Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes
| Autor: | Nicole Hobbs, Elizabeth Littlejohn, Ali Cinar, Iman Hajizadeh, Sediqeh Samadi, Mert Sevil, Xia Yu, Mudassir Rashid, Lauretta Quinn, Zacharie Maloney, Jianyuan Feng, Caterina Lazaro |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Computation 0206 medical engineering 030209 endocrinology & metabolism 02 engineering and technology Information theory 020601 biomedical engineering Article Data modeling 03 medical and health sciences Nonlinear system Identification (information) 0302 clinical medicine Control and Systems Engineering Kernel (statistics) Noise (video) Electrical and Electronic Engineering Algorithm Predictive modelling |
| Zdroj: | IEEE Trans Control Syst Technol |
| ISSN: | 2374-0159 1063-6536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|