Glucose Prediction for Type 1 Diabetes Using KLMS Algorithm
详细信息 查看官网全文
- 英文论文题名：Glucose Prediction for Type 1 Diabetes Using KLMS Algorithm
- 论文作者：Xiaoyu Sun ; Xia Yu ; Jianchang Liu ; Honghai Wang
- 英文论文作者：Xiaoyu Sun ; Xia Yu ; Jianchang Liu ; Honghai Wang ; College of Information Science and Engineering ; Northeastern University
- 年：2017
- 作者机构：College of Information Science and Engineering,Northeastern University;
- 英文论文关键词：type 1 diabetes(T1D) ; glucose prediction ; kernel filtering algorithm ; kernel function selection
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：R587.1;TN713
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：576k
- 原文格式：D
- 会议级别：全国
摘要
For the patients with type 1 diabetes(T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring(CGM) system, it enables estimation of near-future glucose prediction by developing a reliable prediction model. In this paper, a kernel-based adaptive filtering algorithm is applied to build prediction models for glucose prediction. Then, the details for selecting a proper kernel function are also investigated. Finally, the efficiency of the proposed kernel-based forecasting method is evaluated in the short-term blood glucose prediction. The relative results are also analyzed to outline the performance of the proposed KLMS algorithm.
For the patients with type 1 diabetes(T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring(CGM) system, it enables estimation of near-future glucose prediction by developing a reliable prediction model. In this paper, a kernel-based adaptive filtering algorithm is applied to build prediction models for glucose prediction. Then, the details for selecting a proper kernel function are also investigated. Finally, the efficiency of the proposed kernel-based forecasting method is evaluated in the short-term blood glucose prediction. The relative results are also analyzed to outline the performance of the proposed KLMS algorithm.
For the patients with type 1 diabetes(T1D), it is very important to keep their blood glucose concentration in the normal level by insulin injections. As the glucose level can be checked consistently by continuous glucose monitoring(CGM) system, it enables estimation of near-future glucose prediction by developing a reliable prediction model. In this paper, a kernel-based adaptive filtering algorithm is applied to build prediction models for glucose prediction. Then, the details for selecting a proper kernel function are also investigated. Finally, the efficiency of the proposed kernel-based forecasting method is evaluated in the short-term blood glucose prediction. The relative results are also analyzed to outline the performance of the proposed KLMS algorithm.
引文
[1]Guyton,A.C.:Textbook of Medical Physiology,11th edn.Elsevier Inc.,Philadelphia(2006)
[2]Valletta J J,Chipperfield A J,Byrne C D.Gaussian Process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes.[C]//Conference proceedings:.Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society.Conference.Conf Proc IEEE Eng Med Biol Soc,2009:4913-4916.
[3]Cescon M,Johansson R,Renard E.Predicting Glycemia in Type 1 Diabetes Mellitus with Subspace-Based Linear Multistep Predictors[M]//Prediction Methods for Blood Glucose Concentration.2016.
[4]Juhl R,M?ller J K,J?rgensen J B,et al.Modeling and Prediction Using Stochastic Differential Equations[M]//Prediction Methods for Blood Glucose Concentration.Springer International Publishing,2016.
[5]Kirchsteiger H,J?rgensen J B,Renard E,et al.Prediction Methods for Blood Glucose Concentration[M].Springer International Publishing,2016.
[6]Liu W,Pokharel P P,Principe J C.The Kernel Least-Mean-Square Algorithm[J].IEEE Transactions on Signal Processing,2008,56(2):543-554.
[7]Widrow B,Hoff M E.Adaptive Switching Circuits[C]//Ire Wescon Conv.Rec.1966.
[8]Haykin S S,Widrow B.LEAST-MEAN-SQUARE ADAPTIVE FILTERS[M]//Least-Mean-Square Adaptive Filters.2005:335-443.
[9]Widrow B,Hoff M E.Adaptive switching circuits[M]//Neurocomputing:foundations of research.MIT Press,1988.
[10]Smits G F,Jordaan E M.Improved SVM regression using mixtures of kernels[C]//International Joint Conference on Neural Networks.IEEE Xplore,2002:2785-2790.
[11]Amari S,Wu S.Improving support vector machine classifiers by modifying kernel functions.[J].Neural Networks,1999,12(6):783-789.
[12]Kovatchev,M.Breton,C.Dalla Man,C.Cobelli,In silico preclinical trials:a proof of concept in closed-loop control of type 1 diabetes,J.Diabetes Sci.Technol.3(1)(2009)44–55.
[2]Valletta J J,Chipperfield A J,Byrne C D.Gaussian Process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes.[C]//Conference proceedings:.Annual International Conference of the IEEE Engineering in Medicine and Biology Society.IEEE Engineering in Medicine and Biology Society.Conference.Conf Proc IEEE Eng Med Biol Soc,2009:4913-4916.
[3]Cescon M,Johansson R,Renard E.Predicting Glycemia in Type 1 Diabetes Mellitus with Subspace-Based Linear Multistep Predictors[M]//Prediction Methods for Blood Glucose Concentration.2016.
[4]Juhl R,M?ller J K,J?rgensen J B,et al.Modeling and Prediction Using Stochastic Differential Equations[M]//Prediction Methods for Blood Glucose Concentration.Springer International Publishing,2016.
[5]Kirchsteiger H,J?rgensen J B,Renard E,et al.Prediction Methods for Blood Glucose Concentration[M].Springer International Publishing,2016.
[6]Liu W,Pokharel P P,Principe J C.The Kernel Least-Mean-Square Algorithm[J].IEEE Transactions on Signal Processing,2008,56(2):543-554.
[7]Widrow B,Hoff M E.Adaptive Switching Circuits[C]//Ire Wescon Conv.Rec.1966.
[8]Haykin S S,Widrow B.LEAST-MEAN-SQUARE ADAPTIVE FILTERS[M]//Least-Mean-Square Adaptive Filters.2005:335-443.
[9]Widrow B,Hoff M E.Adaptive switching circuits[M]//Neurocomputing:foundations of research.MIT Press,1988.
[10]Smits G F,Jordaan E M.Improved SVM regression using mixtures of kernels[C]//International Joint Conference on Neural Networks.IEEE Xplore,2002:2785-2790.
[11]Amari S,Wu S.Improving support vector machine classifiers by modifying kernel functions.[J].Neural Networks,1999,12(6):783-789.
[12]Kovatchev,M.Breton,C.Dalla Man,C.Cobelli,In silico preclinical trials:a proof of concept in closed-loop control of type 1 diabetes,J.Diabetes Sci.Technol.3(1)(2009)44–55.