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

TY - JOUR

T1 - Assessing common classification methods for the identification of abnormal repolarization using indicators of T-wave morphology and QT interval

AU - Shakibfar, Saeed

AU - Graff, Claus

AU - Ehlers, Lars Holger

AU - Toft, Egon

AU - Kanters, Jørgen K.

AU - Struijk, Johannes J.

N1 - Copyright © 2012 Elsevier Ltd. All rights reserved.

PY - 2012/4

Y1 - 2012/4

N2 - 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.

AB - 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.

KW - Adult

KW - Algorithms

KW - Cluster Analysis

KW - Discriminant Analysis

KW - Electrocardiography

KW - Female

KW - Fuzzy Logic

KW - Humans

KW - Long QT Syndrome

KW - Male

KW - Multivariate Analysis

KW - ROC Curve

KW - Signal Processing, Computer-Assisted

U2 - 10.1016/j.compbiomed.2012.01.001

DO - 10.1016/j.compbiomed.2012.01.001

M3 - Journal article

C2 - 22306238

SN - 0010-4825

VL - 42

SP - 485

EP - 491

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

IS - 4

ER -