The association between reconstructed phase space and Artificial Neural Networks for vectorcardiographic recognition of myocardial infarction

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  × 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%.

Source: Journal of Electrocardiology - February 8, 2018 Category: Cardiology Authors: Cec ília M. Costa, Ittalo S. Silva, Rafael D. de Sousa, Renato A. Hortegal, Carlos Danilo M. Regis Source Type: research