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

• Published in: IEEE transactions on control systems technology Vol. 28; no. 1; pp. 3 - 15
• Main Authors: Yu, Xia, Rashid, Mudassir, Feng, Jianyuan, Hobbs, Nicole, Hajizadeh, Iman, Samadi, Sediqeh, Sevil, Mert, Lazaro, Caterina, Maloney, Zacharie, Littlejohn, Elizabeth, Quinn, Laurie, Cinar, Ali
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive algorithms; Adaptive filters; Algorithms; Computational efficiency; Computational modeling; Data models; Diabetes; Dynamical systems; Glucose; Information theory; Kernel; Kernel filtering algorithms; Kernels; Model accuracy; Noise measurement; Nonlinear dynamics; Pancreas; Performance prediction; Prediction algorithms; Predictive control; Predictive models; Real time; sparsification; Sugar; type-1 diabetes (T1D); Kernel filtering algorithms; type-1 diabetes (T1D); sparsification
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2018.2843785

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.