Wavelet feature extraction for ECG beat classification
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Date
2014-10-29
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Electrocardiography (ECG) signal is a bioelectrical
signal which depicts the cardiac activity of the heart. It is a
technique used primarily as a diagnostic tool for various cardiac
diseases. ECG provides necessary information on the
electrophysiology and changes that may occur in the heart. Due
to the increase in mortality rate associated with cardiac diseases
worldwide despite recent technological advancement, early
detection of these diseases is of paramount importance. This
paper has proposed a robust ECG feature extraction technique
suitable for mobile devices by extracting only 200 samples
between R-R intervals as equivalent R-T interval using Pan
Tompkins algorithm at preprocessing stage. The discrete wavelet
transform (DWT) of R-T interval samples are calculated and the
statistical parameters of wavelet coefficients such as mean,
median, standard deviation, maximum, minimum, energy and
entropy are used as a time-frequency domain feature. 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.
Classification has been performed using neural network
backpropagation algorithm because of its simplicity. While
equivalent R-T interval features gives average accuracy of
98.22%, the proposed hybrid method gives a promising result
with average accuracy of 99.84% with reduced classifier
computational complexity.
Description
Keywords
ECG, DWT, Mobile devices, ECG Feature extraction, Pan Tompkins