Abstract

Cardiovascular diseases (CVD), and particularly cardia arrhythmias, have become one of the main causes of death in the world, regardless of the level of development of the countries. The detection of cardiac arrhythmias on the electrocardiogram (ECG) is a laborious task for physicians, due to the large amount of information that must be analyzed, which could lead to inadvertent errors in diagnosis. For this reason, this work presents an automatic system for the classification/detection of cardiac arrhythmias. To extract the vector of characteristics of the heartbeats, a set of linear and non-linear techniques has been used to generate thirty-three characteristics, which are used from the input of an artificial neural network (ANN) for the classification of seven types of heartbeats. The experimental results, developed on the ECG signals from the MIT-BIH database, ordered according to the AAMI standard, demonstrate a Cohen's Kappa index value of 0;9953, with an error of 0;04 %, show an accuracy of 99;48 %, even under noisy conditions. Later, this system has been implemented in hardware using the MyRio-1900 card. which is composed of a Xilinx FPGA Z-7010.