Hypoglycemia detection using fuzzy inference system with genetic algorithm
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 experi...
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| creator | Sai Ho Ling Nguyen, Hung T. Leung, Frank Hung Fat |
| description | 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. |
| doi_str_mv | 10.1109/FUZZY.2011.6007319 |
| format | Conference Proceeding |
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The classification results in term of sensitivity, specificity, and receiver operating characteristic (ROC) analysis show that the proposed classification method performs well.</description><subject>Biological cells</subject><subject>Brain modeling</subject><subject>Diabetes</subject><subject>Fuzzy logic</subject><subject>Genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Heart rate</subject><subject>Hypoglycemia</subject><subject>Sensitivity</subject><subject>Testing</subject><subject>Training</subject><issn>1098-7584</issn><isbn>9781424473151</isbn><isbn>1424473152</isbn><isbn>9781424473175</isbn><isbn>1424473179</isbn><isbn>1424473160</isbn><isbn>9781424473168</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUEtLAzEYjKhgqfsH9JI_sGtem8dRirVKwYs92EtJs9-ukX2UTYpsf30D9uJchhmY4fsGoQdKCkqJeVputtuvghFKC0mI4tRcocwoTQUTIklVXv_TJb1BsxTUuSq1uENZCD8kQUrDlZqh99V0GJp2ctB5iyuI4KIfenwMvm9wfTydJuz7GkboHeAwhQgd_vXxGzfQQ_QO27YZxmR09-i2tm2A7MJztFm-fC5W-frj9W3xvM59ui7mwrKaqoowogWnfO-4BmU4KZXZK0YUk6rW0jKtKyc1A7DGMqhcBcDK9Aifo8e_Xg8Au8PoOztOu8sa_AxBeFGj</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Sai Ho Ling</creator><creator>Nguyen, Hung T.</creator><creator>Leung, Frank Hung Fat</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201106</creationdate><title>Hypoglycemia detection using fuzzy inference system with genetic algorithm</title><author>Sai Ho Ling ; Nguyen, Hung T. ; Leung, Frank Hung Fat</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-4a2f17d02084313bc38e7930579b7207267f86a288dc682eea9a2edcdee250983</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biological cells</topic><topic>Brain modeling</topic><topic>Diabetes</topic><topic>Fuzzy logic</topic><topic>Genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Heart rate</topic><topic>Hypoglycemia</topic><topic>Sensitivity</topic><topic>Testing</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Sai Ho Ling</creatorcontrib><creatorcontrib>Nguyen, Hung T.</creatorcontrib><creatorcontrib>Leung, Frank Hung Fat</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sai Ho Ling</au><au>Nguyen, Hung T.</au><au>Leung, Frank Hung Fat</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hypoglycemia detection using fuzzy inference system with genetic algorithm</atitle><btitle>2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)</btitle><stitle>FUZZY</stitle><date>2011-06</date><risdate>2011</risdate><spage>2225</spage><epage>2231</epage><pages>2225-2231</pages><issn>1098-7584</issn><isbn>9781424473151</isbn><isbn>1424473152</isbn><eisbn>9781424473175</eisbn><eisbn>1424473179</eisbn><eisbn>1424473160</eisbn><eisbn>9781424473168</eisbn><abstract>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.</abstract><pub>IEEE</pub><doi>10.1109/FUZZY.2011.6007319</doi><tpages>7</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological cells Brain modeling Diabetes Fuzzy logic Genetic algorithm Genetic algorithms Heart rate Hypoglycemia Sensitivity Testing Training |
| title | Hypoglycemia detection using fuzzy inference system with genetic algorithm |
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