Automated T Wave End Detection Methods: Comparison of Four Different Methods for T Wave End Detection

Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved T wave end detection is essential for electrocardiogram (ECG) processing and analysis. Several methods have been proposed and tested, but an objective comparison is lacking. In this paper, four different (semi-) automated methods are compared with the manually annotated T wave ends of the PhysioNet QT database. The first method is a semi-automatic method, based on a template matching algorithm. The second method uses the tangent of the steepest point of the descending limb of the T wave. The third and fourth method perform a maximum area search of, respectively, a trapezium and the area under the curve. In order to evaluate the accuracy and repeatability of the proposed algorithms, the mean and standard deviation (sd) of the detection errors were computed. This was performed for leads I and II separately, after selection of the best annotated T wave end per beat and after selection of the best lead. We demonstrated that the trapezium method is the least repeatable of all methods tested (sd=29.7ms), whilst the integral method scores best in terms of accuracy (mean=2.2ms). These findings were strengthened by the analysis of the generated Bland-Altman plots, where the smallest bias was observed for the integral method (-1.89ms).

ISBN:9789897582127

Jaar van publicatie:2017

BOF-keylabel:ja

IOF-keylabel:ja

Authors from:Government, Higher Education

Toegankelijkheid:Open