Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

Estimation of insulin sensitivity in diabetic Göttingen Minipigs

In patients with type 1 diabetes mellitus, insulin sensitivity is a parameter which strongly affects insulin therapy. Due to its time-dependent variation, this parameter can improve the strategy for automatic closed-loop blood glucose control. The aim of this work is to estimate the insulin sensitivity of patients with type 1 diabetes mellitus based on measured blood glucose concentrations. For this, an Extended Kalman Filter is used, based on a simplified version of the well-known Sorensen model. The compartment model of Sorensen was adapted to the glucose metabolic behaviour in diabetic Göttingen Minipigs by means of experimental data and reduced by neglecting unobservable state variables. Here, the Extended Kalman Filter is designed for simultaneous state and parameter estimation of insulin sensitivity using the insulin infusion rate and the meal size as input signals, and measurements of blood glucose concentration as output signal. The performance of the Extended Kalman Filter was tested in in silico studies using the minipig model, and is analysed by comparing the output signal of the filter with measurement data from the animal trials.     

知识发现在2型糖尿病数据处理中的应用研究

目的:首次将知识发现理论引入到2型糖尿病发病相关因素数据处理中,从大量实测数据中识别出有效的、潜在的、有用的、可理解的发病规律。方法:根据2型糖尿病数据的特点,选用数据挖掘C4.5算法对17072条有效的整群抽样横断面健康调查数据进行决策树分类。结果:通过训练模型给出糖尿病患病与否的决策分类树,该决策树可以直观地给出发病相关因素的不同层次的相对影响,经实验测试结果对于未患病的正确识别率为92.05%,对于患病的正确识别率为80.90%,同时得出了血糖值为5.85的分类临界值。结论:决策分类树结果同目前认识的高危因素趋于一致,说明数据挖掘C4.5算法适用于2型糖尿病的发病相关因素数据分析处理,是2型糖尿病数据处理的一种新方法,其在疾病的宏观控制中有着广阔的应用前景。     

多用户VDSL2系统远端串扰抵消算法

随着宽带接入网速率提升,VDSL2技术成为最后一公里的主流接入方式。用户数量急剧增加使传输线路之间串扰成为制约VDSL2系统性能的重要因素,线路之间串扰分为近端串扰（NEXT）和远端串扰（FEXT）,VDSL2系统采用正交频分复用调制技术,近端串扰可以通过滤波器滤除,远端串扰却无法消除。主要研究VDSL2系统远端串扰噪声消除的方法,提出远端串扰噪声如何进行评估和计算,推导出远端串扰噪声计算公式,通过公式可以计算出每条线路受到其他线路串扰噪声的大小,然后发送信号时通过串扰噪声预抵消运算,接收到的信号就能成功消除串扰噪声的影响,提高了接收信号的SNR值,进而提升了VDSL2传输速率。     

2型糖尿病发病危险因素及其特征提取技术

综合选用数据挖掘中的EM聚类算法和C4.5分类算法,设计并进行了一系列以研究2型糖尿病发病危险因素与血糖变化关系为目的的实验。研究结果包括：发现了新的血糖门限值5.26和未发病门限值4.28,发现和验证了影响最大的8个重要发病危险因素及其对应的一系列重要临界值,定性定量相结合地给出了各个重要发病危险因素的影响程度在血糖值不同预警门限值下的变化关系等。     

Entropy analysis of muscular near-infrared spectroscopy (NIRS) signals during exercise programme of type 2 diabetic patients: Quantitative assessment of muscle metabolic pattern

Diabetes mellitus (DM) is a metabolic disorder that is widely rampant throughout the world population these days. The uncontrolled DM may lead to complications of eye, heart, kidney and nerves. The most common type of diabetes is the type 2 diabetes or insulin-resistant DM.     

糖尿病患者专用鞋内无线多传感器测量系统

介绍一种专为糖尿病患者而开发的鞋内无线多传感器测量系统.该系统共包括鞋内感测装置、便携式无线警报器及监察台3个部份.通过实时感测病患者脚部不同位置的压力、温度及湿度, 以便当被测参数超出预设的安全指标时发出提示铃声.此外, 所感测的参数可传送到监察台作全面分析.     

Pharmacokinetics of insulin lispro in type 2 diabetes during closed-loop insulin delivery

Insulin pharmacokinetics is not well understood during continuous subcutaneous insulin infusion in type 2 diabetes (T2D). We analyzed data collected in 11 subjects with T2D [6 male, 9 white European and two of Indian ethnicity; age 59.7(12.1) years, BMI 30.1(3.9) kg/m², fasting C-peptide 1002.2(365.8) pmol/l, fasting plasma glucose 9.6(2.2) mmol/l, diabetes duration 8.0(6.2) years and HbA1c 8.3(0.8)%; mean(SD)] who underwent a 24-h study investigating closed-loop insulin delivery at the Wellcome Trust Clinical Research Facility, Cambridge, UK. Subcutaneous delivery of insulin lispro was modulated every 15 min according to a model predictive control algorithm. Two complementary insulin assays facilitated discrimination between exogenous (lispro) and endogenous plasma insulin concentrations measured every 15–60 min. Lispro pharmacokinetics was represented by a linear two-compartment model whilst parameters were estimated using a Bayesian approach applying a closed-form model solution. The time-to-peak of lispro absorption (t_max) was 109.6 (75.5–120.5) min [median (interquartile range)] and the metabolic clearance rate (MCR_I) 1.26 (0.87–1.56) × 10⁻² l/kg/min. MCR_I was negatively correlated with fasting C-peptide (r_s = −0.84; P = .001) and with fasting plasma insulin concentration (r_s = −0.79; P = .004). In conclusion, compartmental modelling adequately represents lispro kinetics during continuous subcutaneous insulin infusion in T2D. Fasting plasma C-peptide or fasting insulin may be predictive of lispro metabolic clearance rate in T2D but further investigations are warranted.     

一种等价于PI的二型模糊控制器设计算法

区间二型模糊集合将次隶属度做了简化,基于KM降阶算法的区间二型模糊控制器实现起来相对简单.虽然区间二型模糊控制器在一定程度上优于传统的一型模糊控制器或者PI控制器等,但区间二型模糊控制器并没有充分利用二型模糊集合的次隶属度信息.为解决这些问题,研究了普通二型模糊控制器的一般结构,提出了一种等价于PI的二型模糊控制器.该...     

Experimental blood glucose interval identification of patients with type 1 diabetes

Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty.The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each.Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.     

基于.NET的糖尿病管理系统

糖尿病管理系统主要实现了对现有糖尿病数据的信息化管理,大大提高了医护人员的工作效率;系统使用了．NET环境、SQLSERVER数据库以及B／S的模式,实现了对糖尿病患者的标准化信息管理、病情评估、定时随访等功能,有助于初诊糖尿病患者的教育与管理,同时对于患者治疗的达标和临床药师开展规范的药学监护具有重要的现实意义．     

基于智能手机的血糖监测系统

为了方便患者检测血糖值、长期存储检测数据并及时得到医生的诊断结论,设计了一种基于智能手机血糖监测系统。系统由无线血糖检测传感器、患者智能手机、医生智能手机组成,可实现远程会诊和远程监护。基于MSP430单片机的无线血糖检测传感器通过蓝牙与患者智能手机连接,不仅完成了检测功能,还可利用手机的短信功能发送检测值和接收医生的诊断;同时利用智能手机强大的软件平台完成数据的存储、管理、维护等功能。通过测试,该设计较好地满足了预定要求。     

一种新的区间二型模糊集合降阶算法

区间二型模糊集合的KM/EKM降阶算法,其效率较低,难以用于二型模糊逻辑的实时控制。而基于切换点计算公式的单调性关系的IASC和EIASC算法虽然其运算时间低于KM/EKM,但其初始值都是从两侧端点开始,其效率也有待提高。本文提出了一种新的二型模糊集合降阶算法,利用切换点计算公式和论域中值的关系以及初始化左右切换点的值来提高计算效率,2个仿真实例验证了本文算法的有效性和实用性。     

Induced L2-norm minimization of glucose-insulin system for Type I diabetic patients

Using induced L₂-norm minimization, a robust controller was developed for insulin delivery in Type I diabetic patients. The high-complexity nonlinear diabetic patient Sorensen-model was considered and Linear Parameter Varying methodology was used to develop open-loop model and robust H_∞ controller. Considering the normoglycaemic set point (81.1 mg/dL), a polytopic set was created over the physiologic boundaries of the glucose-insulin interaction of the Sorensen-model. In this way, Linear Parameter Varying model formalism was defined. The robust control was developed considering input and output multiplicative uncertainties with two additional uncertainties from those used in the literature: sensor noise and worst-case design for meal disturbance (60 g carbohydrate). Simulation scenario on large meal absorption illustrates the applicability of the robust LPV control technique, while patient variability is tested with real data taken from the SPRINT clinical protocol on ICU patients.