DETECTION OF QRS COMPLEXES OF
ECG RECORDING BASED ON
WAVELET TRANSFORM USING
MATLAB
RUCHITA GAUTAM
Deptt. of Electronics & Communication Engineering, MAIIT, Kota, Rajasthan, India
E-mail : ruchitagautam@gmail.com
ANIL KUMAR SHARMA**
Deptt. of Electronics & Communication Engineering,
Institute of Engineering & Technology, Alwar- 301 030, Rajasthan, India E-mail : aks_826@yahoo.co.in
Abstract:
The electrocardiogram (ECG) is quite important tool to find out more information about the heart. The main tasks in ECG signal analysis are the detection of QRS complex (i.e. R wave), and the estimation of instantaneous heart rate by measuring the time interval between two consecutive R-waves. After recognizing R wave, other components like P, Q, S and T can be detected by using window method. In this paper, we describe a QRS complex detector based on the Dyadic wavelet transform (DyWT) which is robust in comparison with time- varying QRS complex morphology and to noise. We illustrate the performance of the DyWT-based QRS detector by considering problematic ECG signals from Common Standard for Electrocardiography (CSE) database. We also compare and analyze its performance to some of the QRS detectors developed in the past.
Keywords: CSE, Dyadic wavelet transform, Heart rate, Multi resolution, Wavelet transform.
1. Introduction
(DyWT) is proposed. A chosen “mother wavelet” has a fixed shape; however, the wavelet functions derived from it by changing scales, referred to as “daughter” wavelets, have different bandwidths and time supports. At any particular scale, the DyWT is the convolution of the signal and a dyadically time-scaled daughter wavelet. Scaling the mother wavelet is the mechanism by which the DyWT adapts to the spectral and temporal changes in the signal being analyzed. However, in our approach a specific spline wavelet, suitable for the analysis of QRS complexes is designed and the scales are chosen adaptively based on the signal. The DyWT inherently has a multi resolution capability. For small scale values, it exhibits high temporal and low spectral resolution whereas for large scale values, it exhibits low temporal and high spectral resolution. A multi-resolution approach to signal analysis using the Wavelet Transform has been previously applied in many fields. The DyWT has been previously applied to ECG analysis in the context of detecting Ventricular Late Potentials (VLP's), and separating the various waves (P, R & T) in the ECG. In this paper, we are mainly interested in detecting R waves for estimating the heart rate. Here a QRS detector based on the DyWT is described that is robust both to noise and to non-stationarities in the QRS complex.
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2. What is Dyadic Wavelet Transform (DyWT) ?
In this section, we review the DyWT and list the properties that are useful for ECG signal analysis. The DyWT of a signal x (t) is defined as shown in equation (1), [7]
Both the center frequency and bandwidth of the filters vary inversely with scale, such that the ratio of the center frequency to the bandwidth (quality factor, Q) is constant. Thus the DyWT is a "constant-Q" analysis. The variable band width introduces different resolutions at different scales and hence, the DyWT also has a multi-resolution capability. The important properties of the DyWT are as follows:
the DyWT is linear
the DyWT is time shift invariant the DyWT is scale invariant
if the signal z(t) or one of its derivatives exhibits a discontinuity, then the modulus of the DyWT of x(t), │DyWTx(b, 2i)│, exhibits local maxima around the point of discontinuity and the lines of constant phase of the DyWTx(b, 2i) converge toward the point of discontinuity.
3. QRS Detection using DyWT
Fig. 1 Flow chart depicting the QRS detection by using wavelet transforms.
4. Simulations Methodology
In this part, we briefly describe the other QRS detectors and then compare their performance to the DyWT QRS detector on selected ECG data. The Okada algorithm [1] is an early application of digital filter techniques to the problem of QRS detection. The class of algorithms described in [6], referred to as multiplication of the backward difference (MOBD), multiply successive difference samples to exploit the large amplitude, high frequency characteristics of the QRS complex. The linear preprocessing of the ECG is omitted, permitting a faster detector response time. Finally, the QRS detector developed by Hamilton and Tompkins [4] uses an optimized band pass filtering method. The linear stage consists of band pass filtering and differentiator followed by non-linear squaring of the signal and a detection statistics. It should be noted that the DyWT algorithm is conceptually similar to the Hamilton-Tompkins algorithm in that, both techniques, band pass filter and differentiate the ECG signal. However, there are two significant advantages of the DyWT QRS detector: (i) since the octave band pass filters of the DyWT are scaled versions of one another, the DyWT QRS detector can adapt to changes in the bandwidth of the QRS complex and (ii) unlike regular band pass filtering, the DyWT has the additional property that if a “smoothing” wavelet is used, peaks of the DyWT correlate across successive dyadic scales at the occurrence of a transient. The software used is MATLAB and its wavelet toolbox. The ECG signals used were from Common Standard for Electrocardiography (CSE) database available in the website. The entire record was used for the analysis. The method consists of first analyzing the 2000 samples and then performing the CWT of the ECG signals for calculating the coefficient for scales 2, 4 and 8. After calculating the coefficients, the absolute value of these decompositions is taken and the R peaks are determined by using the algorithm. All feature signals were plotted and visually compared.
5. Results and Discussion
0 200 400 600 800 1000 1200 1400 1600 1800 2000 -800 -700 -600 -500 -400 -300 -200 -100 0
100 X: 198
Y: 154.1
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-2000 -1500 -1000 -500 0
500 X: 592
Y: 583.9
Fig.2 ECG waveform of database lead V1 Fig.3 ECG waveform of database V2 Practical result by MATLAB code of lead V1 Practical result by MATLAB code of lead V2 The first R peak occurs at : 198 The first R peak occurs at : 201
The second R peak occurs at : 590 The second R peak occurs at : 592 HEART RATE: 76.5306 HEART RATE: 76.7263
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-1200 -1000 -800 -600 -400 -200 0 200 400 600 800 X: 594 Y: 703.6
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-500 0 500 1000 X: 597 Y: 863.6
Fig.4 ECG waveform of databaseV3 Fig.5 ECG waveform of database V4
Practical result by MATLAB code of lead V3 Practical result by MATLAB code of lead V4 The first R peak occurs at: 203 The first R peak occurs at: 205
The second R peak occurs at: 594 The second R peak occurs at: 597 HEART RATE: 76.7263 HEART RATE: 76.5306
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-400 -200 0 200 400 600 800 1000 X: 207 Y: 916.4
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-400 -200 0 200 400 600 800 1000 1200 1400 X: 358 Y: 1313
Fig.6 ECG waveform of databaseV5 Fig.7 ECG waveform of database V6
Practical result by MATLAB code of lead V5 Practical result by MATLAB code of lead V6 The first R peak occurs at : 207 The first R peak occurs at : 94
The second R peak occurs at : 598 The second R peak occurs at : 358 HEART RATE: 76.7263 HEART RATE : 113.6364
0 200 400 600 800 1000 1200 1400 1600 1800 2000 -300 -200 -100 0 100 200 300 400 500 600 X: 598 Y: 540.5
0 200 400 600 800 1000 1200 1400 1600 1800 2000 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 X: 211 Y: 0.7895
Fig.8 ECG waveform of database I Fig.9 ECG waveform of database II
Practical result by MATLAB code of lead I Practical result by MATLAB code of lead II The first R peak occurs at : 207 The first R peak occurs at : 211 The second R peak occurs at : 598 The second R peak occurs at : 603
HEART RATE: 76.7263 HEART RATE : 76.5306
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-400 -200 0 200 400 600 800 1000 X: 131 Y: 962.1
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-500 -400 -300 -200 -100 0 100 200 300 X: 205 Y: 226
Fig.10 ECG waveform of database III Fig.11 ECG waveform of database aVL
Practical result by matlab code of lead III Practical result by matlab code of lead aVL
The first R peak occurs at : 131 The first R peak occurs at : 205
The second R peak occurs at : 421 The second R peak occurs at : 596 HEART RATE: 103.4483 HEART RATE: 76.7263
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-500 -400 -300 -200 -100 0 100 200 300 400 500 X: 83 Y: 401.3
0 200 400 600 800 1000 1200 1400 1600 1800 2000
-300 -200 -100 0 100 200 300 400 500 X: 711 Y: 386.3
Fig. 12 ECG waveform of database aVf Fig.13 ECG waveform of database aVR
Practical result by MATLAB code of lead aVF Practical result by MATLAB code of lead aVR The first R peak occurs at : 83 The first R peak occurs at : 245
In this paper, a QRS detection algorithm based on the DyWT was proposed. We have described the properties of the DyWT necessary for ECG signal processing. In particular, the property that local maxima in the DyWT correlate across successive scales and correspond to the occurrence of a transient if a smoothing wavelet is used. We exploited the property that the onset of the local maxima of the │DyWT│ of a transient signal correlates across successive dyadic scales if the mother wavelet is chosen as the first derivative of a smoothing function. The performance of the DyWT-based detector was exhaustively examined by testing the algorithm on standardized CSE database. Moreover, these results were compared to those of well-known QRS detection algorithms. Although no one algorithm exhibited superior performance in all situations, the DyWT-based QRS detector compared well with the standard. The performance of the DyWT based QRS detector is comparable to the performance of the standard techniques and exhibits superior performance over the other techniques in noise corrupted data. We have also checked this algorithm for the signal in which S waves are also detected as R waves and thus gives false detection. The main advantages of the DyWT over existing techniques are its robust noise performance and its flexibility in analyzing non-stationary ECG data. The wavelet analysis is a new promising technique in non-invasive electro cardiology providing improved methods for processing ECG signal. The benefit of wavelet transform lies in its capacity to highlight details of the ECG signal with optimal time-frequency resolution. So we have checked this algorithm on CSE database over all the 12 standard leads, the ECG classification software has successfully classified a given ECG as either normal or abnormal by employing a form of scoring mechanism.
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Miss. Ruchita Gautam did her B.E. from Rajasthan University, Jaipur (India) in 2005 and currently pursuing her M.Tech in Digital Communication Systems from Rajasthan Technical University, Kota. She has a teaching experience of 5 years and presently working as Assistant Professor in MAIIT, Kota (India). Her area of interest includes Optical Communication, Biomedical, Signal Processing and Computer Networking
Anil Kumar Sharma(MIEEE) received his M.E. degree in Electronics and Communication Engineering from Birla Institute of Technology, Deemed University, Mesra, Ranchi – India, in 2007 with first division (CGPA of 8.45 in a 10.00-point scale). He has an experience of 20 years on various RADARs and Communication Equipments. He is
currently an Associate Professor in the Department of Electronics and Communication Engineering, Institute of Engineering and Technology, Alwar- 301 030, Rajasthan, India. He has published 16 papers in International journals
