TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 2, NO. 1, July 2009. (ISSN 0974-3375)

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Abstract—Various methods have been proposed and used forECG feature extraction with considerable percentage of correctdetection. Nevertheless, the problem remains open especiallywith respect to higher detection accuracy in noisy ECGs. In thiswork we have developed an algorithm based on wavelettransform approach using LabVIEW 8.6. LabVIEW (LaboratoryVirtual Instrument Engineering Workbench) is a graphicalprogramming language that uses icons instead of lines of text tocreate programs. Unlike text based programming language,LabVIEW uses the data flow programming, where the flow ofdata determines execution. The flexibility, modular nature andease to use programming possible with labview, makes it lesscomplex. The proposed algorithm is executed in two stages.Firstly, it preprocesses (denoises) the signal to remove the noisefrom the ECG signal. Then it detects P-wave, T- wave, QRScomplex and their onsets and offsets. LabVIEW and the relatedtoolkits, advanced signal processing toolkit and math script areused to build the graphical programme for both the stages. Thealgorithm is evaluated with ECG data files, with different ECGmorphologies and noise content taken from CSE multilead database which contains 5000 samples per ECG, recorded at asampling frequency of 500Hz.

Keywords: ECG, Feature Extraction, Wavelet Transform,LabVIEW.

I. INTRODUCTIONH Biomedical signals are fundamental observations foranalyzing the body functions and for diagnosing a widespectrum of diseases. The analysis of ECG has been

widely used for diagnosing many cardiac diseases. The ECGis a graphic record of the duration and magnitude of theelectrical activity that is generated by depolarization andrepolarization of the atria and ventricles. One cardiac cycle inan ECG consists of the P-QRS-T waves. Most of the clinicallyuseful information in the ECG is found in the intervals andamplitudes defined by its features (characteristics wave peaksand time durations). The development of accurate and quickmethods for automatic ECG feature extraction is of major

Manuscript received*Channappa.Bhyri is with the Department of Instrumentation Technology,

P.D.A.College of Engineering, Gulbarga, Karnataka, India (e-mail:channubhyri@ yahoo.com).

Kalpana.V. is with the Department of Instrumentation Technology,P.D.A.College of Engineering, Gulbarga, Karnataka, India (e-mail:kvanjerkhede@ yahoo.co.in).

S.T.Hamde is with the Department of InstrumentationEngineering,S.G.G.S. Institute of Engineering and Technology,Nanded,Maharashtra, India(e-mail:sthamde@sggs.ac.in). L.M.Waghmare is with the Department of InstrumentationEngineering,S.G.G.S. Institute of Engineering and Technology,Nanded,Maharashtra, India(e-mail:lmwaghmare@sggs.ac.in).

importance. The ECG feature extraction provides fundamentalfeatures (amplitudes and intervals) to be used in subsequentautomatic analysis.Producing an algorithm for the detection of the P wave, QRScomplex and T wave in an ECG is a difficult problem due tothe time varying morphology of the signal subject to thephysiological conditions and presence of noise. Thealgorithms in the relevant bibliography adapt a range ofdifferent approaches to yield a procedure leading to thedenoising and identification of the waves under consideration.These approaches are mainly based on wavelet [1-10],matched filters [11-12], Poisson transform [13], mathematicalmorphology [14], fuzzy logic [15-19], LabVIEW[20-22].From the bibliography[20-21],it is seen that LabVIEWcan be used effectively for QRS detection. In the workpresented here an algorithm is proposed with lowcomputational complexity using LabVIEW for filtering ECGsignals and detection of P wave, T wave, QRS wave and theironsets and offsets.

In a virtual instrumentation system, a computer includingcalculating, storing, replaying, accessing, displaying and filemanaging etc. are used which realize performing the functionsof traditional instrumentation via software. Therefore a VirtualECG Instrumentation system is a new Instrumentation system,which is much better and flexible with the aid of computerintelligent resource.

II. WAVELET TRANSFORMA wavelet is a mathematical function used to divide a givenfunction or continuous time signal into different scalecomponents. Usually one can assign a frequency range to eachscale component. Each scale component can then be studiedwith a resolution that matches its scale. A wavelet transform isthe representation of a function by wavelets. The wavelets arescaled and translated copies (known as daughter wavelets) ofa finite length or fast decaying oscillating waveform (knownas mother wavelet). Wavelet transforms have advantages overtraditional Fourier transforms for representing functions thathave discontinuities and sharp peaks for accuratelydeconstructing and reconstructing finite, non-periodic and/ornon-stationary signals. The mother wavelet is scaled by afactor of ‘a’ and translated (or shifted) by a factor of ‘b’ togive

abt

atba

1)(,

Wavelet transforms provide an approach to multi resolutionanalysis of a signal and this technique has been used to

Estimation of ECG features using LabVIEWChannappa Bhyri*, Kalpana.V, S.T.Hamde, and L.M.Waghmare

T

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identify the ECG signal features such as P wave, QRScomplex and T wave. The wavelet transform provides adescription of the signal in the time scale domain, allowing therepresentation of the temporal features of a signal at differentresolutions therefore, it is a suitable tool to analyse the ECGsignal, which is characterized by a cyclic occurrence ofpatterns with different frequency content. Moreover the noiseand artifacts affecting the ECG signal also appear at differentfrequency bands, thus having different contributions atvarious scales. A large number of known wavelet families andfunctions provide a rich space in which to search for awavelet, which will very efficiently represent a signal ofinterest in a large variety of applications. The choice of thewavelet function depends on the application. Selecting awavelet function, which closely matches the signal to beprocessed, is of utmost importance in the wavelet applications.Daubechies wavelet family is similar in shape to QRScomplex and their energy spectrum is concentrated aroundlow frequencies. Hence Daubechies WT is more suited forECG analysis.

III. DESCRIPTION 0F THE ALGORITHMThe algorithm presented in this section is applied directly atone run over the whole ECG signals which are available asdata files. The algorithm is divided into two parts.

A. Preprocessing of the ECG signal (Denoising).B. Feature extraction of the ECG signal.

A. Preprocessing of ECG signalThe recorded ECG signal is often contaminated by noise andartifacts that can be within the frequency band of interest andmanifest with similar characteristics as the ECG signal itself.In order to extract useful information from the noisy ECGsignal, we need to processes the raw ECG signal.Preprocessing ECG signals remove contaminants from theECG signals.

A.1.Removal of Baseline wanderingBaseline wandering usually comes from respiration atfrequency wandering between 0.15Hz and 0.3Hz and it can besuppressed by a high pass digital filter. Wavelet transform canalso be used to remove the baseline wandering by eliminatingthe trend of the ECG signal. The wavelet transform basedapproach is better because it introduces no latency and lessdistortion than the digital filter based approach [21].TheLabVIEW ASPT (Advanced signal processing toolkit)provides the WA Detrend virtual instrument which removesthe low frequency trend of the signal.

NtlevelTrend

2

2

log2log

Where t is the sampling duration and N is the number ofsampling points. The data used here has a sampling durationof 10 secs and 5000 sampling points in total, therefore thetrend level is 0.35 according to the above equation. Trend

level specifies the number of levels of the waveletdecomposition, which is approximately,

)(log*)1( 2 NleveltrendleveliondecompositofNumber

and for the data base we are using here it is 8. The WAdetrend virtual instrument has an option to specify the wavelettype used for the discrete wavelet analysis. The one selectedhere is Daubechies(db06)wavelet because this wavelet issimilar to the real ECG signal and also Daubechies waveletbeing orthogonal wavelet is suitable for signal denoisingwhere as biorthogonal wavelet is suitable for signal featureextraction.

A.2. Removal of wideband noiseAfter removing baseline wander the resulting ECG signal ismore stationary and explicit than the original signal. However,some other types of noise might still affect feature extractionof the ECG signal. The noise may be complex stochasticprocesses within a wideband, so one cannot remove them byusing traditional digital filters. To remove the widebandnoises, the Wavelet Denoise Express Virtual instrument isused here.

The Express Virtual instrument decomposes the ECG signalinto several sub bands by applying the wavelet transform andthen modifies each wavelet coefficient by applying a thresholdor shrinking a function and finally reconstructs denoisedsignal. From the threshold setting options available, softthresholding is selected and the thresholding rule selected is‘universal’ and the Virtual instrument sets the threshold tosqrt(2xlog(N)).The Virtual instrument offers an option toselect either discrete wavelet transform or undecimatedwavelet transform to denoise the signal. The transform typeused is undecimated wavelet Transform (UWT) to denoise theECG signal. The UWT has a better balance betweensmoothness and accuracy than the discrete wavelet transform.The VI diagram and the obtained waveforms are shown infigure 1(a) and 1(b).

B. Feature extraction

A For the purpose of diagnosis, we often need to extractvarious features from the preprocessed (denoised) ECG data,such as peaks of P,T,QRS waves and their onsets and offsetsof P,T and QRS waves.

Figure 1(a): VI diagram to remove baseline wander and noise

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Figure 1 (b): Original ECG, Base line wander removed signal and Denoisedsignal

B.1. Detection of peaks A well-chosen wavelet can extract main features from noisysignals by multiresolution analysis. Multiresolution expressvirtual instrument is used in this algorithm to decompose theECG signal by 8 level Daubechies(db06) wavelets and thenthe signal is reconstructed from the approximation coefficientsand the detail coefficients of all the frequency bands. Aftermultiresolution analysis peak/valley detection is done. WAmultiscale peak detection virtual instrument is used in peakdetecting mode to detect P, R, and T points by specifyingproper width and threshold. Width specifies the width innumber of samples. Threshold specifies the value, which thevirtual instrument uses to reject peaks of a particular size. Thepeak detector uses the following steps to detect the peaks.

1. Performs undecimated discrete waveletdecomposition with the biorthogonal wavelet on theinput signal.

2. Searches for zero crossing points in the detailcoefficients at all levels.

3. Selects the zero crossing points at the largest scaleas the coarse estimation of the real peaks.

4. Searches the fine scale for the correspondingnearest zero crossing point for each detected point.

5. Repeats step four until it reaches the finest scale,which is the first level.

In the proposed algorithm one peak detector is used for Rwave, another peak detector is used for P and T waves. TheVI diagram and waveforms are shown in figure 2(a) and 2(b).Once the R peaks are detected, with reference to this peak thesignal is scanned for 50 msec on the left side to get theminimum value and this corresponds to Q peak. Similarly theminimum value on the right side of the R peak corresponds toS peak. This procedure is implemented using mathscript.

Figure 2(a): VI diagram for multiresolution analysis and peak detection

Figure 2 (b): ECG after multiresolution analysis and ECG after peak detection

Mathscript is a LabVIEW tool for executing textualmathematical commands (or expressions). Mathscript node isused to evaluate scripts that are created in the LabVIEWmathscript window. The LabVIEW math script syntax issimilar to matlab language syntax.

B.2. Detection of onsets and offsets Once all the peaks are detected, these are taken as thereferences and an optimum window width is selected and thewaves are scanned on both the sides of the peaks to get thezero crossing points for obtaining onsets and offsets. If thesignal is not crossing zero line, then the minimum values inthat window are considered as onsets and offsets. Mathscriptis used to evaluate onsets and offsets. The peak valuesdetected using LabVIEW are carried to the mathscript node asinputs and zero crossing points or minimum values(onsets andoffsets) are taken as outputs from the mathscript node.

IV. RESULTS The detection performance of the proposed LabVIEW basedalgorithm is tested with different CSE multilead data files. Theresults obtained are given in the table 1. The results obtained

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TABLE 1. Results of P, QRS and T waves detection

Lead P-Wave Q-waveAmpin mv

R-waveAmpin mv

S-waveAmpin mv

QRSDurationin msec

T-Wave

Ampin mv

Durationin msecAmp

in mvDurationin msec

1L1

1L2

1L1_mv

1L2_mv

1L3_mv

1L4_mv

0.0270.0500.0500.0470.0450.0540.0510.0470.0470.0380.0010.0770.1720.0550.0780.0620.0620.0720.0630.0590.0160.0480.006

--

0.0250.0500.0470.0540.0520.0440.0430.0270.0390.0320.0560.0580.0570.0600.0550.0230.0090.0360.0350.0470.0230.0270.0060.0160.006

-0.032-0.044-0.012-0.022-0.003-0.040-0.055-0.052-0.059-0.056

7286849696909296967656

10417496

10610010610010096887418--

628086902294665244426270667476

12220

18813010086

12042327068743838126276788482

-0.115-0.066-0.058-0.095-0.052-0.054-0.103-0.058-0.069-0.065-0.279-0.119-0.195-0.129-0.116-0.122-0.116-0.126-0.122-0.140-0.089-0.073-0.078-0.084-0.082-0.059-0.038-0.052-0.075-0.070-0.096-0.079-0.100-0.070-0.079-0.068-0.065-0.073-0.060-0.069-0.008-0.028-0.029+0.005-0.007-0.014-0.035-0.028-0.005-0.0140.0820.0690.0920.0770.0820.0520.0440.0540.0630.065

1.1031.0831.0561.1031.0521.0811.1091.0141.0341.0670.6380.8830.8190.8190.9150.8730.8110.9230.8330.8730.5960.5890.6100.6160.5960.6100.6200.6050.6060.6130.5890.6070.6230.5940.6740.6120.6000.6250.5970.6030.0980.1530.1560.1290.1540.1160.1130.1050.1100.138

-0.604-0.608-0.627-0.596-0.632-0.619-0.620-0.626-0.610-0.604

-0.131-0.134-0.145-0.154-0.131-0.136-0.152-0.141-0.166-0.130 0.017-0.096-0.099-0.086-0.097-0.093-0.099-0.115-0.112-0.092-0.086-0.057-0.070-0.070-0.049-0.063-0.053-0.058-0.068-0.071-0.266-0.259-0.261-0.255-0.255-0.248-0.222-0.232-0.230-0.223-0.222-0.273-0.355-0.288-0.327-0.293-0.285-0.229-0.273-0.270 0.168 0.141 0.140 0.148 0.127 0.131 0.123 0.137 0.142 0.140

1601641621821921981821661801661961821901862062201742122122222102121821701942122021622102021081261121181261161261081341409210694140126108108102102106104102110100120110104136140130

0.0400.0210.0250.0240.0240.0260.0270.0360.029

-0.0430.0460.0810.0530.0370.0440.0430.0430.043

-0.1680.1480.2120.1930.1910.1660.1740.1600.1740.1650.1560.1730.2010.1720.1840.1690.1540.1590.1430.1510.0170.0300.0060.018

-0.0150.0250.0160.0170.009-0.167-0.167-0.214-0.189-0.193-0.175-0.170-0.165-0.163-0.163

18412814612413612216066

240-

9413014612810015013412276

-1101101781761501241161141201081281201501441441361281261241281602226858-

138170112186

-118114178144160130120120122116

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by this algorithm using LabVIEW for both denoising andfeature extractions of ECG signal are comparable withother algorithms reported in the literature. By using the WADetrend virtual instrument and Wavelet denoising expressvirtual instrument, baseline wandering and all the irrelevantnoise has been successfully removed from the signalwithout any additional filters.

V. CONCLUSION

The large variety of ECG feature extraction algorithms, andthe continuous efforts for their enhancement, proves thatuniversally acceptable solution has not been found yet.Difficulties arise mainly from the huge diversity of thewaveform the noise and artifacts accompanying the ECGsignals. The main advantage of this graphical programminglanguage is that, it provides a robust and efficientenvironment and tool for generating very fast, less complexand useful algorithms.

ACKNOWLEDGMENT

The authors would like to acknowledge the Department ofInstrumentation Engineering, S.G.G.S. Institute ofEngineering and Technology, Nanded and Department ofInstrumentation Technology, P.D.A.College ofEngineering, Gulbarga for providing the facilities to carryout this work.

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