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EN
This paper presents an artificial pancreas algorithm implemented with a discrete-time sliding-mode method combined with a nonlinear block controllable form using a unidirectional control law and a discrete adaptive observer. The algorithm is both unidirectional, using insulin as unique input, and hybrid with a percentage of the pre-meal bolus administered in advance to complement the control action. Personalisation combines patient clustering and individual gain calculation after analysing glucose time-in-range. The stability of the complete closed-loop system involving the observer bounds is tested using the Lyapunov methodology. The performance of the algorithm is evaluated with 256 patients, simulated using Hovorka’s model. The evaluation defines two scenarios (closed-loop and open loop with continuous subcutaneous insulin infusion, CSII), divided into three 1- week intervals, to respectively compare the impact of glucose control under the expected meal plan as a result of changes in carbohydrates quantities and meal schedule, and in response to more extreme events. The performance of time-in-range in each period obtained with CSII is improved by hybrid closed-loop in all cases, i.e., with the expected meals (weekdays 74.9 vs 78.4; weekends 71.5 vs 73.4), with meal variability (weekdays 74.3 vs 77.6; weekend 71.6 vs 72.7), and in the presence of transgressions (forgetting meal announcement: 50.1 vs 63.4; meal omission: 78.2 vs 82.03; copious meal: 60.1 vs 63.4). Compared to CSII, the personalised nonlinear block controller was able to increase the time-in-range without glucose presence in time below range level 2 for 98 % of the cohort for expected meals, meal variability, and transgressions.
EN
Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model's features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.
EN
The type1 of diabetes is a chronic situation characterized by abnormally high glucose levels in the blood. Persons with diabetes characterized by no insulin secretion in the pancreas (ß-cell) which also known as insulin-dependent diabetic Mellitus (IDDM). In order to keep the levels of glucose in blood near the normal ranges (70–110mg/dl), the diabetic patients needed to inject by external insulin from time to time. In this paper, a Modified Second Order Sliding Mode Controller (MSOSMC) has been developed to control the concentration of blood glucose levels under a dis-turbing meal. The parameters of the suggested design controller are optimized by using chaotic particle swarm optimization (CPSO) technique, the model which is used to represent the artificial pancreas is a minimal model for Bergman. The simulation was performed on a MATLAB/SIMULINK to verify the performance of the suggested controller. The results showed the effectiveness of the proposed MSOSMC in controlling the behavior of glu-cose deviation to a sudden rise in blood glucose.
4
Content available remote In silico testing of optimized Fuzzy P+D controller for artificial pancreas
EN
Background and objectives: Despite therapeutic advances, a complete cure has not been found yet for patients with type 1 diabetes (T1D). Artificial pancreas (AP) is a promising approach to cope with this disease. The controller part of the AP can compute the insulin infusion rate that keeps blood glucose concentration (BGC) in normoglycemic ranges. Most controllers rely on model-based controllers and use manual meal announcements or meal detection algorithms. For a fully automated AP, a controller only using the patient's BGC data is needed. Methods: An optimized Mamdani-type hybrid Fuzzy P+D controller was proposed. Using the University of Virginia/Padova Simulator, a 36 h scenario was tested in nine virtual adult patients. To take into account the effect of continuous glucose monitor noise, the scenario was repeated 25 times for each adult.The main outcomes were the percentage of time BGC levels in the euglycemic range, low blood glucose index (LBGI), and blood glucose risk index (BGRI), respectively. Results: The obtained BGC values were found to be in the euglycemic range for 82.6% of the time. Moreover, the BGC values were below 50 mg/dl, below 70 mg/dl and above 250 mg/dl for 0%, 0.35% and 0.74% of the time, respectively. The BGRI, LBGI, and high blood glucose index (HBGI) were also found as 3.75, 0.34 and 3.41, respectively. The proposed controller both increases the time the BGC levels in the euglycemic range and causes less hypoglycemia and hyperglycemia relative to the published techniques studied in a similar scenario and population.
EN
Recent technological advancements in diabetes technologies, such as Continuous Glucose Monitoring (CGM) systems, provide reliable sources to blood glucose data. Following its development, a new challenging area in the field of artificial intelligence has been opened and an accurate prediction method of blood glucose levels has been targeted by scientific researchers. This article proposes a new method based on Artificial Neural Networks (ANN) for blood glucose level prediction of Type 1 Diabetes (T1D) using only CGMdata as inputs. To show the efficiency of our method and to validate our ANN, real CGM data of 13 patients were investigated. The accuracy of the strategy is discussed based on some statistical criteria such as the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The obtained averages of RMSE are 6.43 mg/dL, 7.45 mg/dL, 8.13 mg/dL and 9.03 mg/dL for Prediction Horizon (PH) respectively 15 min, 30 min, 45 min and 60 min and the average of MAPE was 3.87% for PH = 15 min, knowing that the smaller is the RMSE and MAPE, the more accurate is the prediction. Experimental results show that the proposed ANN is accurate, adaptive, and very encouraging for a clinical implementation. Furthermore, while other studies have only focused on the prediction accuracy of blood glucose, this work aims to improve the quality of life of T1D patients by using only CGM data as inputs and by limiting human intervention.
EN
Type 1 diabetes (T1D) is a chronic disease requiring patients to know their blood glucose values in order to ensure blood glucose levels as close to normal as possible. Hence, the ability to predict blood glucose levels is of a great interest for clinical researchers. In this sense, the literature is rich with several solutions that can predict blood glucose levels. Unfortunately, these methods require the patient to specific their daily activities: meal intake, insulin injection and emotional factors, which can be error prone. To reduce this burden on the patent, this work proposes to use only continuous glucose monitoring (CGM) data to predict blood glucose levels independently of other factors. To support this, support vector regression (SVR) and differential evolution (DE) algorithms were investigated. The proposed method is validated using real CGM data of 12 patients. The obtained average of root mean square error (RMSE) was 9.44, 10.78, 11.82 and 12.95 mg/dL for prediction horizon (PH) respectively equal to 15, 30, 45 and 60 min. The results of the present study and comparison with some previous works show that the proposed method holds promise. The SVR based on DE algorithm achieved high prediction accuracy while being robustness, automatic, and requiring no human intervention.
PL
Niniejsza praca miała na celu ukazanie specjalistom, jak dużą grupę pacjentów stanowią osoby z cukrzycą oraz wskazać możliwe postępowanie w przypadku takiego pacjenta. Podkreśla też ważne aspekty opieki specjalistycznej zarówno przed aplikacją, jak i po aplikacji soczewek kontaktowych. Wyjściem do badań było założenie, że osoba z cukrzycą typu I nie ma innych chorób, związanych bezpośrednio z cukrzycą. Przyjętą tezą było stwierdzenie, iż osoby z cukrzycą nie powinny być dyskwalifikowane do noszenia soczewek kontaktowych. W badaniu wzięło udział 12 osób chorujących na cukrzycę, których wady zostały skorygowane soczewkami kontaktowymi. Wszyscy badani przy zachowaniu odpowiedniej higieny noszenia, użytkowania oraz dezynfekcji byli poddawani regularnym kontrolom po tygodniu, dwóch i czterech tygodniach oraz po trzech i sześciu miesiącach od daty pierwszej aplikacji. Wnioskiem ogólnym, który potwierdził przyjętą tezę pracy, jest to, że pacjent obarczony cukrzycą może być dobrym kandydatem na użytkownika soczewek kontaktowych. Jednak powinno się kłaść duży nacisk na edukację pacjenta ze strony optometryczno-okulistycznej, ale także diabetologicznej i udostępnić tę formę korekcji pod pewnymi warunkami, które w dużej mierze są podobne jak dla osoby niechorującej na cukrzycę.
EN
The purpose of this master thesis was to show to the specialists how large could be the group of patients with diabetes and to indicate possible vision correction of such patients. It emphasizes the important aspects of their care, before and after application of the contact lenses. The output of the research was the assumption that a person with type I diabetes does not have other diseases directly related to diabetes. Adopted thesis was that people with diabetes should not be disqualified for wearing contact lenses. In the study participated 12 diabetics who were corrected with contact lenses for half a year. All of them were taught a proper hygiene and disinfection, were regularly checked, after a week, two weeks, a month, three and six months after the application. The study confirmed the thesis that patients affected by diabetes should be taken under consideration as good candidates for correction with contact lenses, but under the certain conditions, which are largely identical with those for standard contact wearers. They also should be educated not only by their specialists like optometrists and ophthal-mologists, but also by diabetologists.
8
EN
In theory, the continuous subcutaneous insulin infusion (CSII) has a few advantages over the multiple daily insulin injections (MDI) that should lead to improved glycemic control and lower risk of hypoglycemia. In practice, both treatment regimens allow for adequate control of glycemia. The objective of this review is to discuss the most important factors contributing to this situation. We made a comprehensive evidence-based review of the factors affecting effectiveness of CSII and MDI, with a special attention to algorithms for insulin dose adjustments and the automatic bolus calculators. Regardless of the treatment regimen that is used a few different interdependent factors influence the final result of the intensive insulin therapy. These factors comprise: patients' education, attitude, emotional stability and compliance, and careful analysis of the treatment results by a physician establishing the appropriate rate of basal insulin infusion or the basal dose of insulin and adjusting insulin doses to: the meals, the planned physical activity and the actual and target glucose levels. Our study implies that good glycemic control in patients with type 1 diabetes requires not only a thorough patient education and complying with medical recommendations, but also an individual determination of therapy goals and ways of achieving them. That is why, regardless of the treatment method that is applied, it is the choice of appropriate algorithms and adjusting them to the patient's way of life what allow for achieving pre-specified therapeutic goals. Technical means such as automatic bolus calculators might supplement but they cannot replace patients education and compliance.
EN
Natural biological control involves the normal functioning of the living organism (i.e. human body) to regulate its parameters such that the vital functions are kept within the normal operating range. When this natural control fails, the biological feedback (thus a closed loop system) is unstable and/or operates under non-optimal conditions of the vital capacity of the subject. In this context, ensuring surviving capacity of the subject implies to artificially control the vital functions presenting the functional failure. Nowadays technology enables development of artificial closed loop devices to correct and provide the normal functions of the organism, replacing thus the damaged/non-optimal parts or helping in recovering their natural properties (rehabilitation techniques). Two of the most en-vogue applications of artificial control will exemplify the importance and the posed challenges: - a neuroprothesis device to control paralyzed skeletal muscles; this enables rehabilitation of drop-foot or hand-grasp movements with paretic or paralyzed skeletal muscles by use of a self-adaptive (auto-tuning) control strategy; - and an artificial pancreas for diabetes type I patients; the blood glucose control in diabetic patients type I is made by use of an in-house developed model-based predictive control algorithm in which input (insulin rate) and output (glucose level) are constrained.
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