ANALYSIS OF ECG WITH DB10 WAVELET USING VERILOG HDL

International Journal of Technical Research and Applications e-ISSN: 2320-8163,
www.ijtra.com Volume 3, Issue 3 (May-June 2015), PP. 142-146
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ANALYSIS OF ECG WITH DB10 WAVELET
USING VERILOG HDL
Shivani Mehrotra1, Dr. Janak B. Patel*2, Ms. Jagandeep Kaur*3, Ms. Nisha Charaya*4
1
M.Tech (ECE), Amity University Gurgaon (Haryana), India
*2
Professor, Amity University Gurgaon (Haryana), India
*3,4
Assistant Professor, Amity University Gurgaon (Haryana), India
shivanimtech5@gmail.com
Abstract-The abnormal condition of electrical activity of
the heart given by ECG (Electrocardiogram) shows the cardiac
diseases affecting the human being. The P, QRS, T wave shape,
amplitude and time intervals between its various peaks contains
useful information about the nature of disease.
This paper presents wavelet technique to analyze ECG signal.
Discrete Wavelet Transform (DWT) is employed as noise
removal and feature extraction tool to achieve efficient design.
Daubechies wavelet of order 10 has been designed using Verilog
Hardware Description Language (HDL) and ModelSim Altera
6.4a is used as simulator. MIT-BIH database has been used for
the analysis.
Keywords- Discrete Wavelet Transform, ECG
(Electrocardiography), FIR Digital filter, STFT (Short time
Fourier transform), Wavelet
I. INTRODUCTION
The Dutch Physician Willem Einthoven, in 1903 marked
the beginning of new era in medical diagnostic techniques for
establishment of clinical ECG. [2]
It is the objective measurement for the activation,
transportation and recovery of heart activities.
Figure 1: Electrocardiogram Signal
ECG gives two kinds of information:
1. The duration of electrical wave crossing the heart
decides the electrical activity is normal, slow or
irregular.
2. The amount of electrical activity passing enables to
find whether parts of heart are too large or
overworked. [3]
ECG signal is characterized by P wave, QRS complex, T
wave. Each wave conveys useful information.
The information conveyed by different waves of ECG is
shown in the form of table:
Table No.1: Information conveyed by P-QRS-T wave of
ECG signal
ECG wave INFORMATION
P wave Atrial
activation(contraction)
PQ interval Signal travels from SA
node to AV node
Q wave Activation of
Interventricular septum
R wave Activation of major
mass of ventricles
S wave Last phase of
activation of ventricles
ST interval Represents activity of
pumping of blood by
ventricles
T wave Ventricular relaxation
ECG signal is easily interfered by the different noises because
it is very faint. Method to suppress noises effectively is always
an important problem in the analysis of ECG signal. ECG
varies in time, thus there is a need for an accurate description
of the ECG frequency contents according to their location in
time. This justifies the use of time frequency representation in
quantitative electro cardiology. [4]
II. WAVELET TRANSFORM
The meaning of wavelet transform is that a series of
translated and compressed wavelet function is used to describe
sampling signal. A series of shifted and compressed wavelet
basis functions are used for mapping sampling signal.
A set of analyzing wavelets allows the decomposition of
ECG signal into a set of coefficients. Each analyzing wavelet
has its own time duration, time location and frequency band.
The wavelet coefficient resulting from the wavelet
transformation corresponds to a measurement of the ECG
components in this time segment and frequency band. [4],[7]
The required information characteristic of time is
obtained through shifting the signal, and the frequency
characteristic of the signal is available through scaled
transform of Wavelet.

143 | P a g e
Wavelet Transform can be divided into two categories, as
shown below:
Figure 2: Wavelet Transform categories
Wavelet basis functions have many families which include
Haar, Daubechies, meyer, symlets, coiflets, B-spline etc,
shown in the figures below:
Figure 3: Representing wavelet basis function (haar, db2,
db4, meyr wavelet)

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Figure 4: Representing wavelet basis function (sym2, coif1,
coif2, bior2.8 wavelet)
If the wavelet basis function has a certain similarity with
sampling signal, the energy of filtered signal becomes more
concentrated and complexity of calculation is reduced after
transformation.
III. OPTIMAL WAVELET SELECTION
The selection of suitable mother wavelet basis function is
necessary for efficient analysis of ECG. Optimal wavelet will
lead to maximization of coefficient values; this will produce
highest local maxima of the ECG. The best characterization of
frequency content of ECG signal is possible with optimally
selected wavelet filter.
Daubechies wavelet picks up the minute detail that is
missed by the other wavelet algorithm. Even if a signal is not
represented well by one member of daubechies family, it may
be efficiently represented by another.
In this paper, Db10 is used in order to study its effect on
the noisy ECG signal.
IV. DESIGN ALGORITHM
Step 1: Choose a wavelet
Step 2: Choose a decomposition level, up to N. In this paper
the decomposition level chosen is 4.
Step 3: Design of FIR lowpass filter
Step 4: Design of FIR highpass filter
Step 5: Downsample the output from step 3 and 4 by factor of
2.
Step 6: The output of step 5 is again passed through the
lowpass filter and highpass filter, followed by downsampling.
Step 6 is repeated up to the desired level of decomposition.
V. SIMULATION RESULTS
LOWPASS FILTER: The lowpass filter module in
instantiated in the top module. Depending upon the wavelet
decomposition tree, the module is instantiated.
The waveform in sky blue color is the input ECG signal and
red color waveform is the lowpass filter output
Figure 5: Waveform output from the Lowpass filter
HIGHPASS FILTER: The highpass filter is responsible for
removing of the noise from the ECG signal. The module is
instantiated in the top module, depending upon the desired
decomposition levels.
The yellow wave, in the figure 6 shows the ECG signal and
the sea green waveform shows the noise filtered out from the
ECG signal.
Figure 6: Noise removed output from the Highpass filter
WAVELET DECOMPOSITION TREE OUTPUT: In the
design, decomposition tree up to level 4 has been designed.
The input ECG signal is given through the test bench in the
form of ‘.txt’ file.
In figure 7 below, the signal in white is the input ECG signal
while the signal in pink is the wavelet output.

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Figure 7: Noisy free ECG signal
The input ECG signal does not contain considerable amount of
noise. So some amount of the noise is added in order to verify
the design.
The pink waveform in the figure 8, shows the noise added
ECG signal, while the original ECG signal is shown with help
of white waveform. The yellow waveform, which is the output
of the wavelet decomposition tree shows that the design is
able to remove considerable amount of noise.
Figure 8: Noise free ECG waveform at output, after noise
being added to it.
VI. RESULTS AND DISCUSSIONS
The design removes considerable amount of noise.
Wavelet transform (WT) is used to solve the problem of non-
stationary signal such as EGG.
Wavelet transform allows processing of ECG by using
multi resolution decomposing into sub signals. The benefit of
wavelet transformation lies in its capacity to highlight the
details of ECG signal with optimal frequency resolutions.
VII. FUTURE WORK
The proposed design can be implemented on FPGA. The
wavelet decomposition level can be increased in order to
remove noise significantly.
REFERENCES
[1] www.utp.edu.co/~andreses1/wavelets
[2] Sonu Bittoliya & R. P. “Intelligent Ecg Signal Noise
Removal With Moving Median Filter Using Neural
Network” International Journal of Research in Engineering
&Technology (IJRET), Vol. 1, Issue 3, Aug 2013 [--13770]
[3] C. Saritha,V. Sukanya, Y. Narasimha Murthy “ECG Signal
Analysis Using Wavelet Transforms” in proc. heron press
Ltd. ,2008 [bjp…]
[4] Aaushi Kumari, Megha Bisht “Analytical view of de-
noising methods in ECG signals” in Proc. International
Journal of Research in Computer and Communication
technology, Vol 1, Issue 5, October 2012.[73-131]
[5] Zainab N. Ghanim “ECG SLANTLET TRANSFORM
WITH FPGA DESIGN” in Proc. Journal of Engineering,
Volume 16, December 2010
[6] Manish Kansal, Hardeep Singh Saini, Dinesh Arora,
“Designing & FPGA Implementation of IIR Filter Used for
detecting clinical information from ECG” in Proc.
International Journal of Engineering and Advanced
Technology (IJEAT) ISSN: 2249 – 8958, Volume-1, Issue-
1, October 2011
[7] R.Sivakumar, R.Tamilselvi and S.Abinaya, “Noise
Analysis & QRS Detection in ECG Signals” International
Conference on Computer Technology and Science, vol. 47,
2012
[8] M.mRavi Kumar, “Electrocardiogram (Ecg) Signal
Processing On Fpga For Emerging Healthcare
Applications” International Journal of Electronics Signals
and Systems (IJESS), Vol-1 Iss-3, 2012
[9] Bhumika Chandrakar, O.P.Yadav, V.K.Chandra, “A
SURVEY OF NOISE REMOVAL TECHNIQUES FOR
ECG SIGNALS” International Journal of Advanced
Research in Computer and Communication Engineering,
Vol. 2, Issue 3,March 2013
[10] Leelakrishna. M, Selvakumar. J “Fpga Implementation Of
High Speed Fir Low Pass Filter For Emg Removal From
ECG” International Journal of Engineering Research &
Technology (IJERT), Vol. 2 ,Issue 5,May – 2013
[11] Kiran Kumar Jembula ,Prof. G.Srinivasulu, Dr. Prasad K.S,
“Design Of Electrocardiogram (ECG Or EKG) System On
FPGA” International Journal Of Engineering And Science ,
Vol.3, Issue 2 ,May 2013
[12] Upasana Mishra, Mr. Love Verma “ Noise Removal from
ECG Signal by Thresholding with Comparing Different
Types of Wavelet” International Journal of Application or
Innovation in Engineering & Management (IJAIEM),
Volume 3, Issue 3, March 2014
[13] Iffat Ara, Md. Najmul Hossain, S. M. Yahea Mahbub
“Baseline Drift Removal and De-Noising of the ECG
Signal using Wavelet Transform” International Journal of
Computer Applications, Volume 95– No.16, June 2014
[14] Rupali Madhukar Narsale, Dhanashri Gawali and Amit
Kulkarni “FPGA Based Design & Implementation of Low
Power FIR Filter for ECG Signal Processing” International
Journal of Science,Engineering and Technology Research
(IJSETR), Volume 3,Issue 6,June 2014

146 | P a g e
Professor Dr. Janak B. Patel is currently working as a Professor,
Electronics & Communication Engineering Department, ASET,
Amity University Haryana. He has done his Ph.D. from IIT
Roorkee. He has 22 years of industrial and teaching experience in
Engg. College. His areas of research are image processing and VLSI
Design.
Ms. Jagandeep Kaur received the B.Tech. degree in Electronics and
Communication Engineering from Apeejay College of Engineering,
Gurgaon of Maharishi Dayanand University, India in 2004. She has
received the M. Tech. degree in VLSI Design from NIT Kurukshetra
in 2009. She has 7 years of teaching experience. Her research
interests include analog, digital and mixed signal circuit design.
Nisha Charaya received the M.Sc. degree in Physics from
Department of Physics, Maharishi Dayanand University, India in
2007. She has received the M. Tech. degree in VLSI Design from
GJU, Hissar, India in 2010.She has 5 years of teaching experience.
Her research interests include Signal Processing, VLSI Design and
Embedded System Design.

ANALYSIS OF ECG WITH DB10 WAVELET USING VERILOG HDL

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ANALYSIS OF ECG WITH DB10 WAVELET USING VERILOG HDL