Hypoglycemia detection using fuzzy inference system with genetic algorithm

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IEEE International Conference on Fuzzy Systems 2011, 2011, pp. 2225 - 2231
Issue Date:
2011-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010003987OK.pdf Published version441.04 kB
Adobe PDF
AbstractIn this paper, we develope a genetic algorithm based fuzzy inference system to recognize hypoglycemic episodes based on heart rate and corrected QT interval of the telectrocardiogram (ECG) signal. Genetic algorithm is introduced to optimize the membership functions and fuzzy rules. A practical experiment based on data from 15 children with T1DM is studied. All the data sets are collected from the Department of Health, Government of Western Australia. To prevent the phenomenon of overtraining (over-fitting), a validation strategy that may adjust the fitness function is proposed. Thus, the data are organized into a training set, a validation set, and a testing set randomly selected. The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.
Please use this identifier to cite or link to this item: