Classification of Cardiac Beats Using Discrete Wavelet Features

Abstract

With the growing technology, the tools which continuously monitor the health status of the people are becoming the integral part of our lives. The detection of a cardiac disease or tracking the heart activities for ongoing cardiac conditions is now possible with portable electrocardiography (ECG) monitors. For detection and classification of ECG signals in portable devices, the robust features and efficient classification algorithms are very important. Thus, in this study, a robust feature set based on discrete wavelet transform (DWT) is proposed, and the performance of the classification tools such as artificial neural networks, support vector machines and probabilistic neural networks are compared. After preprocessing, the R peaks are located by the well-known Pan Tompkins algorithm and 200 samples are taken as equivalent R-T interval in the proposed technique. The statistical parameters such as mean, median, standard deviation, maximum, minimum, energy and entropy of DWT coefficients are used as the feature set. The proposed hybrid technique has been tested by classifying three ECG beats as normal, right bundle branch block (Rbbb) and paced beat using the signals from Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) arrhythmia database and processed using Matlab 2013 environment. The best accuracy of 99.84% has been obtained by Db4 mother wavelet with artificial neural network as classifier.