Free-living inferential modeling of blood glucose level using only noninvasive inputs

Rollins, Derrick K.; Bhandari, Nidhi; Kleinedler, Jim; Kotz, Kaylee; Strohbehn, Amber; Boland, Lindsay; Murphy, Megan J.; André, Dave; Vyas, Nisarg; Welk, Greg; Franke, Warren E.

doi:10.1016/j.jprocont.2009.09.008

Cited by 41 publications

(72 citation statements)

References 24 publications

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“…Exercise can increase or decrease in glucose levels depending on the patient’s eating history (Rollins, 2009). Physiological measurements from a multi-sensor body monitor will supplement the CGM information and can be used to enhance the univariate glucose model.…”

Section: System Modelingmentioning

confidence: 99%

“…Such physiological signals will supplement the CGM information and enhance the prediction accuracy of the glucose predicting models. Even though activity and stress levels are known to have a significant effect on subject’s whole-body fuel metabolism, only a few studies are available in literature that focus on glucose homoeostasis during exercise or stress (Derouich, 2002; Kim, 2007; Roy, 2008; Rollins, 2009; Valletta, 2009). …”

Section: Introductionmentioning

confidence: 99%

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Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

et al. 2012

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Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject’s future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject’s continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.

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Section: System Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

et al. 2012

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“…29 More specifically, for each v i,t , the parameters (i.e., δ 1,i , δ 2,i , ω 1,i , ω 2,i ) in eq 6 are determined from highly nonlinear functions of the continuoustime dynamic parameters, τ ai , τ i , and ζ i , via eqs 7−10. After eq 6 is obtained for each i, the modeled glucose value is determined by substituting these results into the specific static function, f(V), such as a second-order regression form shown below: …”

Section: Modeling Methodologymentioning

confidence: 99%

An Extended Static and Dynamic Feedback–Feedforward Control Algorithm for Insulin Delivery in the Control of Blood Glucose Level

Rollins¹,

Goeddel²,

Matthews³

et al. 2015

Ind. Eng. Chem. Res.

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The potential for successful automatic control of blood glucose concentration (BGC) has entered a new era because of recent technological advancements in insulin pumps and blood glucose sensors. However, a critical advancement necessary for full automation and long-term use is a control algorithm that can effectively maintain tight control of BGC under extreme variation of important disturbances such as activity, stress, and food consumption. Because feedforward control (FFC) models disturbances directly, it has the potential to eliminate the effects of disturbances completely. A Wiener-type feedforward control law is limited to the inclusion of only input (i.e., modeled disturbances and the manipulated variable) dynamics. Using a semicoupled modeling network that includes pseudo-blood insulin concentration, this work presents a more phenomenological FFC law that includes input dynamics, blood insulin and blood glucose dynamics, and blood glucose levels. Modeling results on 15 adults with type 1 diabetes mellitus for the proposed method are nearly identical to Wiener modeling results.

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“…Stahl et al [3], based on blood glucose values, food and insulin intake, made an attempt to predict the glycemic behavior for the next 2 hours through linear and nonlinear time-series models. Recently, the effect of free-living data on glucose behavior was taken into consideration [4,5]. A method based on the Wiener models that accurately maps input disturbances concerning food, exercise and stress in blood glucose levels is reported in [4].…”

Section: Introductionmentioning

confidence: 99%

Predictive modeling of glucose metabolism using free-living data of type 1 diabetic patients

Georga

Protopappas

Polyzos

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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The aim of this study is to model the blood glucose metabolism of type 1 diabetic patients using free-living data. The proposed method considers the effect of diet, medication and exercise on blood glucose levels. Compartmental models are used to quantify the absorption of subcutaneously administered insulin, the absorption of glucose from the gut following a meal, as well as the effects of exercise on plasma glucose and insulin dynamics. Compartmental analysis is combined with a glucose predictive model which employs Support Vector machines for Regression to estimate the subcutaneous glucose concentrations. The model is trained and tested on real data recorded from two type 1 diabetic patients. The results obtained demonstrate the ability of the model to predict glucose response with a sufficient accuracy.

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Free-living inferential modeling of blood glucose level using only noninvasive inputs

Cited by 41 publications

References 24 publications

Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

An Extended Static and Dynamic Feedback–Feedforward Control Algorithm for Insulin Delivery in the Control of Blood Glucose Level

Predictive modeling of glucose metabolism using free-living data of type 1 diabetic patients

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