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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sai Ho Ling Hung Nguyen Kit Yan Chan |
| Copyright Year | 2010 |
| Description | Author affiliation: Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, WA, Australia (Kit Yan Chan) || Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia (Sai Ho Ling; Hung Nguyen) |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 125734 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424469093 |
| e-ISBN | 9781424469116 |
| DOI | 10.1109/CEC.2010.5586315 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-18 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Heart rate Sensitivity Training Testing Biological cells Blood Sugar |
| Content Type | Text |
| Resource Type | Article |
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