Download Comparative Study Of FIR Digital Filter for Noise

Comparative Study Of FIR Digital Filter for
Noise Suppression in ECG Signal
.
Praveen Sharma
Electronics & Communication Department
Madhav Institute Of Technology, Gwalior
Madhya Pradesh, India
Abstract
Electrocardiogram (ECG), a non-invasive technique is used as a primary diagnostic tool for cardiovascular diseases. A cleaned
ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that
may occur. It provides valuable information about the functional aspects of the heart and cardiovascular system. This paper
deals application of the digital FIR filter on the ECG signal. In this notch filter are utilized. In this paper using 222txt ECG data set
from MIT-BIH arrhythmia database [8].
Keywords:222txt MLII ECG data signal, FDA tool, FIR filter.
1. Introduction:
Electrocardiogram (ECG) is a diagnosis tool that reported the electrical activity of heart recorded by skin electrode. The
morphology and heart rate reflects the cardiac health of human heart beat[1]. It is a non-invasive technique that means this
signal is measured on the surface of human body, which is used in identification of the heart diseases. Any disorder of heart
rate or rhythm, or change in the morphological pattern, is an indication of cardiac arrhythmia, which could be detected
by analysis of the recorded ECG waveform. The amplitude and duration of the P-QRS-T wave contains useful information about
the nature of disease afflicting the heart.
1.1 P wave:The amplitude level of this voltage signal wave is low (approximately 1 mV) and represent depolarization and
contraction of the right and left atria [2].A clear P wave before the QRS complex represents sinus rhythm. Absence of P waves
may suggest atrial fibrillation, junctional rhythm or ventricular rhythm. It is very difficult to analyze P waves with a high signal-tonoise ratio in ECG signal.
.
Fig.1. schematic representation of normal ECG waveform
1.2 QRS complex : The QRS complex is the largest voltage deflection of approximately 10–20 mV but may vary in size
depending on age, and gender. The voltage amplitude of QRS complex may also give information about the cardiac disease[6].
Duration of the QRS complex indicates the time for the ventricles to depolarize and may give information about conduction
problems in the ventricles such as bundle branch block.
1.3 T wave:Represents ventricular repolarisation[3].
2. Digital FIR filter:
2.1 Design of FIR Equi-ripple Low pass Filter:
Power line interference is one of the reasons of corruption of the ECG signal[9]. Mostly it causes the 50 Hz interference and
there higher order harmonics gets added to the ECG. This section deals with the design and implementation of the FIR digital
filter to the ECGsignal.The results are presented using the frequency spectrum of the ECG signal before and after filtration.
2.1. Design of Equi-ripple filter:
In this design the minimum order of the filter selected, the sampling frequency of 360 Hz , pass band frequency 54 Hz, stop band
frequency 60 Hz, pass band attenuation 1dB, stop band attenuation 80 dB and density factor 20. Whichrepresents the response
shown below in figures[3][10].
Figure 1.Arrhythmia ECG signals.
Figure 2.Arrhythmia ECG signal base line.
Figure3.Magnitude Response of the Equi-ripple LPF.
Figure 4.Phase Response of the Equi-ripple LPF.
Figure 5.Impulse Response of the EquirippleLPF.
Figure 6. Pole Zero diagram of the Equiripple LPF.
2.2 Implementation of Equiripple notch filter:
To get the real time filtration the model in MATLAB is built. The parameters of the model are set to get real time filtration of the
ECG signal. Figure shows 7 model used for the real time Equiripplefiltration.The sampling frequency used in the present work is
360 Hz.
3. Result of implementation of the equi-ripple low pass FIR filter and Kaiser window FIR filter:
In the equi-ripple low pass filter and Kaiser window. We are taking same value of the pass band frequency, stop band frequency,
pass band attenuation, stop attenuation and minimum order.
Figure8. ECG data signal after filtering with equripple low pass filter
Figure 9. ECG data signal after filtering with Kaiser window
Table1.
Before Filtering
mean
Standard deviation
Signal to noise ratio
Arrhythmia signal
0.1367
7.9796
0.01713
After Filtering
mean
Standard deviation
Signal to noise ratio
With Kaiser window
With Equiripple filter
-0.047
0.006
0.2116
0.2162
-0.2221
0.0277
Table 2.
4. Conclusion :
Figure 8 and 9 shows that noise has been reduced when ECG signal filtered using FIR filter results have been concluded from
Kaiser window filter and equi-ripple low pass filter. Table 1 shows signal to noise ratio before filtering and Table2 shows signal to
noise ratio after filtering with Kaiser and equi-ripple filter.
References
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Francisco,
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,W.B.Saunders,1996.
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signal
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neurological
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filter.IEEE(2010).
6. http://en.wikipedia.org/wiki/Electrocardiography.
7. E. Farahabadi, A. Farahabadi, H. Rabbinic,: Noise Removal from Electrocardiogram Signal
Employing an Artificial Neural Network in Wavelet Domain. IEEE (2009).
8. Htt://www.physionet.org/physiobank/database/PTB.
9. G.M.Friesen,T.C.Jannett,M.A.Jadallah,S.L.Yates,S.R.Quint,andH.T.Nagle,“Acomparisonof
thenoisesensitivityofnineQRSdetectionalgorithm,”IEEETrans.Biomed.Eng.Vol.37,pp. 85–98,1990.
10. Mahesh,S.,Agarvala,A.,Uplane,M.,:Design and implementation of digital FIR filter equiripple notch
filter on ECG signal for removal of power line interference.Springer(2008).