Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22946
Title: Non-invasive nocturnal hypoglycemia detection for insulin-dependent diabetes mellitus using genetic fuzzy logic method
Authors: Ling, SH
San, PP
Nguyen, HT
Leung, FHF 
Keywords: Diabetes
Fuzzy logic
Genetic algorithm
Hypoglycemia
Issue Date: 2012
Publisher: Imperial College Press
Source: International journal of computational intelligence and applications, 2012, v. 11, no. 4, 1250025 How to cite?
Journal: International journal of computational intelligence and applications 
Abstract: 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 effected 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 (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
URI: http://hdl.handle.net/10397/22946
ISSN: 1469-0268
EISSN: 1757-5885
DOI: 10.1142/S1469026812500253
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