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Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection

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Open access
Authors
Ling, S.
Nguyen, H.
Chan, Kit Yan
Date
2010
Type
Conference Paper

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Citation
Ling, Sai Ho and Nguyen, Hung and Chan, Kit Yan. 2010. Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection, in Fogel, G. (ed), IEEE Congress on Evolutionary Computation (CEC 2010), Jul 18 2010, pp. 1-6. Barcelona, Spain: IEEE.
Source Title
Proceedings of the IEEE congress on evolutionary computation (CEC 2010)
Source Conference
IEEE Congress on Evolutionary Computation (CEC 2010)
DOI
School
Digital Ecosystems and Business Intelligence Institute (DEBII)
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Abstract

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.