Noise reduction in ecg by iir filters a comparative study

International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN

0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 4, July-August (2013), © IAEME

13

NOISE REDUCTION IN ECG BY IIR FILTERS: A COMPARATIVE

STUDY

Imteyaz Ahmad1, F Ansari

2, U.K. Dey

3

1Dept of ECE ,

2Dept of Electrical Engg. ,

3Dept of Mining Engg.

BIT Sindri, Dhanbad-828123, Jharkhand, India

ABSTRACT

Background: In monitoring mode only two leads are used so that ECG waveform has large

R wave amplitude so lead II is chosen. The monitoring mode bandwidth is 0.5-50 Hz as only

rhythmic information is required. The present paper deals with the digital filtering method

to reduce noise artifacts in the ECG signal. 4th

order Butterworth, Chebyshev 1,

Chebyshev 2 and elliptic filters are used to reduce noise interference from ECG signals.

Method: ECG signal is taken from physionet database. A ECG signal (without noise) is

added with 50 Hz interference, base line wander noise of .15 Hz and high frequency noise of

150 Hz and processed by low pass filter of cutoff frequency of 50 Hz, High pass filter of

cutoff frequency of 0.5 Hz and notch filter of 3 dB stop band bandwidth 0.2(49.9–50.1) Hz.

The order of filter is taken as 4. In this paper 4th order Butterworth, Chebyshev 1, Chebyshev

2 and elliptic filters are applied on the noisy ECG signal. Simulation results are also shown.

Comparison of these filters are done. All the designs are implemented using MATLAB FDA

tool.

Result: Performance of filters are analyzed by comparing signal power before and after

filtration and distortion to ECG waveform. It is found that digital filters works satisfactory.

Conclusion: 4th

order Butterworth filter gives best performance as compared to others as it

introduces minimum distortion to ECG waveform.

Key Words: Electrocardiogram, Butterworth, Chebyshev, elliptic and notch Filter.

INTERNATIONAL JOURNAL OF ELECTRONICS AND

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)

ISSN 0976 – 6472(Online)

Volume 4, Issue 4, July-August, 2013, pp. 13-25

© IAEME: www.iaeme.com/ijecet.asp

Journal Impact Factor (2013): 5.8896 (Calculated by GISI)

www.jifactor.com

IJECET

© I A E M E



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INTRODUCTION

The electrocardiogram is the graphic recording or display of time variant voltage

produced by the myocardium during Cardiac cycle. The electrocardiogram is used clinically

is diagnosing various diseases and conditions associated with the heart. It also serves as a

timing reference for other measurements.

Figure 1: ECG waveform

Engineers working in the medical profession are encouraged to learn as much as

possible about medical and hospital practices and in particular about physiology of human

body. It is only by gaining such an understanding that they can communicate intelligently

with medical professionals. This interaction between the two fields has led to the

development of sophisticated medical equipment and systems. In monitoring mode only two

leads are used so that ECG waveform has large R wave amplitude so lead II is chosen. The

monitoring mode bandwidth is 0.5-50 Hz as only rhythmic information is required. The

tracing of voltage difference at any two sites due to the electrical activity of the heart is called

a lead. Although two electrodes can be attached to any part of the body to lead the heart

current to the galvanometer, it is customary to make use of the forearms, the left leg and the

pericardium. Each chamber of the heart produces a characteristics electrocardiographic

pattern. Since the electrical potentials over the various areas of the heart differ, the recorded

tracing from each limb vary accordingly [1].



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Figure 2: The Einthoven triangle for defining ECG lead

ECG measurements may be corrupted by many sorts of noise. The ones of primary

interest are:• Power line interference• Electrode contact noise• Motion artifacts• EMG

noise• Instrumentation noise These artifacts strongly affects the ST segment, degrades the

signal quality, frequency resolution, produces large amplitude signals in ECG that can

resemble PQRST waveforms and masks tiny features that are important for clinical

monitoring and diagnosis. Cancelation of these artifacts in ECG signals is an important task

for better diagnosis.

While designing the ECG amplifiers bandwidth requirements should be considered

[2]. Van Alste JA, van Eck W, Herrmann OE has proposed the linear filtering method

for base line wonder reduction [4]. The time varying filtering is also proposed by Sornmo

L. for the reduction of the baseline wonder [5]. For the baseline wander filter presented

is a linear phase high-pass filter having a cutoff frequency lower than the heart rate [6].

Alarcon G, Guy CN, Binnie CD has applied the recursive butterworth filter for

reducing the noise contaminations [7]. Choy TT, Leung PM, has developed notch filter

ECG signal since its analog version is difficult to design [8]. Gaydecki P. has

described a simple but highly integrated digital signal processing system for real time

filtering of biomedical signals. Filters are realized using a finite impulse response; no

phase distortion is introduced into the processed signals [9].McManus CD, Neubert K

D, Cramer E, has compared filtering methods for elimination of AC noise in

electrocardiograms[10]. Cramer E te.al has given global filtering approach in which

two different filters are designed and are compared for power line estimation and removal

in the ECG [11]. Electromyogram (EMG) artifacts often contaminate the electrocardiogram

(ECG). They are more difficult to suppress or eliminate, compared for example to the

power line interference, due to their random character and to the considerable

overlapping of the frequency spectra of ECG. For filtering of electromyogram signal from

the ECG signal Christov II, Daskalov IK has given the solution by designing Low

pass digital filter of 35 Hz cutoff frequency[12]. Mahesh S. Chavan, R.A. Agarwala,

M.D. Uplane has given a comparative study of Butterworth, chebyshev 1, chebyshev 2 and

elliptic filter and analyzed the performance by comparing signal power before and after

filtration[13]. In this paper filter performance based on time and frequency domain analysis

was done.



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Input ECG: Input ECG ECG signal is taken from physionet ECG database with sampling frequency of 500

Hz as shown below in Figure 3. A ECG signal (without noise) is added with 50 Hz

interference, base line wander noise of .15 Hz and high frequency noise of 150 Hz is shown

in Figure 4.

0 1 2 3 4 5 6 7 8 9 10-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Time(s)

Amplitude(m

V)

Pure ECG

Figure 3: Input ECG signal with sampling frequency of 500 Hz

0 1 2 3 4 5 6 7 8 9 10-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6Noisy ECG

Time(s)

Amplitude(m

V)

Figure 4: Noisy ECG signal (contain 50 Hz interference, base line wander noise of 0.15 Hz

and high frequency noise of 150 Hz)

Design of low pass filter In the present paper all design is performed using Matlab FDA tool. Figure 5

shows basic Matlab model used in the filtration of the noise in ECG. Time scopes are

configured to store up to 5000 ECG samples. The 4th

order Butterworth low pass filter has

cutoff frequency of 50 Hz for monitoring mode. The magnitude response is flat and all poles

are inside the unit circle so design filter is stable. The phase response is nonlinear and

impulse response decay with time as shown in Figure 6.



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Figure 5: basic Matlab model used in the filtration of the power line noise in ECG

0 50 100 150 200

-120

-100

-80

-60

-40

-20

0

Frequency (Hz)

Ma

gn

itud

e (

dB

)

Magnitude Response (dB)

0 50 100 150 200

-6

-5

-4

-3

-2

-1

0

Frequency (Hz)

Ph

as

e (

rad

ian

s)

Phase Response

-1.5 -1 -0.5 0 0.5 1 1.5

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Real Part

Ima

gin

ary

Pa

rt

4

Pole/Zero Plot

0 10 20 30 40 50 60 70 80

-0.05

0

0.05

0.1

0.15

0.2

Time (mseconds)

Impulse Response

Am

plit

ud

e

Figure 6: magnitude response, phase response, pole-zero diagram, impulse response, step

response of the Butterworth low pass filter with cutoff frequency of 50 Hz



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Figure 7: time and frequency domain response of before and after filtration of low pass

Butterworth filter with cutoff frequency of 50 Hz



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Design of High pass filter The 4

th order Butterworth high pass filter has cutoff frequency of 0.5 Hz for

monitoring mode. The phase response is linear. Impulse response is 1 at t=0 and is 0 for rest

of time. Poles lies on unit circle of the z plane. Designed filter is stable.

Figure 8: the magnitude response, phase response, pole-zero diagram, impulse response of

the Butterworth high pass filter with cutoff frequency of 0.5 Hz

0 50 100 150 200

0

1

2

3

4

5

6

Frequency (Hz)

Ph

as

e (

rad

ian

s)

Phase Response

-1.5 -1 -0.5 0 0.5 1 1.5

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Real Part

Ima

gin

ary

Pa

rt

4

Pole/Zero Plot

0 1 2 3 4 5 6 7 8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (seconds)

Impulse Response

Am

plit

ud

e

0 50 100 150 200

-60

-50

-40

-30

-20

-10

0

Frequency (Hz)

Ma

gn

itud

e (

dB

)




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Figure 9: time and frequency domain response of before and after filtration of high pass

Butterworth filter with cutoff frequency of 0.5 Hz

Design of Notch filter 3- dB stop band bandwidth and the order of the filter were defined to design the

Butterworth notch filter. In the present case, order of the filter is 4 and the 3- dB stop

band bandwidth of 0.2(49.9–50.1)Hz were considered. Figure 10 shows the magnitude ,

phase response pole-zero diagram, impulse response, step response of the Butterworth

notch filter with the 3- dB stop band bandwidth of .2(49.9–50.1). The magnitude response

shows sharp cutoff at 50 Hz. The phase response is nonlinear. All zeros lies on the unit circle.

The zeros are located at ±0.6 radians.



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0 50 100 150 200-40

-35

-30

-25

-20

-15

-10

-5

0

Frequency (Hz)

Ma

gn

itud

e (

dB

)


0 50 100 150 200

-3

-2

-1

0

1

2

3

Frequency (Hz)

Ph

as

e (

rad

ian

s)

Phase Response

-1.5 -1 -0.5 0 0.5 1 1.5

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Real Part

Ima

gin

ary

Pa

rt

2

2

Pole/Zero Plot

0 5 10 15 20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (seconds)

Am

plit

ud

e

Impulse Response

Figure 10: the magnitude response , phase response, pole-zero diagram, impulse response

of the Butterworth notch filter with the 3- dB stop band bandwidth of .2(49.9–50.1)



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Figure 11: time and frequency domain response of before and after filtration of Butterworth

notch filter with 3- dB stop band bandwidth of .2(49.9–50.1) Hz

Simulation result

Butterworth Low pass filter

The time domain response shows that high frequency noise is considerably reduced

and amplitude of R wave is also reduced slightly. The frequency domain response shows that

high frequency noise is considerably reduced and ECG signal power before filtration of -

18.34 dB drops to -68.5 dB after filtration at 150 Hz as shown in Figure 7.



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Butterworth High pass filter The time domain response shows that low frequency noise i.e. baseline wander noise

is reduced to minimum. The frequency domain response shows that low frequency noise is

considerably reduced as shown in Figure 9.

Butterworth Notch filter The time domain response shows that ECG noise at 50 Hz is effectively reduced.

From frequency domain response ,the ECG signal spectrum before and after Butterworth

notch filtering with the 3- dB stop band bandwidth of .2(49.9–50.1)Hz shows power

reduction from -18.145 dB to -37.05 dB as shown in Figure 11.

Figure 12: shows noisy ECG, pure ECG, output of 4

th order Butterworth filter

Figure 13: shows noisy ECG, pure ECG, output of 4th

order Chebyshev 1 filter



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order Chebyshev 2 filter


order elliptic filter

CONCLUSION

4th

order Butterworth, Chebyshev 1, Chebyshev 2 and elliptic filters were designed

for sampling frequency of 500Hz. It is observed from time domain analysis of Figure 12,

Figure 13, Figure 14, Figure 15 that PQRST distortion in ECG waveform is lowest in

Butterworth filter as compared to other filters. In case of Butterworth low pass filter the

frequency domain response shows that high frequency noise is considerably reduced and

ECG signal power before filtration of -18.34 dB drops to -68.5 dB after filtration at 150 Hz

as shown in Figure 7.After low pass filtering this signal is applied to Butterworth high pass

filter to reduce baseline wander. From time and frequency domain response it is observed that

baseline wander is completely removed as shown in Figure 9.After this ECG signal is applied

to Butterworth notch filter for reducing 50 Hz noise. From time and frequency domain

response it is observed that 50 Hz noise is completely removed as shown in Figure 11. It is

observed that the signal power at 50 Hz before filtration is -18.145dB and after

filtration power is reduced from-18.145 dB to –37.05 dB. Simulation result shows that

while filtering the noise in ECG the PQRST segment of the ECG signal is modified.



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Noise reduction in ecg by iir filters a comparative study

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