Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method

Publisher:
Imperial College Press
Publication Type:
Journal article
Citation:
Leung, Frank et al. 2012, 'Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method', International Journal of Computational Intelligence and Applications, vol. 11, no. 4, pp. 1250025-1-1250025-17.
Issue Date:
2012
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Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be eA?ected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overA?tting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The eA?ectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and speciA?city compared with other existing methods for hypoglycemia detection.
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