Classification of Cardiac Beats Using Discrete Wavelet Features
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Date
2015-06
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Covenant University, Otta Nigeria
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.
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Keywords
ECG, DWT, Mobile devices, ECG Feature extraction, Pan Tompkins