ECG CLASSIFICATION USING SLANTLET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Abstract

Automatic detection and classification of cardiac arrhythmias is important for diagnosis of cardiac abnormality. This paper shows a method to accurately classify ECG arrhythmias through a combination of slantlet transform and artificial neural network (ANN). The ability of the slantlet transform to decompose signal at various resolutions allows accurate extraction of features from non-stationary signals like ECG. The low frequency coefficients, which contain the maximum information about the arrhythmia, were selected from the slantlet decomposition. These coefficients are fed to a Multi-Layer Perceptron (MLP) artificial neural network which classifies the arrhythmias. In the present work the ECG data is taken from standard MIT- BIH database. The proposed system is capable of distinguishing the normal sinus rhythm and nine different arrhythmias. The overall accuracy of classification of the proposed approach is 98.40 %. Three other transformation methods are used and the accuracy of the classification of each was compared with the slantlet system accuracy. These transformation methods are: the Fourier transform which gives 67.80% accuracy, the discrete cosine transform which gives 92.72% accuracy, and the wavelet transform (using Haar and Daubechies-4 scaling function coefficients, which give an accuracies of 96.02% and 96.25% respectively).