In order to find the most suitable algorithm of T-wave end point detection for clinical detection, we tested three methods, which are not just dependent on the threshold value of T-wave end point detection, i.e. wavelet method, cumulative point area method and trapezium area method, in PhysioNet QT database (20 records with 3 569 beats each). We analyzed and compared their detection performance. First, we used the wavelet method to locate the QRS complex and T-wave. Then we divided the T-wave into four morphologies, and we used the three algorithms mentioned above to detect T-wave end point. Finally, we proposed an adaptive selection T-wave end point detection algorithm based on T-wave morphology and tested it with experiments. The results showed that this adaptive selection method had better detection performance than that of the single T-wave end point detection algorithm. The sensitivity, positive predictive value and the average time errors were 98.93%, 99.11% and (-2.33±19.70) ms, respectively. Consequently, it can be concluded that the adaptive selection algorithm based on T-wave morphology improves the efficiency of T-wave end point detection.
Citation： LIChengtao, ZHANGYongliang, HEZijun, YEJun, HUFusong, MAZuchang, WANGJingzhi. Comparative Study on the Three Algorithms of T-wave End Detection: Wavelet Method, Cumulative Points Area Method and Trapezium Area Method. Journal of Biomedical Engineering, 2015, 32(6): 1185-1190, 1195. doi: 10.7507/1001-5515.20150210 Copy