| 1. |
- Cescon, Marzia, et al.
(författare)
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Adaptive Subspace-based prediction of T1DM glycemia
- 2011
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Konferensbidrag (refereegranskat)abstract
- Blood glucose levels fluctuate widely in Type 1 diabetic patients expecially during stressful situations, intercurrent illness, exercise and changes in meal composition. Furthermore, inter- and intra-subject variability make the prediction of such fluctuations an even harder task. The paper deals with the application of online data-driven multi-step subspace-based patient-specific predictor models to T1DM glycemia prediction, exploiting the interplay between previously injected insulin, meal intake and eventually vital signs. When the unknown underlying model is changing over time we believe such an adaptive scheme may constitute a valuable step towards the development of an advisory tool capable of informing the patient at any time about the evolution of glycemia and possibly give advices on the most appropriate control action to take.
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| 2. |
- Cescon, Marzia, et al.
(författare)
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Glycemic Trend Prediction Using Empirical Model Identification
- 2009
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Ingår i: Proc. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference (CDC2009 & CCC 2009). ; , s. 3501-3506
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Konferensbidrag (refereegranskat)abstract
- Using methods of system identification and prediction, we investigate near-future prediction of individual specific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX- and ARMAX-based predictors was done. Predictions over 30 minutes look-ahead were capable to track glucose variation even in sensible ranges for estimation data, but not on validation data.
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| 3. |
- Cescon, Marzia, et al.
(författare)
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Identification of Individualized Empirical Models of Carbohydrate and Insulin Effects on T1DM Blood Glucose Dynamics
- 2014
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Ingår i: International Journal of Control. - : Informa UK Limited. - 0020-7179 .- 1366-5820. ; 87:7, s. 1438-1453
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Tidskriftsartikel (refereegranskat)abstract
- One of the main limiting factors in improving glucose control for type 1 diabetes mellitus (T1DM) subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing the magnitude and duration of such effects would be useful not only for patients and physicians, but also for the development of a controller targeting glycaemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualised multi-input single-output (MISO) models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be estimated. The estimation strategy is continuous-time data-driven system identification and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters.
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| 4. |
- Cescon, Marzia, et al.
(författare)
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Impulsive Predictive Control of T1DM Glycemia: an In-Silico Study
- 2013
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Ingår i: Proc. ASME 2012 5th Annual Dynamic Systems and Control Conference & JSME 2012 11th Motion and Vibration Conference (DSCC2012-MOVIC 2012), Oct 17-19, 2012, Fort Lauderdale, Florida, USA.. ; , s. 319-326
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Konferensbidrag (refereegranskat)
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| 5. |
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| 6. |
- Cescon, Marzia, et al.
(författare)
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Individualized Empirical Models of Carbohydrate and Insulin Effects on T1DM Blood Glucose Dynamics
- 2013
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Ingår i: Proc. 2013 IEEE Multi-conference on Systems and Control (MSC2013).
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Konferensbidrag (refereegranskat)abstract
- ne of the main limiting factors in improving glucose control for T1DM subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing magnitude and duration of such effects would be useful not only for patients and physicians but also for the development of a controller targeting glycemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualized MISO models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be identified. The estimation strategy is data-driven and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters.
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| 7. |
- Cescon, Marzia
(författare)
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Linear Modeling and Prediction in Diabetes Physiology
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. The development of a prediction engine capable of personalized on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise would therefore be a valuable initiative towards an improved management of the desease. This thesis presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 minutes, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. In order to address model-based control for blood glucose regulation, low-order, individualized, data-driven, stable, physiological relevant models were identified from a population of 9 T1DM patients data. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models.ARMAX multi-step-ahead predictors were estimated by means of least-squares estimation; next regularization of the autoregressive coefficients was introduced. ARMAX-based predictors and zero-order hold were computed to allow comparison.Finally, preliminary results on subspace-based multi-step-ahead multivariate predictors is presented.
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| 8. |
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| 9. |
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| 10. |
- Cescon, Marzia
(författare)
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Modeling and Prediction in Diabetes Physiology
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diabetes is a group of metabolic diseases characterized by the inability of the organism to autonomously regulate the blood glucose levels. It requires continuing medical care to prevent acute complications and to reduce the risk of long-term complications. Inadequate glucose control is associated with damage, dysfunction and failure of various organs. The management of the disease is non trivial and demanding. With today’s standards of current diabetes care, good glucose regulation needs constant attention and decision-making by the individuals with diabetes. Empowering the patients with a decision support system would, therefore, improve their quality of life without additional burdens nor replacing human expertise. This thesis investigates the use of data-driven techniques to the purpose of glucose metabolism modeling and short-term blood-glucose predictions in Type I Diabetes Mellitus (T1DM). The goal was to use models and predictors in an advisory tool able to produce personalized short-term blood glucose predictions and on-the-spot decision making concerning the most adequate choice of insulin delivery, meal intake and exercise, to help diabetic subjects maintaining glycemia as close to normal as possible. The approaches taken to describe the glucose metabolism were discrete-time and continuous-time models on input-output form and statespace form, while the blood glucose short-term predictors, i.e., up to 120 minutes ahead, used ARX-, ARMAX- and subspace-based prediction.
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| 11. |
- Cescon, Marzia, et al.
(författare)
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Modeling the Impact of a Standardized Breakfast on T1DM Fasting Blood Glucose
- 2012
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Konferensbidrag (refereegranskat)abstract
- The design of a controller for glycemia regulation, either in open or in closed-‐loop, relies on models able to describe the effects of a meal intake and an insulin injection on blood glucose dynamics. The purpose of this study was therefore to propose a physiological relevant yet parsimonious model for carbohydrate action on fasting blood glucose in T1DM patients when no insulin is taken.
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| 12. |
- Cescon, Marzia, et al.
(författare)
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Multi-step-ahead Multivariate Predictors: A Comparative Analysis
- 2010
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Ingår i: Proc. 49th IEEE Conf. Decision and Control (CDC2010). ; , s. 2837-2842
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Konferensbidrag (refereegranskat)abstract
- The focus of this article is to undertake a comparative analysis of multi-step-ahead linear multivariate predictors. The approach considered for the estimation will be based on geometrically reliable linear algebra tools, resorting to subspace identification methods. A crucial issue is quantification of both bias error and variance affecting the estimate of the prediction for increasing values of the look ahead when only a small number of samples is available. No complete theory is available so far, nor sufficient numerical experience. Therefore, the analysis of this paper aims at shading some lights on the topic providing some insights and help to develop some intuitions.
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| 13. |
- Cescon, Marzia, et al.
(författare)
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On Data-driven Multistep Subspace-based Linear Predictors
- 2011
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Ingår i: 18th IFAC World Congress. - 1474-6670. - 9783902661937 ; 44:1, s. 11447-11452
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Konferensbidrag (refereegranskat)abstract
- The focus of this contribution is the estimation of multi-step-ahead linear multivariate predictors of the output making use of finite input-output data sequences. Different strategies will be presented, the common factor being the exploitations of geometric operations on appropriate subspaces spanned by the data. In order to test the capabilities of the proposed methods in predicting new data, a real-life example, namely, the case of blood glucose prediction in Type 1 Diabetes patients, is provided.
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| 14. |
- Cescon, Marzia, et al.
(författare)
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Patient-specific Glucose Metabolism Models for Model Predictive Control of T1DM Glycemia
- 2012
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Konferensbidrag (refereegranskat)abstract
- The development of a predictive control algorithm for glycaemia regulation in diabetic subjects requires patient-specific models of the glucose metabolism which are physiologically relevant, parsimonious, yet able to accurately forecast blood glucose. Given the measured data: total plasma insulin [mIU/L]; plasma glucose [mg/dL]; plasma glucose rate of appearance after intestinal absorption [mg/kg/min], the objective was to find individualized, simple and plausible glucose-insulin interaction models suitable for exploitation in a MPC framework.
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| 15. |
- Cescon, Marzia, et al.
(författare)
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Personalized Short-Term Blood Glucose Prediction in T1DM
- 2012
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Konferensbidrag (refereegranskat)abstract
- Insulin therapy for tight glycemia regulation in T1DM strongly depends on patients ́ daily decisions about insulin delivery adaptations in relation to: health status, current BG, target BG, insulin sensitivity, diet and foreseen activities. A personalized predictor providing near future BG predictions would support the users in the decision-making tasks while letting them maintaining control over their own treatments management.
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| 16. |
- Cescon, Marzia, et al.
(författare)
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Predicting Glycemia in Type 1 Diabetes Mellitus with Subspace-Based Linear Multistep Predictors
- 2016
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Ingår i: Prediction Methods for Blood Glucose Concentration. - Cham : Springer International Publishing. - 9783319259130 ; , s. 107-132
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Bokkapitel (refereegranskat)abstract
- A major challenge for a person with diabetes is to adapt insulin dosage regimens and food intake to keep blood glucose within tolerable limits during daily life activities. The accurate prediction of blood glucose levels in response to inputs would support the patients with invaluable information for appropriate on-the-spot decision making concerning the management of the disease. Against this background, in this paper we propose multistep data-driven predictors to the purpose of predicting blood glucose multiple steps ahead in the future. We formulate the predictors based on the state-space construction step in subspace identification methods for linear systems. The clinical data of 14 type 1 diabetic patients collected during a 3-days long hospital visit were used. We exploited physiological models from the literature to filter the raw information on carbohydrate and insulin intakes in order to retrieve the inputs signals to the predictors. Predictions were based on the collected CGMS measurements, recalibrated against finger stick samples and smoothed through a regularization step. Performances were assessed with respect to YSI blood glucose samples and compared to those achieved with a Kalman filter identified from data. Results proved the competitiveness of the proposed approach.
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| 17. |
- Cescon, Marzia, et al.
(författare)
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Short-Term Diabetes Blood Glucose Prediction Based On Blood Glucose Measurements
- 2009
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Konferensbidrag (refereegranskat)abstract
- Given current glucose value, amount and timing of insulin injections and food intake, is it possible to predict future blood glucose levels with a prediction error of ±20 mg/dL? In the current study an attempt is made to empirically model the glucose-insulin dynamic interplay and to provide model-based short-term predictors suitable for the purpose.
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| 18. |
- Cescon, Marzia, et al.
(författare)
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Subspace-based Identification of Compliance Dynamics of Parallel Kinematic Manipulator
- 2009
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Ingår i: IEEE/ASME International Conference On Advanced Intelligent Mechatronics, 2009. ; 1-3, s. 1028-1033
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Konferensbidrag (refereegranskat)abstract
- A high-bandwith robot-workpiece interaction requires a stiff robot without resonances in the frequency range of operation. In this article, the compliance dynamics of the Gantry-Tau parallel kinematic robot were identified using subspace-based identification and physical modeling. Measurements were performed both with a camera vision system developed and with a laser tracker. Although promising simulation results for another Gantry-Tau prototype exist, both vision and laser tracker experiments identified multiple resonances around 14 Hz, which can reduce force control performance.
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| 19. |
- Cescon, Marzia, et al.
(författare)
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Subspace-Based Linear Multi-Step Predictors in Type 1 Diabetes Mellitus
- 2015
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Ingår i: Biomedical Signal Processing and Control. - : Elsevier BV. - 1746-8094. ; 22, s. 99-110
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge for a person with diabetes is to adapt insulin dosage regimens and food intake to keep blood glucose within tolerable limits during daily life activities. The early knowledge about the effects of inputs on glycemia would provide the patients with invaluable information for appropriate on-the-spot decision making concerning the management of the disease. Against this background, in this paper we propose multi-step data-driven predictors to the purpose of predicting blood glucose multiple steps ahead in the future, supporting therefore the patients when deciding upon treatments. We formulate the predictors based on the state-space construction step in subspace identification methods for linear systems. Physiological models from the literature were used to filter the raw information on carbohydrate and insulin intakes in order to retrieve the input signals to the predictors. The clinical data of 14 type 1 diabetic patients collected in hospital during a 3-days long visit were used. Half of the data were employed for predictor development and the remaining half for validation. Mean population prediction error standard deviation on 30 min, 60 min, 90 min, 120 min ahead prediction on validation data resulted in, respectively, 19.17 mg/dL, 37.99 mg/dL, 50.62 mg/dL and 58.06 mg/dL. (C) 2014 Elsevier Ltd. All rights reserved.
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| 20. |
- Cescon, Marzia, et al.
(författare)
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Subspace-Based Model Identification of Diabetic Blood Glucose Dynamics
- 2009
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Ingår i: IFAC Proceedings Volumes. ; 42:10, s. 233-238
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Tidskriftsartikel (refereegranskat)abstract
- The paper describes an on-line identification algorithm to estimate the steam production of a municipal solid waste incinerator. The algorithm has to learn on-line the system dynamics due to the heavy disturbances acting on the incineration process. The learning algorithm is based on radial basis function networks and combines the growth criterion of the resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks.
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| 21. |
- Cescon, Marzia, et al.
(författare)
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Subspace-Based Multi-Step Predictors for Predictive Control
- 2015
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Ingår i: Control-Oriented Modelling and Identification: Theory and Practice. - 9781849196147 - 9781849196154 ; , s. 125-142
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Bokkapitel (refereegranskat)abstract
- In the framework of the subspace-based identification of linear systems, the first step for the construction of a state-space model from observed input-output data involves the estimation of the output predictor. Such construction is based on projection operations of certain structured data matrices onto suitable subspaces spanned by the collected data. To the purpose of predictive control using short-term predictors, this algorithmic step can be elaborated to provide data-based multi-step predictors. Using such an approach, this contribution deals with subspace-based identification methods for the estimation of short-term predictors. One illustrative example is provided: blood glucose prediction in type 1 diabetes mellitus.
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| 22. |
- Johansson, Rolf, et al.
(författare)
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Continuous-Time Model Identification Using Non-Uniformly Sampled Data
- 2013
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Ingår i: Proc. IEEE AFRICON 2013 Conference.
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Konferensbidrag (refereegranskat)abstract
- This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs, thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem. Next, theory, algorithms and validation results are presented for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data.
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| 23. |
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