PID and LQG controllers for diabetes system with internal delay: a comparison study.

Closed-loop treatment for insulin dependent type 1 diabetes patients is a recent medical practice in insulin delivery (bionic pancreas) which aims to achieve tight control of glucose level in plasma and ensure minimizing risk of hypoglicemia. Among those most popular closed-loop controller strategies, proportional integral derivative (PID) and linear quadratic Gaussian (LQG) controllers are designed and compared for insulin delivery in diabetic patients. The controllers are designed based on individual and nominal model which is to study the ability of each controller in order to maintain blood glucose concentration for similar patient's dynamic. The comparison is conducted numerically not only for for patients suffering type 1 diabetes mellitus (T1DM), but also type 2 diabetes mellitus (T2DM), and double diabetes mellitus (DDM) in the present of internal delay systems, which causes instability. The responses show that the proposed PID controller is better at maintaining the blood glucose level in the normal range for a longer delay of delay in hepatic glucose production. The patient with longer performing physical exercise has lower oscillation peaks in blood glucose concentration.

A formal approach to discovering simultaneous additive masking between auditory medical alarms.

The failure of humans to respond to auditory medical alarms has resulted in numerous patient injuries and deaths and is thus a major safety concern. A relatively understudied source of response failures has to do with simultaneous masking, a condition where concurrent sounds interact in ways that make one or more of them imperceptible due to physical limitations of human perception. This paper presents a method, which builds on a previous implementation, that uses a novel combination of psychophysical modeling and formal verification with model checking to detect masking in a modeled configuration of medical alarms. Specifically, the new method discussed here improves the original method by adding the ability to detect additive masking while concurrently improving method usability and scalability. This paper describes how these additions to our method were realized. It then demonstrates the scalability and detection improvements via three different case studies. Results and future research are discussed.