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. 2020 Jan;28(1):3-15.
doi: 10.1109/tcst.2018.2843785. Epub 2018 Jun 22.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes

Xia Yu  1 , Mudassir Rashid  2 , Jianyuan Feng  2 , Nicole Hobbs  3 , Iman Hajizadeh  2 , Sediqeh Samadi  2 , Mert Sevil  3 , Caterina Lazaro  4 , Zacharie Maloney  4 , Elizabeth Littlejohn  5 , Laurie Quinn  6 , Ali Cinar  7
Affiliations

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes

Xia Yu et al. IEEE Trans Control Syst Technol. 2020 Jan.

Abstract

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

Keywords: Kernel filtering algorithms; sparsification; type-1 diabetes (T1D).

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Figures

Fig. 1.
Fig. 1.
Comparison of measured and 30-min-ahead predicted glucose values using the KRLS, ALD-KRLS, and SC-KRLS algorithms for Adult Subject #9. (a) Prediction results along with actual CGM measurements. (b) Criteria of ALD and SC.
Fig. 2.
Fig. 2.
Comparison of measured and 30-min-ahead predicted glucose values using the KRLS, ALD-KRLS, and SC-KRLS algorithms for Adolescent Subject #5.
Fig. 3.
Fig. 3.
Comparison of measured and 30-min-ahead predicted glucose values using the KRLS, ALD-KRLS, and SC-KRLS algorithms for Child Subject #6.
Fig. 4.
Fig. 4.
Comparison of measured and 30-min-ahead predicted glucose values using KRLS, ALD-KRLS, and SC-KRLS algorithms for Clinical Subject A.
Fig. 5.
Fig. 5.
Comparison of measured and 30-min-ahead predicted glucose values using KRLS, ALD-KRLS, and SC-KRLS algorithms for Clinical Subject B.
Fig. 6.
Fig. 6.
Comparison of measured and 30-min-ahead predicted glucose values using KRLS, ALD-KRLS, and SC-KRLS algorithms for Clinical Subject C.
Fig. 7.
Fig. 7.
PRED-EGA based on KRLS, ALD-KRLS, and SC-KRLS predictors for Clinical Subject C. (a) P-EGA for KRLS. (b) P-EGA for ALD-KRLS. (c) P-EGA for SC-KRLS. (d) R-EGA for KRLS. (e) R-EGA for ALD-KRLS. (f) R-EGA for SC-KRLS.
Fig. 8.
Fig. 8.
Evaluation of the tradeoff between the PH and the prediction accuracy.
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