Comparative Study on the Three Algorithms of T-wave End Detection: Wavelet Method, Cumulative Points Area Method and Trapezium Area Method / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 1185-1195, 2015.
Article
em Zh
| WPRIM
| ID: wpr-357897
Biblioteca responsável:
WPRO
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.
Texto completo:
1
Base de dados:
WPRIM
Assunto principal:
Algoritmos
/
Eletrocardiografia
/
Análise de Ondaletas
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2015
Tipo de documento:
Article