Hypoglycemia detection for insulin-dependent diabetes mellitus: Evolved fuzzy inference system approach

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Computational Intelligence and its Applications: Evolutionary Computation, Fuzzy Logic, Neural Network and Support Vector Machine Techniques, 2012, pp. 61 - 85
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© 2012 by Imperial College Press. All rights reserved. Insulin-dependent diabetes mellitus is classified as Type 1 diabetes and it can be further classified as immune-mediated or idiopathic. Hypoglycemia is a common and serious side effect of insulin therapy in patients with Type 1 diabetes. In this chapter, we measure physiological parameters continuously to provide a non-invasive hypoglycemia detection for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters of the electrocardiogram (ECG) signal, such as heart rate, corrected QT interval, change of heart rate and change of corrected QT interval, an evolved fuzzy inference model is developed for classification of hypoglycemia. To optimize the rules and membership functions of the fuzzy system, a hybrid particle swarm optimization with wavelet mutation (HPSOWM) is introduced. For the clinical study, 15 children with Type 1 diabetes are volunteered overnight. All the real data sets are collected from the Department of Health, Government of Western Australia and are randomly organized into a training set (10 patients) and testing set (5 patients). The results show that the evolved fuzzy inference system approach performs well in terms of sensitivity and specificity.
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