Soft Computing for Recognition Based on Biometrics

Volume 312 of the series Studies in Computational Intelligence pp 37-55

Hybrid System for Cardiac Arrhythmia Classification with Fuzzy K-Nearest Neighbors and Neural Networks Combined by a Fuzzy Inference System

  • Eduardo RamírezAffiliated withGraduate Studies, Tijuana Institute of Technology
  • , Oscar CastilloAffiliated withGraduate Studies, Tijuana Institute of Technology
  • , José SoriaAffiliated withGraduate Studies, Tijuana Institute of Technology

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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 heartbeats. These were segmented and transformation and 3 methods of classification were used: Fuzzy KNN, Multi Layer Perceptron with Gradient Descent and momentum Backpropagation and Multi Layer Perceptron with Scaled Conjugate Gradient Backpropagation. Finally, we used a Mamdani type fuzzy inference system to combine the outputs of each classifier, and we achieved a very high classification rate of 98%.


Fuzzy KNN Mamdani Fuzzy System Neural Network Arrhythmia Classification