Hybrid particle swarm optimization based normalized radial basis function neural network for hypoglycemia detection

Abstract

In this study, a normalized radial basis function neural network (NRBFNN) is presented for detection of hypoglycemia episodes by using physiological parameters of electrocardiogram (ECG) signal. Hypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. Based on heart rate (HR) and corrected QT interval (QTc) of electrocardiogram (ECG) signal, a hybrid particle swarm optimization based normalized RBFNN is developed for recognization of hypoglycemia episodes. A global learning algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used to optimize the parameters of NRBFNN. From a clinical study of 15 children with Type 1 diabetes, natural occurrence of nocturnal hypoglycemic episodes associated with increased heart rates and corrected QT interval are studied. The overall data are organized into a training set (5 patients), validation set (5 patients) and testing set (5 patients) randomly selected. Using the optimized NRBFNN, the testing performance for detection of hypoglycemic episodes are satisfactory with 76.74% of sensitivity and 51.82% of specificity.