A Novel Fuzzy Neural Network Estimator for Predicting Hypoglycaemia in Insulin-Induced Subjects
KEY UNIV RESEARCH STRENGTH IN HEALTH TECHNOLOGIES SYDNEY (AUSTRALIA)
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Predicting the onset of hypoglycaemia can avoid major health complications in Type 1 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 modeling 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 BCL were monitored at regular intervals, The FNNE algorithm was trained using all subjects from group A and validatedtested on the remaining subjects from group B, The mean error of estimation of BCL profile for the training data set group A was 0,107 p 0,05 and for the validationtest 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.
- Medicine and Medical Research
- Numerical Mathematics