Assessing common classification methods for the identification of abnormal repolarization using indicators of T-wave morphology and QT interval
Research output: Contribution to journal › Journal article › Research › peer-review
Saeed Shakibfar, Claus Graff, Lars Holger Ehlers, Egon Toft, Jørgen K. Kanters, Johannes J. Struijk
Various parameters based on QTc and T-wave morphology have been shown to be useful discriminators for drug induced I(Kr)-blocking. Using different classification methods this study compares the potential of these two features for identifying abnormal repolarization on the ECG. A group of healthy volunteers and LQT2 carriers were used to train classification algorithms using measures of T-wave morphology and QTc. The ability to correctly classify a third group of test subjects before and after receiving d,l-sotalol was evaluated using classification rules derived from training. As a single electrocardiographic feature, T-wave morphology separates normal from abnormal repolarization better than QTc. It is further indicated that nonlinear boundaries can provide stronger classifiers than a linear boundaries. Whether this is true in general with other ECG markers and other data sets is uncertain because the approach has not been tested in this setting.
|Journal||Computers in Biology and Medicine|
|Number of pages||7|
|Publication status||Published - Apr 2012|
- Adult, Algorithms, Cluster Analysis, Discriminant Analysis, Electrocardiography, Female, Fuzzy Logic, Humans, Long QT Syndrome, Male, Multivariate Analysis, ROC Curve, Signal Processing, Computer-Assisted