01-01-2013, 01:01 PM
Wavelet based QRS detection in ECG using MATLAB
Wavelet based QRS detection in ECG using MATLAB.pdf (Size: 400.35 KB / Downloads: 128)
Abstract
In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survival
analysis of heart diseases. Electrocardiography has had a profound influence on the practice of medicine.
This paper deals with the detection of QRS complexes of ECG signals using derivative
based/Pan-Tompkins/wavelet transform based algorithms. The electrocardiogram signal contains an
important amount of information that can be exploited in different manners. The ECG signal allows for the
analysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals from
MIT/BIH Arrhythmia data base are used to verify the various algorithms using MATLAB software.
Wavelet based algorithm presented in this paper is compared with the AF2 algorithm/Pan-Tompkins
algorithms for signal denoising and detection of QRS complexes meanwhile better results are obtained for
ECG signals by the wavelet based algorithm. In the wavelet based algorithm, the ECG signal has been
denoised by removing the corresponding wavelet coefficients at higher scales. Then QRS complexes are
detected and each complex is used to find the peaks of the individual waves like P and T, and also their
deviations.
Introduction
The ECG is nothing but the recording of the heart’s electrical activity. The deviations in the normal
electrical patterns indicate various cardiac disorders. The time domain method of ECG signal analysis is not
always sufficient to study all the features of ECG signals. So, the frequency representation of a signal is
required. To accomplish this, FFT (Fast Fourier Transform) technique can be applied. But the unavoidable
limitation of this FFT is that the technique failed to provide the information regarding the exact location of
frequency components in time. As the frequency content of the ECG varies in time, the need for an accurate
description of the ECG frequency contents according to their location in time is essential. This justifies the
use of time frequency representation in quantitative electrocardiology. The immediate tool available for this
purpose is the Short Term Fourier Transform (STFT). But the major draw-back of this STFT is that its time
frequency precision is not optimal. Hence we opt a more suitable technique to overcome this drawback.
Derivative Operator
The next processing step is differentiation, standard technique for finding the high slopes that normally
distinguish the QRS complexes from other ECG waves. The derivative procedure suppresses the low
frequency components of P and T waves, and provides a large gain to the high-frequency components
arising from the high slopes of the QRS Complex.
Squaring
The squaring operation makes the result positive and emphasizes large differences resulting from QRS
complexes; the small differences arising from P and T waves are suppressed. The high frequency
components in the signal related to the QRS complex are further enhanced. This is a nonlinear
transformation that consists of point by point squaring of the signal samples.
Integration
The squared waveform passes through a moving window integrator. This integrator sums the area under the
squared waveform over a suitable interval, advances one sample interval, and integrates the new predefined
interval window. The half-width of window has been chosen as 27 to include the time duration of extended
abnormal QRS complexes, but short enough that it does not overlap both a QRS complex and a T-wave.
MA (moving average) filter extracts features in addition to the slope of the R wave. It is implemented with
the following difference equation: