title: [Comparative Study on the Three Algorithms of T-wave End Detection: Wavelet Method, Cumulative Points Area Method and Trapezium Area Method].
abstract: 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.
authors.forename: Chengtao
authors.initials: C
authors.lastname: Li
authors.forename: Yongliang
authors.initials: Y
authors.lastname: Zhang
authors.forename: Zijun
authors.initials: Z
authors.lastname: He
authors.forename: Jun
authors.initials: J
authors.lastname: Ye
authors.forename: Fusong
authors.initials: F
authors.lastname: Hu
authors.forename: Zuchang
authors.initials: Z
authors.lastname: Ma
authors.forename: Jingzhi
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D006801:Humans; D058067:Wavelet Analysis