ECG classification using slantlet transform and artificial neural network

Abstract EN

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 transforms which gives 67.80 % accuracy, the discrete cosine transforms 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).