Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

Gani, Abdul; Gribok, Andrei V.; Rajaraman, Srinivasan; Ward, W. Kenneth; Reifman, Jaques

doi:10.1109/tbme.2008.2005937

Cited by 118 publications

(112 citation statements)

References 18 publications

(34 reference statements)

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“…it reflects predictability. ACF has been applied to blood glucose time series [9,23] to analyse linear predictability, but as mentioned above, a major obstacle is the existence of non-stationary behaviour.…”

Section: The Autocorrelation Function Of Differential Incrementsmentioning

confidence: 99%

Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles

Khovanova

Khovanov

Sbano

et al. 2013

Computer Methods and Programs in Biomedicine

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Abstract-Continuous glucose monitoring is increasingly used in the management of diabetes.Subcutaneous glucose profiles are characterized by a strong non-stationarity, which limits the application of correlation-spectral analysis. We derived an index of linear predictability by calculating the autocorrelation function of time series increments and applied detrended fluctuation analysis to assess the non-stationarity of the profiles. Time series from volunteers with both type 1 and type 2 diabetes and from control subjects were analysed. The results suggest that in control subjects, blood glucose variation is relatively uncorrelated, and this variation could be modelled as a random walk with no retention of 'memory' of previous values.In diabetes, variation is both greater and smoother, with retention of inter-dependence between neighboring values. Essential components for adequate longer term prediction were identified via a decomposition of time series into a slow trend and responses to external stimuli. Implications for diabetes management are discussed.

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Section: The Autocorrelation Function Of Differential Incrementsmentioning

confidence: 99%

Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles

Khovanova

Khovanov

Sbano

et al. 2013

Computer Methods and Programs in Biomedicine

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“…Gani et al 5,9 clinically evaluated subject-specific AR models with 30 model orders to improve BG management. The results of Zhao et al 29 show that the best order of the AR model is 7.…”

Section: Resultsmentioning

confidence: 99%

“…The coefficients a i can be calculated using the least squares method, 5 and then the model can be subsequently used for predicting glucose concentrations. The AR prediction model is easy to implement and can make complete use of all data.…”

Section: Ar Model Forecastingmentioning

confidence: 99%

“…4 However, the use of such methods is limited because of the inherent complexity of the glucose-insulin dynamic system. The second group includes data-driven methods, [5][6][7] which can predict glucose concentration based only on measured data. Data-driven techniques mainly depend on measured data and do not require any previous knowledge about the physiology of diabetes.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Originally developed by [4] the idea of T1DM CGM time-series analysis has been further pursued by [5] and [6] to predict future glucose concentration from its past history. However, none of these works considered the dynamic interplay between previously injected insulin, meal intake and eventually exercise.…”

Section: Introductionmentioning

confidence: 99%