Multiple disturbance modeling and prediction of blood glucose in Type 1 Diabetes Mellitus

Type 1 diabetics often experience extreme variations in glucose concentrations which can have adverse long– and short–term effects such as severe hypoglycemia, hyperglycemia and organ degeneration. Studies have established that there is a need to maintain the glucose levels within a normal range (e.g. 80—150 mg/dL) to avoid complications caused by diabetes. However, initial attempts to regulate blood glucose levels using insulin infusion pumps, multiple injections or a combination of the two have had limited success as they lack the ability to decide the appropriate rate and/or dose of insulin based on the current metabolic state of the body. Consequently, what is needed is an automatic insulin delivery system (i.e., artificial pancreas) with the ability to determine continuously the rate of insulin delivery required to provide optimum closed-loop glucose control (i.e., to minimize the variability around a desired glucose level) and to eliminate the individual from the insulin dosage decision making in this control loop. Due to recent advances in biomedical technology, such as automatic insulin delivery systems using glucose sensors and insulin pumps, blood glucose modeling and control has received considerable attention in the process control community and models of various degrees of complexity have been developed. Glucose levels are affected by many variables, such as stress, physical activity, hormonal changes, periods of growth, medications, illness/infection, fatigue, as well as food intake and insulin tolerance. Furthermore, not only does glucose change from several sources of disturbances but their impact on blood glucose level is highly correlated, dynamic and nonlinear making it difficult to distinguish the effect each input has on blood glucose. Thus, the objective of this research is to introduce a modeling methodology that is able to take into account the simultaneous and multiple effects of food, activity, stress and their interactions. The research presented in this thesis is carried out on 15 Type 1 diabetic subjects where thirteen variables (i.e., three food variables, seven activity variables, basal insulin, bolus insulin, and time of day (TOD)) are collected for two weeks and modeled using the Wiener block–oriented model. Three types of models are compared: input–only (Model 1), input–output (Model 2), and output–only (Model 3). Results are given for k –steps ahead prediction (k –SAP) from 5 minutes to 3 hours in the future and show the importance of taking into account the interactions between input variables

Multiple-input subject-specific modeling of plasma glucose concentration for feedforward control

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an e ffective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC

Development of a Model-Based Noninvasive Glucose Monitoring Device for Non-Insulin Dependent People

Continuous-time glucose monitoring (CGM) effectively improves glucose control, as oppose to infrequent glucose measurements (i.e. using Lancet Meters), by providing frequent blood glucose concentration (BGC) to better associate this variation with changes in behavior. Currently, the most widely used CGM devices rely on a sensor that is inserted invasively under the skin. Because of the invasive nature and also the replacement cost of sensors, the primary users of current CGM devices are insulin dependent people (type 1 and some type 2 diabetics). Most non-insulin dependent diabetics use only lancet glucose measurements. The ultimate goal of this research is the development of CGM technology that overcomes these limitations (i.e. invasive sensors and their cost) in an effort to increase CGM applications among non-insulin dependent people. To meet this objective, this preliminary work has developed a methodology to mathematically infer BGC from measurements of non-invasive input variables which can be thought of as a “virtual” or “soft” sensor approach. In this work virtual sensors are developed and evaluated on 20 subjects using four BGC measurements per day and eight input variables representing meals, activity, stress, and clock time. Up to four weeks of data are collected for each subject. One evaluation consists of 3 days of training and up to 25 days of testing data. The second one consists of one week of training, one week of validation, and 2 weeks of testing data. The third one consists two weeks of training, one week of validation and one week of testing data. Model acceptability is determined on an individual basis based on the fitted correlation to CGM testing data. For 3 day, 1 week, and 2 weeks training studies, 35%, 55% and 65% of the subjects, respectively, met the Acceptability Criteria that we established based on the concept of usefulness

Multiple disturbance modeling and prediction of blood glucose in Type 1 Diabetes Mellitus

Type 1 diabetics often experience extreme variations in glucose concentrations which can have adverse long– and short–term effects such as severe hypoglycemia, hyperglycemia and organ degeneration. Studies have established that there is a need to maintain the glucose levels within a normal range (e.g. 80—150 mg/dL) to avoid complications caused by diabetes. However, initial attempts to regulate blood glucose levels using insulin infusion pumps, multiple injections or a combination of the two have had limited success as they lack the ability to decide the appropriate rate and/or dose of insulin based on the current metabolic state of the body. Consequently, what is needed is an automatic insulin delivery system (i.e., artificial pancreas) with the ability to determine continuously the rate of insulin delivery required to provide optimum closed-loop glucose control (i.e., to minimize the variability around a desired glucose level) and to eliminate the individual from the insulin dosage decision making in this control loop. Due to recent advances in biomedical technology, such as automatic insulin delivery systems using glucose sensors and insulin pumps, blood glucose modeling and control has received considerable attention in the process control community and models of various degrees of complexity have been developed. Glucose levels are affected by many variables, such as stress, physical activity, hormonal changes, periods of growth, medications, illness/infection, fatigue, as well as food intake and insulin tolerance. Furthermore, not only does glucose change from several sources of disturbances but their impact on blood glucose level is highly correlated, dynamic and nonlinear making it difficult to distinguish the effect each input has on blood glucose. Thus, the objective of this research is to introduce a modeling methodology that is able to take into account the simultaneous and multiple effects of food, activity, stress and their interactions. The research presented in this thesis is carried out on 15 Type 1 diabetic subjects where thirteen variables (i.e., three food variables, seven activity variables, basal insulin, bolus insulin, and time of day (TOD)) are collected for two weeks and modeled using the Wiener block–oriented model. Three types of models are compared: input–only (Model 1), input–output (Model 2), and output–only (Model 3). Results are given for k –steps ahead prediction (k –SAP) from 5 minutes to 3 hours in the future and show the importance of taking into account the interactions between input variables.</p

Multiple-input subject-specific modeling of plasma glucose concentration for feedforward control

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an e ffective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC.Reprinted (adapted) with permission from Industrial and Engineering Chemistry Research 53 (2104): 18216, doi: 10.1021/ie404119b. Copyright 2014 American Chemical Society.</p

Development of a Model-Based Noninvasive Glucose Monitoring Device for Non-Insulin Dependent People

Continuous-time glucose monitoring (CGM) effectively improves glucose control, as oppose to infrequent glucose measurements (i.e. using Lancet Meters), by providing frequent blood glucose concentration (BGC) to better associate this variation with changes in behavior. Currently, the most widely used CGM devices rely on a sensor that is inserted invasively under the skin. Because of the invasive nature and also the replacement cost of sensors, the primary users of current CGM devices are insulin dependent people (type 1 and some type 2 diabetics). Most non-insulin dependent diabetics use only lancet glucose measurements. The ultimate goal of this research is the development of CGM technology that overcomes these limitations (i.e. invasive sensors and their cost) in an effort to increase CGM applications among non-insulin dependent people. To meet this objective, this preliminary work has developed a methodology to mathematically infer BGC from measurements of non-invasive input variables which can be thought of as a “virtual” or “soft” sensor approach. In this work virtual sensors are developed and evaluated on 20 subjects using four BGC measurements per day and eight input variables representing meals, activity, stress, and clock time. Up to four weeks of data are collected for each subject. One evaluation consists of 3 days of training and up to 25 days of testing data. The second one consists of one week of training, one week of validation, and 2 weeks of testing data. The third one consists two weeks of training, one week of validation and one week of testing data. Model acceptability is determined on an individual basis based on the fitted correlation to CGM testing data. For 3 day, 1 week, and 2 weeks training studies, 35%, 55% and 65% of the subjects, respectively, met the Acceptability Criteria that we established based on the concept of usefulness.This article is published as Rollins DK, Beverlin L, Mei Y, Kotz K, Andre D, Vyas N, Welk G, Franke WD. The development of a virtual sensor in glucose monitoring for non-insulin dependent people. Bioinformatics and Diabetes. 2014:1, 19; doi:10.14302;issn.2374-9431.jbd-13-283. Posted with permission.</p

Multiple-Input Subject-Specific Modeling of Plasma Glucose Concentration for Feedforward Control

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an effective modeling approach for feedforward control (FFC). This approach is a Wiener block-oriented methodology, which has unique attributes for meeting critical requirements for effective, long-term, FFC