Table of Contents
ISRN Biomedical Engineering
Volume 2013 (2013), Article ID 261917, 6 pages
Research Article

Artificial Neural Network-Based Automated ECG Signal Classifier

1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, P.O. Box 32952, Menouf, Egypt
2Department of Biomedical Engineering, College of Engineering, University of Dammam, Dammam 31451, Saudi Arabia

Received 28 March 2013; Accepted 29 May 2013

Academic Editors: A. Antonio Alencar De Queiroz and A. Qiao

Copyright © 2013 Sahar H. El-Khafif and Mohamed A. El-Brawany. 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.


The ECG signal is well known for its nonlinear dynamic behavior and a key characteristic that is utilized in this research; the nonlinear component of its dynamics changes more significantly between normal and abnormal conditions than does the linear one. As the higher-order statistics (HOS) preserve phase information, this study makes use of one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back-propagation (BP) learning algorithm was used as an automated ECG classifier to investigate the possibility of recognizing ischemic heart disease from normal ECG signals. Different NN structures are tested using two data sets extracted from polyspectrum slices and polycoherence indices of the ECG signals. ECG signals from the MIT/BIH CD-ROM, the Normal Sinus Rhythm Database (NSR-DB), and European ST-T database have been utilized in this paper. The best classification rates obtained are 93% and 91.9% using EDBD learning rule with two hidden layers for the first structure and one hidden layer for the second structure, respectively. The results successfully showed that the presented NN-based classifier can be used for diagnosis of ischemic heart disease.