In the last decades, mathematical models have become of great importance in the context of diabetes treatment planning. Several modeling approaches, based on first principles or input-output techniques, have been proposed. However, a relevant open problem common to all these approaches is that they are not able to recover or to systematically account for the various unmeasured signals which affect a diabetic patient (e.g. food, physical activity, emotions, etc.). A novel blind identification approach is introduced in this paper, allowing us to model type 1 diabetic patients and to effectively recover the unmeasured input signals. The approach is applied to an experimental study regarding identification and prediction of the blood glucose concentration in five type 1 diabetic patients.
|Titolo:||A nonlinear blind identication approach to modeling of diabetic patients|
|Data di pubblicazione:||2016|
|Digital Object Identifier (DOI):||10.1109/TCST.2015.2462734|
|Appare nelle tipologie:||1.1 Articolo in rivista|