Hypoglycemia Detection using Fuzzy Inference System with Genetic Algorithm

被引：0

作者：

Ling, Sai Ho ^{[1
]}

Nguyen, Hung T. ^{[1
]}

Leung, Frank Hung Fat ^{[2
]}

机构：

[1] Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia

[2] Hong Kong Polytech Univ, Elect & Informat Engn Dept, Hong Kong, Peoples R China

来源：

IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) | 2011年

关键词：

Diabetes; Fuzzy logic; Genetic algorithm; Hypoglycemia; SUPPORT VECTOR MACHINE; NEURAL-NETWORKS; RECOGNITION; RESPONSES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In 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 electrocardiogram (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.

引用

页码：2225 / 2231

页数：7

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