Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection
Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signa...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2010
|
| Online Access: | http://hdl.handle.net/20.500.11937/43787 |
| _version_ | 1848756808998453248 |
|---|---|
| author | Ling, S. Nguyen, H. Chan, Kit Yan |
| author2 | Gary Fogel |
| author_facet | Gary Fogel Ling, S. Nguyen, H. Chan, Kit Yan |
| author_sort | Ling, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities. |
| first_indexed | 2025-11-14T09:18:05Z |
| format | Conference Paper |
| id | curtin-20.500.11937-43787 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:18:05Z |
| publishDate | 2010 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-437872017-09-13T16:00:10Z Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection Ling, S. Nguyen, H. Chan, Kit Yan Gary Fogel Low blood glucose (Hypoglycaemia) is dangerous and can result in unconsciousness, seizures and even death. It has a common and serious side effect of insulin therapy in patients with diabetes. We measure physiological parameters (heart rate, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval) continuously to provide detection of hypoglycaemic. Based on these physiological parameters, we have developed a genetic algorithm based multiple regression model to determine the presence of hypoglycaemic episodes. Genetic algorithm is used to determine the optimal parameters of the multiple regression. The overall data were organized into a training set (8 patients) and a testing set (another 8 patient) which are randomly selected. The clinical results show that the proposed algorithm can achieve predictions with good sensitivities and acceptable specificities. 2010 Conference Paper http://hdl.handle.net/20.500.11937/43787 10.1109/CEC.2010.5586315 IEEE fulltext |
| spellingShingle | Ling, S. Nguyen, H. Chan, Kit Yan Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title | Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title_full | Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title_fullStr | Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title_full_unstemmed | Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title_short | Genetic Algorithm based Fuzzy Multiple Regression for the Nocturnal Hypoglycaemia Detection |
| title_sort | genetic algorithm based fuzzy multiple regression for the nocturnal hypoglycaemia detection |
| url | http://hdl.handle.net/20.500.11937/43787 |