Assessing common classification methods for the identification of abnormal repolarization using indicators of T-wave morphology and QT interval

Research output: Contribution to journalJournal articleResearchpeer-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.
Original languageEnglish
JournalComputers in Biology and Medicine
Volume42
Issue number4
Pages (from-to)485-91
Number of pages7
ISSN0010-4825
DOIs
Publication statusPublished - Apr 2012

    Research areas

  • Adult, Algorithms, Cluster Analysis, Discriminant Analysis, Electrocardiography, Female, Fuzzy Logic, Humans, Long QT Syndrome, Male, Multivariate Analysis, ROC Curve, Signal Processing, Computer-Assisted

ID: 48052437