Modelling of blood glucose profiles non-invasively using a neural network algorithm

Monitoring blood glucose levels of Insulin-Dependent-Diabetes-Mellitus (IDDM) is essential for detecting onset of hypoglycaemia and hyperglycaemia. We have developed a method based on a neural network algorithm for estimating blood glucose levels non-invasively using only physiological parameters such as skin impedance and heart rate. Results have shown that an accuracy of 10% can be achieved.