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Cardiac Arrhythmias Classification Method Based on MUSIC, Morphological Descriptors, and Neural Network

  • Ahmad R. Naghsh-Nilchi1Email author and
  • A. Rahim Kadkhodamohammadi1
EURASIP Journal on Advances in Signal Processing20092008:935907

Received: 6 June 2008

Accepted: 12 December 2008

Published: 13 January 2009


An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.


Left Bundle Branch BlockMorphological DescriptorProbabilistic Neural NetworkRight Bundle Branch BlockClassifier Neural Network

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Authors’ Affiliations

Department of Computer Engineering, University of Isfahan, Isfahan, Iran


© A. R. Naghsh-Nilchi and A. R. Kadkhodamohammadi. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.