Diabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies.

One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate “online” plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements from compartments alternative to blood. Current commercially available CGMS are minimally invasive and offer an estimation of plasma glucose from measurements in the interstitial fluid

CGMS is a key component of the technical approach to build the artificial pancreas, aiming at closing the loop in combination with an insulin pump. Yet, the accuracy of current CGMS is still poor and it may partly depend on low performance of the implemented Calibration Algorithm (CA). In addition, the sensor-to-patient sensitivity is different between patients and also for the same patient in time. 

It is clear, then, that the development of new efficient calibration algorithms for CGMS is an interesting and challenging problem.

The indirect measurement of plasma glucose through interstitial glucose is a main confounder of CGMS accuracy. Many components take part in the glucose transport dynamics. Indeed, physiology might suggest the existence of different local behaviors in the glucose transport process.

For this reason, local modeling techniques may be the best option for the structure of the desired CA. Thus, similar input samples are represented by the same local model. The integration of all of them considering the input regions where they are valid is the final model of the whole data set.

Clustering is the technique chosen in this application for local modeling, as it is able to find automatically knowledge inherent to a database.

The general goal of this work is to design a new calibration algorithm capable of improving the accuracy of plasma glucose estimations of current devices. The proposed CA is a dynamic local-model-based clustering algorithm designed according to the system requirements.

Compensation of sensor sensitivity variations is included in the calibration algorithm  through an adaptive scheme. The algorithms developed are validated with data from several clinical trials and in silico studies.