Performance Assessment of Model-Based Artificial Pancreas Control Systems

Feng, Jun‐e; Turksoy, Kamuran; Çınar, Ali

doi:10.1007/978-3-319-25913-0_13

Cited by 6 publications

(6 citation statements)

References 34 publications

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“…The SLSM has two sub-modules, a CPA and a SED&FR. In previous work(Feng, Turksoy, & Cinar, 2016), an online CPA module with six indexes to track different aspects of a model-based controller was developed. The indexes used in the previous CPA module included model prediction error index ( I MPE ), model error elimination speed index ( I MEES ), dangerous change potential index ( I DCP ), dangerous change index ( I DC ), insulin constraints limitation index ( I ICL ), and weight ratio index ( I WR ).…”

Section: Methodsmentioning

confidence: 99%

“…The hybrid sensor error detection and functional redundancy (SED&FR) module of the SLSM detects and mitigates the effects of abnormal CGM sensor behavior (Figure 2). (Feng, Turksoy, Samadi, et al, 2016) All CGM sensor signals are analyzed and reconciled in SED&FR module before transmission to other modules of the controller. This module uses two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS.…”

Section: Methodsmentioning

confidence: 99%

“…The ML-SCM module contains three sub-modules based on different time scales: sample level supervision module (SLSM), period-level supervision module (PLSM) and 24-hour day level supervision module (DLSM). In previous work, an index based online controller performance assessment (CPA) module (Feng, Turksoy, & Cinar, 2016) and a hybrid CGM sensor error detection and functional reconciliation (SED&FR) module(Feng, Turksoy, Samadi, et al, 2016) based on outlier-robust Kalman filter (ORKF) and locally-weighted partial least square (LW-PLS) were developed for a GPC based AP(Turksoy et al, 2014a; Turksoy et al, 2014b). These two modules were capable of retuning the controller and reconciling the CGM signal values at each sampling time.…”

Section: Introductionmentioning

confidence: 99%

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Multi-level supervision and modification of artificial pancreas control system

Feng

Hajizadeh

Xia

et al. 2018

Computers & Chemical Engineering

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Artificial pancreas (AP) systems provide automated regulation of blood glucose concentration (BGC) for people with type 1 diabetes (T1D). An AP includes three components: a continuous glucose monitoring (CGM) sensor, a controller calculating insulin infusion rate based on the CGM signal, and a pump delivering the insulin amount calculated by the controller to the patient. The performance of the AP system depends on successful operation of these three components. Many APs use model predictive controllers that rely on models to predict BGC and to calculate the optimal insulin infusion rate. The performance of model-based controllers depends on the accuracy of the models that is affected by large dynamic changes in glucose-insulin metabolism or equipment performance that may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors and missing signals will cause calculation of erroneous insulin infusion rates. And the performance of the controller may vary at each sampling step and each period (meal, exercise, and sleep), and from day to day. Here we describe a multi-level supervision and controller modification (ML-SCM) module is developed to supervise the performance of the AP system and retune the controller. It supervises AP performance in 3 time windows: sample level, period level, and day level. At sample level, an online controller performance assessment sub-module will generate controller performance indexes to evaluate various components of the AP system and conservatively modify the controller. A sensor error detection and signal reconciliation module will detect sensor error and reconcile the CGM sensor signal at each sample. At period level, the controller performance is evaluated with information collected during a certain time period and the controller is tuned more aggressively. At the day level, the daily CGM ranges are further analyzed to determine the adjustable range of controller parameters used for sample level and period level. Thirty subjects in the UVa/Padova metabolic simulator were used to evaluate the performance of the ML-SCM module and one clinical experiment is used to illustrate its performance in a clinical environment. The results indicate that the AP system with an ML-SCM module has a safer range of glucose concentration distribution and more appropriate insulin infusion rate suggestions than an AP system without the ML-SCM module.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-level supervision and modification of artificial pancreas control system

Feng

Hajizadeh

Xia

et al. 2018

Computers & Chemical Engineering

Self Cite

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“…The risk analysis aims to define general requirements on safe control functions without choosing a specific control strategy. Nonspecific basic control functions working perfectly in nominal control are assumed, thereby excluding controller errors which depend on the control strategy such as MPC [39]. Moreover, it is assumed in the analysis that no safety functions such as low-glucose-suspend systems are implemented, though they became a standard in marketed insulin pumps.…”

Section: Controller Unitmentioning

confidence: 99%

Risk analysis for the design of a safe artificial pancreas control system

et al. 2018

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Closed-loop glucose control has the potential to improve the glycemic control in patients with diabetes mellitus type 1. Such an artificial pancreas (AP) should keep the user safe despite all disturbances and faults. The objective of this paper is to analyze those perturbations according to their effects on the glycemic status, and thereby supporting an informed design process of the control system. As suggested by the international standard ISO 14971 for risk management of medical devices, the well proven failure modes and effects analysis (FMEA) was chosen as instrument. An FMEA scheme was modified for this purpose and applied to a single-hormone system with subcutaneous and intraperitoneal routes for glucose sensing and insulin administration. Faults that imply urgent danger and thus require fast detection and diagnosis were identified and distinguished from disturbances that can be sufficiently addressed by basic control functions, e.g. by adaptive control algorithms. Requirements and testing criteria for basic control functions as well as fault detection and diagnosis functions can be derived from the provided overview.Safety is the primary requirement on a control system that doses insulin to regulate the blood glucose level (BGL) in diabetes mellitus type 1 (DM1). People with DM1 have an insufficient pancreatic insulin production and depend on lifelong insulin administration to avoid hyperglycemia. The chronically elevated BGL would otherwise lead to several long-term complications. Hypoglycemia caused by overdosing of insulin, on the other hand, may lead to unconsciousness and death in the F.

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“…Data driven method includes machine learning, multivariate statistical analysis, signal processing, information fusion and rough set [18]. The multivariate statistical analysis is a kind of performance analysis of the correlation between multiple variables of the process.…”

Section: Theoretical Basis Of Data Driven Technologymentioning

confidence: 99%

Performance Analysis of Multi-Variable Control System Based on Data Driven

Shi-zhe¹,

Wang²

2018

IJIMSE

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With the development of science and technology, the control system has become an indispensable means to ensure the safe, stable and efficient operation of the process with the improvement of system capability and modernization level. As time goes on, the characteristics of industrial production process will change, resulting in the degradation of control performance, product quality decline, directly affecting economic benefits. Therefore, performance evaluation of control system is of great significance to improve control performance and economic benefits of enterprises. Combustion control system is an important and typical multivariable control system in thermal power plant. Its performance evaluation is very important for power production process. So a method and detail steps of performance analysis based on data driven for multivariable control systems are presented. Using multivariate statistical analysis, the overall performance of the system and the performance index of the individual variable are defined respectively by the generalized eigenvalue of the covariance matrix. Through the supervisory information system, the data of the combustion control system of a certain thermal power unit is obtained and the operating data for one day is analyzed using the proposed method. The results show that this method can realize the relative accuracy evaluation of the overall performance and the individual performance for each controlled variable of the control system.

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Performance Assessment of Model-Based Artificial Pancreas Control Systems

Cited by 6 publications

References 34 publications

Multi-level supervision and modification of artificial pancreas control system

Multi-level supervision and modification of artificial pancreas control system

Risk analysis for the design of a safe artificial pancreas control system

Performance Analysis of Multi-Variable Control System Based on Data Driven

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