Zeinab Mahmoudi - Performance enhancement of continuous glucose monitoring in a dual hormonal artificial pancreas based on nonlinear filtering

Fully automated control of blood glucose (BG) level by means of a portable artificial pancreas (AP) will substantially increase the quality of life for type 1 diabetes patients, by reducing the burden of meticulous considerations about manual adjustment of insulin dosage and timing. 

However a fully automated AP has not been realized yet, due to the faults and failures of the AP components, and also because the state-of-the-art insulins do not have fast-enough action after administration. All those factors are significant barriers to the development of a fully automated AP, because they reduce the patient’s safety. One of the main sources of the AP failures is the anomalies and low accuracy of the continuous glucose monitoring (CGM) sensor in the AP, which is a challenge demanding considerable effort to handle.

The first aim of this project is to develop a CGM accuracy enhancement module that can reduce the deviations of the CGM readings from the actual BG levels as much as possible, and can detect and correct the CGM faults and anomalies in a dual-hormonal AP. This is planned to be achieved by using novel signal processing methods based on stochastic differential equations, nonlinear modeling and filtering, in combination with sensor redundancy.

The second aim of the project is the clinical evaluation of the module. To fulfil this goal, we will pursue two approaches: 1) the performance of the CGM accuracy enhancement module will be investigated in the presence of different AP disturbances such as meal, exercise, and stress, 2) the effect of CGM improvement on the CGM-based clinical decision making for diabetes treatment adjustments outside the closed-loop approach, will be evaluated in a follow-up study.