Hybrid system for cardiac arrhythmia classification with fuzzy k-nearest neighbors and Multi Layer Perceptrons combined by a fuzzy inference system

In this paper we describe a hybrid architecture for classification of cardiac arrhythmias taking as a source the ECG records MIT-BIH Arrhythmia database. The Samples were taken from the LBBB, RBBB, PVC and Fusion Paced and Normal arrhythmias, as well as the normal beats. These were segmented and normalized and three methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent with Momentum Backpropagation, and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.