Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm.
Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate, corrected QT interval of the ECG signal and skin impedance, a Bayesian neural network detection algorithm has been developed to recognize the presence of hypoglycemic episodes. From a clinical study of 25 children with T1DM, associated with hypoglycemic episodes, their heart rates increased (1.152+/-0.157 vs. 1.035+/-0.108, P<0.0001), their corrected QT intervals increased (1.088+/-0.086 vs. 1.020+/-0.062, P<0.0001) and their skin impedances reduced significantly (0.679+/-0.195 vs. 0.837+/-0.203, P<0.0001). The overall data were organized into a training set (14 cases) and a test set 14 cases) randomly selected. Using an optimal Bayesian neural network with 11 hidden nodes, and an algorithm developed from the training set, a sensitivity of 0.8346 and specificity of 0.6388 were achieved for the test set.
Nguyen
Hung T
HT
Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia. Hung.Nguyen@uts.edu.au
Ghevondian
Nejhdeh
N
Nguyen
Son T
ST
Jones
Timothy W
TW
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002675:Child, Preschool; D003922:Diabetes Mellitus, Type 1 / Q000150:complications / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007003:Hypoglycemia / Q000175:diagnosis* / Q000209:etiology; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity