Electrocardiographic T-wave peak-to-end interval for hypoglycaemia detection
Electrocardiographic T wave peak-to-end interval (TpTe) is one parameter of T wave morphology, which contains indicators for hypoglycaemia. This paper shows the corrected TpTe (TpTe c ) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support...
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| Veröffentlicht in: | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology 2010-01, Vol.2010, p.618-621 |
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| description | Electrocardiographic T wave peak-to-end interval (TpTe) is one parameter of T wave morphology, which contains indicators for hypoglycaemia. This paper shows the corrected TpTe (TpTe c ) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. By comparing with the classification systems using inputs of corrected QT interval (QT c ) and heart rate only, the results indicate that the inclusion of TpTec in combination with QTc and heart rate performs better in the detection of hypoglycaemia in terms of sensitivity, specificity and accuracy. |
| doi_str_mv | 10.1109/IEMBS.2010.5627430 |
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This paper shows the corrected TpTe (TpTe c ) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. 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This paper shows the corrected TpTe (TpTe c ) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. 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This paper shows the corrected TpTe (TpTe c ) interval as one of the inputs contributing to detect hypoglycaemia. Support vector machine (SVM) and fuzzy support vector machine (FSVM) utilizing radial basis function (RBF) are used as the classification methods in this paper. By comparing with the classification systems using inputs of corrected QT interval (QT c ) and heart rate only, the results indicate that the inclusion of TpTec in combination with QTc and heart rate performs better in the detection of hypoglycaemia in terms of sensitivity, specificity and accuracy.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>21096769</pmid><doi>10.1109/IEMBS.2010.5627430</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Artificial Intelligence Classification algorithms Diabetes Diagnosis, Computer-Assisted - methods Electrocardiography - methods Heart rate Humans Hypoglycemia - diagnosis Kernel Pattern Recognition, Automated - methods Reproducibility of Results Sensitivity Sensitivity and Specificity Support vector machines |
| title | Electrocardiographic T-wave peak-to-end interval for hypoglycaemia detection |
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