 diabetes mellitus

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