Abstract:
Predicting the onset of hypoglycaemia can avoid major
health complications in Type 1 insulin-dependent-diabetesmellitus
(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.
