Optimized variable translation wavelet neural network and its application in hypoglycemia detection system

An hybrid particle swarm optimization based optimized variable translation wavelet neural network (VTWNN) is proposed for detection of hypoglycemic episodes in patients with Type 1 diabetes mellitus (T1DM). Due to excellent performance in capturing nonstationary signal and nonlinear function modeling of VTWNN, it is used as a suitable classifier in hypoglycemia detection system. A global training algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) operation is investigated for parameters optimization of proposed VTWNN detection system. In this clinical study, 15 children with Type 1 diabetes were observed overnight. All the real data sets collected from Department of Heath, Government of Western Australia. Several experiments are performed over a randomly selected training set 5 patients (184 data points), validation set 5 patients (192 data points) and testing set 5 patients (153 data points) respectively. Using variable translation wavelet neural network (VTWNN), the value of testing sensitivity and specificity are 79.07 % and 50.00 %. The results show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 IEEE