The Association between Reconstructed Phase Space and Artificial Neural Networks for Vectorcardiographic Recognition of Myocardial Infarction
J. eletrocardiol
; 51(3): 443-449, June. 2018. tab, graf, ilus
Artículo
en Inglés
| Sec. Est. Saúde SP, CONASS, SESSP-IDPCPROD, Sec. Est. Saúde SP
| ID: biblio-1222559
Biblioteca responsable:
BR79.1
ABSTRACT
Abstract Myocardial infarction is one of the leading causes of death worldwide. As it is life threatening, it requires an immediate and precise treatment. Due to this, a growing number of research and innovations in the field of biomedical signal processing is in high demand. This paper proposes the association of Reconstructed Phase Space and Artificial Neural Networks for Vectorcardiography Myocardial Infarction Recognition. The algorithm promotes better results for the box size 10 x 10 and the combination of four parameters box counting (Vx), box counting (Vz), self-similarity method (Vx) and self-similarity method (Vy) with sensitivity=92%, specificity=96% and accuracy=94%. The topographic diagnosis presented different performances for different types of infarctions with better results for anterior wall infarctions and less accurate results for inferior infarctions.
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Colección:
Bases de datos nacionales
/
Brasil
Base de datos:
CONASS
/
Sec. Est. Saúde SP
/
SESSP-IDPCPROD
Asunto principal:
Vectorcardiografía
/
Algoritmos
/
Redes Neurales de la Computación
/
Infarto del Miocardio
Tipo de estudio:
Factores de riesgo
Idioma:
Inglés
Revista:
J. eletrocardiol
Año:
2018
Tipo del documento:
Artículo
Institución/País de afiliación:
Dante Pazzanese Institute of Cardiology/BR
/
Federal Institute of Paraíba/BR