Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 29th International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 3140 - 3143
Issue Date:
2007-01
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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.
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