A novel fuzzy neural network estimator for predicting hypoglycaemia in insulin-induced subjects

Predicting the onset of hypoglycaemia can avoid major health complications in Type I insulin-dependent-diabetes-mellitus (IDDM) patients. This paper describes the design of a novel fuzzy neural network estimator algorithm (FNNE) for predicting the glycaemia profile and onset of hypoglycaemia in insulin-induced subjects, by modelling the changes in heart rate and skin impedance parameters. Hypoglycaemia was induced briefly in 12 volunteers (group A: 6 non-diabetic subjects and group B: 6 Type 1 IDDM patients) using insulin infusion. Their skin impedances, heart rates and actual blood glucose levels (BGL) were monitored at regular intervals. The FNNE algorithm was trained using all subjects from group A and validated/tested on the remaining subjects from group B. The mean error of estimation of BGL profile for the training data set (group A) was 0.107 (p < 0.05) and for the validation/test data set (group B) was 0.139 (p < 0.05). Furthermore, the FNNE algorithm was able to predict the onset of hypoglycaemia episodes in group A and group B with a mean error of 0.071 (p < 0.03) and 0. 176 (p < 0.05) respectively.