Por favor, use este identificador para citar o enlazar este ítem:
http://dspace.ucuenca.edu.ec/handle/123456789/34255
Título : | ECG Multilead QT interval estimation using support vector machines |
Autor: | Vanegas Peralta, Pablo Fernando Morocho Zurita, Carlos Villie Dugarte, Nelson Cuadrado, Jhosmary Medina, Ruben Wong de balzan , Sara Null |
Correspondencia: | Cuadrado, Jhosmary, jhosmary.cuadros@sansano.usm.cl |
Palabras clave : | Vector machines Interval estimation Multilead QT |
Área de conocimiento FRASCATI amplio: | 2. Ingeniería y Tecnología |
Área de conocimiento FRASCATI detallado: | 2.11.2 Otras Ingenierias y Tecnologías |
Área de conocimiento FRASCATI específico: | 2.11 Otras Ingenierias y Tecnologías |
Área de conocimiento UNESCO amplio: | 05 - Ciencias Físicas, Ciencias Naturales, Matemáticas y Estadísticas |
ÁArea de conocimiento UNESCO detallado: | 0511 - Biología |
Área de conocimiento UNESCO específico: | 051 - Ciencias Biológicas y Afines |
Fecha de publicación : | 2019 |
Volumen: | Volumen 2019 |
Fuente: | Journal of Healthcare Engineering |
metadata.dc.identifier.doi: | 10.1155/2019/6371871 |
Tipo: | ARTÍCULO |
Abstract: | This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points ( and ) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of . |
Resumen : | This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points ( and ) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of . |
URI : | http://dspace.ucuenca.edu.ec/handle/123456789/34255 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065251764&origin=inward |
URI Fuente: | https://www.hindawi.com/journals/jhe/contents/year/2019 |
ISSN : | 2040-2295 |
Aparece en las colecciones: | Artículos
|
Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.