Comparative Study on the Three Algorithms of T-wave End Detection: Wavelet Method, Cumulative Points Area Method and Trapezium Area Method.
- Author:
Chengtao LI
;
Yongliang ZHANG
;
Zijun HE
;
Jun YE
;
Fusong HU
;
Zuchang MA
;
Jingzhi WANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electrocardiography;
Humans;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2015;32(6):1185-1195
- CountryChina
- Language:Chinese
-
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.
