Diabetes is a group of metabolic diseases where the body’s pancreas does not produce enough insulin or does not properly respond to insulin produced, resulting in high blood sugar levels over a prolonged period. There are several different types of diabetes, but the most common forms are type 1 and type 2 diabetes. Type 1 diabetes Mellitus (T1DM) can occur at any age, but is most commonly diagnosed from infancy to late 30s. If a person is diagnosed with type 1 diabetes, their pancreas produces little to no insulin, and the body’s immune system destroys the insulin-producing cells in the pancreas. Those diagnosed with type 1 diabetes must inject insulin several times every day or continually infuse insulin through a pump, as well as manage their diet and exercise habits. If not treated appropriately, it can cause serious complications such as cardiovascular disease, stroke, kidney failure, foot ulcers, and damage to eyes.
During the past decade, researchers have developed artificial pancreas (AP) to ease management of diabetes. AP has three components: continuous glucose monitor (CGM), insulin pump, and closed-loop control algorithm. Researchers have developed algorithms based on control techniques such as Proportional Integral Derivative (PID) and Model Predictive Control (MPC) for blood glucose level (BGL) control; however, variability in metabolism between or within individuals hinders reliable control.
This study aims to develop an adaptive algorithm using Artificial Neural Networks (ANN) based Model Predictive Control (NN-MPC) to perform proper insulin injections according to BGL predictions in diabetic rats. This study is a ground work to implement NN-MPC algorithm on real subjects. BGL data collected from diabetic rats using CGM are used with other inputs such as insulin injection and meal information to develop a virtual plant model based on a mathematical model of glucose–insulin homeostasis proposed by Lombarte et al. Since this model is proposed for healthy rats; a revised version on this model with three additional equations representing diabetic rats is used to generate data for training ANN which is applicable for the identi?cation of dynamics and the glycemic regulation of rats. The trained ANN is coupled with MPC algorithm to control BGL of the plant model within the normal range of 100 to 130 mg/dl by injecting appropriate amount of insulin. The ANN performed well with less than 5 mg/dl error (2%) for 5-minute prediction and about 15 mg/dl error (7%) for 30-minute prediction. In ¬¬addition, the NN-MPC algorithm kept BGL of diabetic rats more than 90 percent of the time within the normal range without hyper/hypo-glycaemia.
|Advisor:||Ko, Hoo Sang|
|Commitee:||Kwon, Guim, Lee, H. Felix|
|School:||Southern Illinois University at Edwardsville|
|Department:||Mechanical and Industrial Engineering|
|School Location:||United States -- Illinois|
|Source:||MAI 56/03M(E), Masters Abstracts International|
|Subjects:||Biomedical engineering, Industrial engineering, Mechanical engineering|
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