international journal of modern electronics and ... khanpur kalan, sonipat, haryana bpsmv khanpur...

International Journal of Modern Electronics and Communication Engineering (IJMECE) ISSN: 2321-2152 Volume No.-4, Issue No.-4, July, 2016

RES Publication © 2012 Page | 29 www.ijmece.org

Analysis, feature extraction and compression of ECG signal

with DWT technique using NI-BIOMEDICAL WORKBENCH

& LABVIEW

Anju Malik Rajender Kumar

Department of Electronics & communication Engineering Department of Electronics & communication Engineering

BPSMV Khanpur Kalan, Sonipat, Haryana BPSMV Khanpur Kalan, Sonipat, Haryana

[email protected] [email protected]

Abstract - Lab VIEW and the signal processing-related toolkits can provide a robust and efficient environment and tools for resolving

ECG (Electrocardiogram) signal dispensation difficulty. This term paper demonstrate how to use these advance powerful tools in

denoising, extracting, analyzing, ECG signals simply and suitably not only in heart illness diagnosis but also in ECG signal

processing research. This paper presents study and analysis of ECG signal using LABVIEW (Advance signal processing toolkit as

well as biomedical workbench 2014). This paper also discuss on Heart rate monitoring and ECG signal compression using DWT

(discrete wavelet transform) technique. Data is imported from online data bank files, such as Physio bank MIT-BIH record to the

application in this tool kit for examination. The proposed algorithm is executed in two steps. In the first stage, ECG indication is

acquired which is after that followed by filtering the raw ECG signal to remove unwanted noises. Then the next stage focuses on

extracting the features from the acquired ECG indication then it detects heart rate, heart rate standard deviation, QRS amplitude, QRS

standard deviation, QRS width, PR-interval, QT-interval their onsets and offsets, as well as at last visualize and analyze the extraction

outcome.

Keywords: - ECG Signal, Feature Extraction, Discrete wavelet transform, NI-Biomedical workbench 2014 and LABVIEW.

I. INTRODUCTION

Human heart is divided into four main chambers called atria

and ventricles both with their left as well as right instances.

Those chambers together form a biological pump for

propelling the blood throughout the body. Moreover those four

observable sections there are several other parts of the heart

for very specialized functions like separating atria From

ventricles, slow inclination circulation, Very fast impulse

propagation etc. all of them performing particular tasks,

ensuring that blood flows suitably and efficiently all the way

through the body. When electrical impulse propagates during

heart and all these particular cells, ECG electrodes pick up that

Impulse in various directions and speed. In this way ECG

waveforms are formed [1-2]. The ECG signal is characterized

by five peaks and valleys labelled by the letters P, Q, R, S, T.

In various cases we moreover use another peak called U. The

normal heart rate is 60 – 100 beats per minute. Heart rate

slower than 60 beats per minute is called bradycardia as well

as a heart rate faster than 100 beats per minute is called

tachycardia.

Figure 1.1: ECG signal representation [4]



An ECG signal representation is shown in Fig.1.1. [4] The

main objective of data compression is to reduce the number of

bits so that it reduces the cost of conduction and increases

storage capability. The various sections of this paper are as

follows. Section 2 analysis of ECG signal. This is followed by

NI-Biomedical workbench ECG signal analysis and

compression in section 3. In last section, conclusion is drawn

about the result.

II. ANALYSIS OF ECG SIGNAL

The Lab VIEW Wavelet Analysis Tools give a collection of

Wavelet Analysis VIs that assists you in dispensation signals

in the LabVIEW environment. You can use the Continuous

Wavelet VIs, Discrete Wavelet Vis and Wavelet Packet VIs to

execute the continuous wavelet transform, the discrete wavelet

transform, the integer wavelet transform. The Wavelet

Analysis Tools contain Express VIs that provides interfaces

for signal processing and analysis. This Express VIs enables

you to identify parameters and settings for an analysis and

observe the results without delay. For illustration, the Wavelet

Denoise Express VI graphs both the original as well as

denoised signals. You can see the denoised signal instantly as

you choose a wavelet, identify a threshold, and set other

parameters. Analysis of ECG signal includes ECG signal

generation, feature extraction and pre-processing in ECG

signals.

Fig 2.1General steps for ECG Signal Analysis

A. Pre-processing

Pre-processing Electrocardiogram signals helps to eliminate

contaminants starting the ECG signals. Electrocardiogram

contaminants are confidential into the subsequent categories

[6]:

Power line interference

Patient–electrode motion artefacts

Electrode pop or contact noise

Baseline wandering

Electromyography (EMG) noise

Removing Baseline Wandering

The wavelet transform is an effectual way to remove signals

inside specific sub-bands. The Lab view ASPT provides the

WA Detrend VI which can take away the low frequency trend

of a signal.

Fig 2.2 Using the WA Detrend VI to remove baseline wandering

This process uses the Daubechies6 (db06) wavelet because

this wavelet is similar to the real ECG signal.

Fig 2.3 ECG Signal before and after removing baseline wandering

Noise Removal for Pre-processing

Detection of Peaks

Detection of onset offset of Individual

peaks

Estimation of ECG clinical signatures

Clinical diagnosis by physician

http://zone.ni.com/reference/en-XX/help/372656C-01/lvwavelettk/continuouswaveletpal/



http://zone.ni.com/reference/en-XX/help/372656C-01/lvwavelettk/discretewaveletpal/

http://zone.ni.com/reference/en-XX/help/372656C-01/lvwavelettk/waveletpacketpal/

http://zone.ni.com/reference/en-XX/help/372656C-01/lvwavelettk/wavelet_denoise/





B. Feature Extraction

For the intention of diagnosis, often we need to take out

various features from the preprocessed ECG data, including

QRS intervals, PR intervals, QRS amplitudes and QT

intervals, etc. These features give information about the heart

rate, the conduction velocity, the circumstance of tissues

within the heart as well as a variety of abnormalities [8].

Fig 2.4 Implementation of QRS Detection

Fig 2.5 Original ECG, ECG after Detrending, Denoising and QRS parameters

detection

The pre-processed ECG signal is used to identify position of R

impression. After that, all extra features determination is

extracted using innovative signal, because the signal

enhancement may transform these features [10].

Heart Rate monitoring

Fig 2.6 Back Panel for Heart Rate Monitoring

Fig 2.7 Front Panel for Heart Rate Monitoring

III. NI- BIOMEDICAL WORKBENCH ECG

SIGNAL ANALYSIS AND COMPRESSION

Fig 3.1 LABVIEW Biomedical Workbench 2014

The LABVIEW Biomedical Toolkit has the ability for

generate ECG signals from exterior files that (ECG data) can

be taken from MIT-BIH Arrhythmia Database.

1. ECG Feature Extractor

a. Imports ECG signals from different file types. See

Biosignal Viewer for file formats supported.

b. Imports ECG signals from phsiobank ATM (MIT-

BIH ECG database).

c. Integrates robust extraction algorithms to identify

ECG features, such as the QRS Complex, T wave

and P wave.

d. Saves ECG features to TDMS file.

e. Transfers RR distance data to HRV Analysis

application.

f. Exports ECG features reports for printing.



Fig 3.2 ECG Feature Extractor

Fig 3.3 ECG Feature Extractor with HR Histogram

Fig 3.4 ECG Feature Extractor Report

2. Heart Rate Variability (HRV) Analyzer

a. Imports RR intervals from an electrocardiogram

(ECG) file that the ECG Feature Extractor

application generates or from a text file that contains

RR intervals.

b. Provides a variety of analysis methods for HRV

analysis including Statistics (histogram), Poincare

plot, FFT (Fast Fourier Transform) spectrum etc.

c. Supports user-defined analysis methods.

d. Exports heart rate variability measurements report for

printing.

Fig 3.5 Heart Rate Variability Analyzer

Fig 3.6 HRV Report

ECG Compression

ECG compression techniques can be categorized into: 1)

direct time-domain techniques, 2) transformed frequency

domain techniques and 3) parameters optimization techniques

[11]

A. Direct Signal Compression Techniques

A direct technique performs the compression immediately on

the ECG signal. These are besides known as time domain

techniques. To obtain a high performance time domain

compression algorithm, intellectual sample collection criteria

should be used. This group includes AZTEC, TP and

CORTES, modified AZTEC algorithms. [11]

B. Transformed ECG Compression Methods Transform

method, changes the time domain signal to the frequency or

other domains and analyzes the power circulation. This group

includes dissimilar transform techniques such as the Fourier

transform, Cosine transform and further newly the wavelet

transform. [12]



C. Optimization Methods for ECG Compression

Optimization technique minimizes the renovation factual error

given a bound on the numeral of samples to be extracted or the

class of the reconstructed signal toward is achieved [11].

ECG Compression using Discrete Wavelet Transform:

Wavelets permit both time as well as frequency analysis of

signals at the same time because of the reality that power of

wavelet is determined in time and still possesses the signal

like characteristics [12-13].

Compression Algorithm: Step 1: Downloading of ECG

signal from MIT-BIH arrhythmia data base from Physiobank

ATM.

Step 2: Transform the original ECG signal using DWT.

Step 3: To achieve an adaptive threshold compute the

maximum value of the transformed coefficients.

Step 4: Apply the threshold of a fix noise based on absolute

maximum values of the transform coefficients.

Step 5: Apply inverse discrete wavelet transform to get the

reconstruct signal.

Step 6: Calculation of Signal to Noise ratio (SNR).

Fig 3.7 Block Diagram for ECG Compression using DWT Technique

Fig 3.8 Front Panel for ECG Compression using DWT Technique

IV. CONCLUSION

The advanced analysis scheme accessible on the workstation

is attractive invaluable to the practicing physician as well as

researchers. Clinical applications and investigate studies

simultaneously apply heart rate variability analysis results for

statistical and frequency methods. From the results it can be

concluded that as for by using the LABVIEW WA De trend

virtual instrument and Wavelet Denoise express VI,

wandering and all the irrelevant noise has been successfully

removed from raw ECG signal. The advantage of LABVIEW

(GPL) graphical programming language is that, it provides a

vigorous along with well-organized environment and tool for

generating very quick, less complex as well as useful

algorithms. From the ECG compression results it can be

concluded that as (SNR) signal to noise ratio is calculated to

compress error which yields high data reduction and poor

signal fidelity. For the future work the same data compression

algorithm is to be implemented in FPGA using Verilog HDL.

REFERENCES

[1] G. D. Clifford, F. Azuaje, and P. McSharry, Advanced Methods

And Tools for ECG Data Analysis. Norwood, MA, USA: Artech

House, Inc., 2006.

[2] A. Camm, T. L¨uscher, and P. Serruys, The ESC Textbook of

Cardiovascular Medicine. OUP Oxford, 2009.

[3] Fozzard HA, Haber E, Jennings RB, Katz AM, Morgan HI (eds.)

(1991): The Heart and Cardiovascular System, 2193 Total excitation

of the isolated human heart. Circulation 41 :( 6) 899-912.

[4] Sumi Thomas, Soniya Peter “ Study of Different ECG Signal

Compression Techniques” International Journal of Science and

Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value

(2013): 6.14 | Impact Factor (2013): 4.438

[5] Deepa Annamalai, S.Muthukrishnan “Study and analysis of ECG

signal using LABVIEW and Multisim” International Journal of pure

applied research in engineering and technology Research Article

ISSN: 2319-507X, IJPRET, 2014; Volume 2 (7): 26-34

[6] Juan Pablo Martinez, Rute Almeida, Salvador Olmos,,”A Wavelet

Based ECG Delinator: Evaluation on standard data bases”, IEEE



Transactions on Biomedical Engineering. 2004, Vol 51, No (4),570-

581

[7] Channappa Bhyri*, Kalpana.V, S.T.Hamde, and L.M.Waghmare

“Estimation of ECG features using LabVIEW” technia– International

Journal of Computing Science and Communication Technologies,

VOL. 2, NO. 1, July 2009. (ISSN 0974-3375)

[8] Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M.D., Eslami,

M. and Bidgoli, J.H. 2005. ECG Feature Extraction Based on

Multiresolution Wavelet Transform. Proceedings of the 2005 IEEE

Engineering in Medicine and Biology 27th Annual Conference

(Shanghai, China, September 1-4, 2005). 0-7803-8740-6/05/$20.00

©2005 IEEE.

[9] LabVIEW 2014 Biomedical Toolkit Help Edition Date: June

2014 Part Number: 373696B-01 »View Product Info June 2014,

373696B-01

[10] Jigar D. Shah, M. S. Panse, “EEG purging using LABVIEW

based wavelet analysis”, National Conference on Computational

Instrumentation CSIO Chandigarh, INDIA, pp.19-20, March ,2010

[11] Prof. Mohammed Abo-Zahhad,‖ ECG Signal Compression

Using Discrete Wavelet Transform‖, Vice-Dean for Graduate Studies,

Faculty of Engineering, University of Assiut, Egypt

[12] Mrs.S.O.Rajankar and Dr. S.N. Talbar, ―An Optimized

Transform for ECG Signal Compression‖, ACEEE Int. J. on Signal &

Image Processing, Vol. 01, No. 03, Dec 2010

[13] Ruqaiya Khanam and Syed Naseem Ahmad,‖ ECG Signal

Compression for Diverse Transforms‖, ISSN 2224-5758 (Paper)

ISSN 2224-896X (Online, Vol 2, No.5, 2012

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