Adjustment of basal insulin infusion rate in T1DM by hybrid PSO

Lou, Zhijiang; Liu, Bo; Xie, Hongsheng; Wang, Youqing

doi:10.1007/s00500-014-1378-6

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…The proposed algorithm could, potentially, help other algorithms [ 20 , 21 , 22 , 23 , 24 ] to improve the obtained results by automating the task of determining the patient’s basal needs and removing the need for initially establishing the basal profile for the therapy.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Berián

Bravo

Gardel

et al. 2021

Sensors

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Technology advances have made possible improvements such as Continuous Glucose Monitors, giving the patient a glucose reading every few minutes, or insulin pumps, allowing more personalized therapies. With the increasing number of available closed-loop systems, new challenges appear regarding algorithms and functionalities. Several of the analysed systems in this paper try to adapt to changes in some patients’ conditions and, in several of these systems, other variables such as basal needs are considered fixed from day to day to simplify the control problem. Therefore, these systems require a correct adjustment of the basal needs profile which becomes crucial to obtain good results. In this paper a novel approach tries to dynamically determine the insulin basal needs of the patient and use this information within a closed-loop algorithm, allowing the system to dynamically adjust in situations of illness, exercise, high-fat-content meals or even partially blocked infusion sites and avoiding the need for setting a basal profile that approximately matches the basal needs of the patient. The insulin sensitivity factor and the glycemic target are also dynamically modified according to the situation of the patient. Basal insulin needs are dynamically determined through linear regression via the decomposition of previously dosed insulin and its effect on the patient’s glycemia. Using the obtained value as basal insulin needs and other mechanisms such as basal needs modification through its trend, ISF and glycemic targets modification and low-glucose-suspend threshold, the safety of the algorithm is improved. The dynamic basal insulin needs determination was successfully included in a closed-loop control algorithm and was simulated on 30 virtual patients (10 adults, 10 adolescent and 10 children) using an open-source python implementation of the FDA-approved (Food and Drug Administration) UVa (University of Virginia)/Padova Simulator. Simulations showed that the proposed system dynamically determines the basal needs and can adapt to a partial blockage of the insulin infusion, obtaining similar results in terms of time in range to the case in which no blockage was simulated. The proposed algorithm can be incorporated to other current closed-loop control algorithms to directly estimate the patient’s basal insulin needs or as a monitoring channel to detect situations in which basal needs may differ from the expected ones.

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Section: Discussionmentioning

confidence: 99%

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Berián

Bravo

Gardel

et al. 2021

Sensors

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“…Taking the branch valve function of Equation as the base of the sparse atoms, the sparse atoms corresponding to each dilution water branch valve are generated by the PSO algorithm according to the CD basis weight deviation curves

{normalQ}_{normalΔ} true(normalx true)

, in order to obtain the over‐complete atomic library . Each atom of the over‐complete atomic library contains the opening control information and location information of the valve, and the sparse decomposition of

{normalQ}_{normalΔ} true(normalx true)

is realized.…”

Section: Decoupling Control Of Paper CD Basis Weight Based On Sparse mentioning

confidence: 99%

Decoupling control method of paper cross‐directional basis weight based on sparse decomposition

et al. 2019

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With a focus on the coupling problems of multivariable control for the paper cross‐directional (CD) basis weight caused by a hydraulic headbox, a novel approach of multivariable decoupling control based on sparse decomposition is proposed. The model function of the CD basis weight control for a single dilution water branch valve is used as the basis of a sparse atomic library. Then, according to the deviation curve of the CD basis weight, the particle swarm optimization (PSO) algorithm is used to obtain atoms corresponding to each control unit (the multiple dilution water branch valves) to form a sparse atomic library. As each atom in the atomic library contains the control information of the corresponding dilution water branch valve, the decoupling control of paper CD basis weight can be realized via mining control information. The essence of this method is to solve the coupling problems of multivariable control by using optimization theory. Experimental results demonstrate that this method achieves a remarkable decoupling control effect.

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“…The main innovations of this study are as follows. First, we propose a modularization method based on singular value decomposition (SVD) 24 and particle swarm optimization (PSO) 25 , which can divide the process variables into different minimalist modules and an independent module. Then, we propose new monitoring indices for each module.…”

Section: Introductionmentioning

confidence: 99%

Minimalist module analysis for fault detection and localization

Lou

Wang

et al. 2021

Sci Rep

Self Cite

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Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.

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Adjustment of basal insulin infusion rate in T1DM by hybrid PSO

Cited by 4 publications

References 39 publications

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Dynamic Insulin Basal Needs Estimation and Parameters Adjustment in Type 1 Diabetes

Decoupling control method of paper cross‐directional basis weight based on sparse decomposition

Minimalist module analysis for fault detection and localization

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