Genetic algorithm based fuzzy multiple regression for the nocturnal hypoglycemic classification

Publication Type:
Conference Proceeding
IEEE Congress on Evolutionary Computation (CEC) - 2010 IEEE WORLD Congress on Computational Intelligence, 2010, pp. 2659 - 2664
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2010000088.pdf159.03 kBAdobe PDF
Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities.
Please use this identifier to cite or link to this item: