Abstract of the body This signal is ranging from

Abstract –
Electrocardiogram (ECG) is a method of
measuring the electrical activities of heart. Every portion of ECG is very
essential for the diagnosis of different cardiac problems. But the amplitude
and duration of ECG signal is usually corrupted by different noises. Removing motion artifacts
from an electrocardiogram (ECG) is one of the important issues to be considered
during real-time heart rate measurements in health care. It is essential
to reduce these disturbances and improve the accuracy as well as reliability.
The noises that commonly disturb the ECG signals are Random noise, Gaussian
white noise, Power line interference, Baseline wander and Electromyography
(EMG) noise .These noises can be classified according to their frequency
content. The noise signals have been generated and added to the ECG signal
taken from MIT-BIH arrhythmia database. In this paper we have done a broader
study for denoising every types of noise involved with real ECG signal. One
adaptive filter, least-mean-square (LMS) 
is applied to remove the noises. PSNR and MSE performance parameter
are  estimated.

 

Keywords – ECG signal,
artifacts, adaptive filter, LMS algorithm

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  INTRODUCTION

ECG
is generated by the heart muscle and measured on the skin surface of the body
This signal is ranging from 10microvolts to 5 mille-volts, frequency from 0.05
Hz to 100 Hz.ECG is helpful to  detect
changes in cardiac muscles like myocardial infarction, conduction defects and
arrhythmia . When the electrical abnormalities of the heart occur, the heart
cannot pump and supply enough blood to the body and brain. As ECG is a
graphical recording of electrical impulses generated by heart, it is needed to
be done when chest pain occurred such as heart attack, shortness of breath,
faster heartbeats, high blood pressure, high cholesterol and to check the
heart’s electrical activity. It recognises the variability’s of heart activity,
so it is very important to get the ECG signal  free from noise.

 

 

 Basically ECG signal is characterized by five  peak points – P, Q, R, S, and T. The waveform which
 is repetitive and have various bumps and
parts of the waveform are designated as the P-wave, QRS complex and T-wave,
PR-segment, ST-segment, PR-interval and QT-interval as given in Figure 1. The
origins of these waves are:

i.
P wave: sequential activation (depolarization) of the right and left atria

ii.
QRS complex: right and left ventricular depolarization

iii.
T wave: ventricular repolarisation

iv.
U wave: repolarisation of the papillary muscles, rarely seen.

 

 

Fig.
1:ECG waveform

 

II.  NOISES IN ECG SIGNAL

 

ECG
Signals generated from human body are often very weak so as to be easily
covered by background noise. The noise in the ECG signals occur due to various
reasons like electromagnetic interference due to ubiquitous supply lines and
plugs, movement of patient, signals generated by other organs and impedance mismatching
between electrodes. Hence the ECG signals can be corrupted by various types of noises
such as Power line interference, Electrode contact noise, Motion artifact, Muscle
contraction, Base line drift, Instrumental noise generated by electronic
devices . Power line interference noise is electromagnetic field from the
powerline which causes 50/60Hz sinusoidal interference. This noise causes
problem in interpreting low amplitude waveform like ECG. Various methods have
been employed for the removal of artifacts from ECG signals. Adaptive filtering
is one of the efficient method in the removal of noises in the ECG signals.

 

III.GENERATION
OF NOISES

 

The low frequency noise (base line
wander, i.e. electrode contact noise and motion artifact) has frequency less
than 1Hz, high frequency noise (EMG noise) whose frequency is more than 100Hz
and power line interference of frequency 50 Hz or 60Hz (depending on the
supply) can be generated as follows. These noises are generated in MATLAB based
on their frequency content, which is then added with the ECG signal to get the
noisy ECG.

 

A .Generation of
Random noise

 

High
frequency random noise signal is generated. The generated high frequency noise
is shown in Figure 2.

 

 

 

Fig. 2: Random
noise signal

 

B.
Generation of  Gaussian White noise

               

                Generated Gaussian white noise is
shown in Figure 3.

 

 

Fig. 3: Gaussian
white noise signal

 

C. Generation of
 Baseline wander noise

 

Generated the baseline drift by  which is shown in Figure 4.

 

 

Fig. 4: Baseline
wander noise signal

 

D. Generation of
Power line interference noise

 

 We have
considered the 50 Hz power supply. So, we have taken a sine wave of 50 Hz
amplitude to represent the power line interference. The resulted power line
interference is shown in Figure 5.

 

 

Fig.
5: Powerline interference noise signal

 

 

 

 

IV. ADAPTIVE
FILTERING

 

ECG
signal has been a major diagnostic tool for the cardiologists and ECG signal
provides almost all the information about electrical activity of the heart. So
care should be taken while doing the ECG filtering, such that the desired
information is not distorted or altered in any way. The original ECG signal is taken from the MIT-BIH arrhythmia database. The different types of noise signal are generated by using MATLAB. The noise signal is then added with the real ECG signal. To remove the different types of noises, the noisy ECG signal is then pass through adaptive filter
algorithms (e.g., LMS). However, the basic block diagram  of adaptive filtering is shown in figure 6.

 

Fig.
6: Adaptive filter

 

In
this paper, we have designed a multistage filter for cancellation of these
artifacts. The filter consists of two stages(1st stage and 2nd stage) as shown
in Fig 7.

 

Fig.
7: LMS two stage filtering

 

Least mean squares (LMS) algorithms are a class of
adaptive filter used to mimic a desired filter by finding the filter
coefficients that relate to producing the least mean squares of the error
signal (difference between the desired and the actual signal). It is a
stochastic gradient descent method in that the filter is only adapted based on
the error at the current time. The LMS Algorithm consists of two basic
processes

 

1. Filtering process -Calculate the output of FIR
filter by convolving input and taps. Calculate estimation error by comparing
the output to desired signal.

2.
Adaptation process:-Adjust tap weights based on the estimation error.

Consider
a length L LMS based adaptive filter, that takes an input sequence x(n)
and updates the weights as

 

w(n + 1) = w(n) + ? x(n) e(n)

 

where
w(n) = w0(n) w1(n)….wL?1(n)t is
the tap weight vector at the nth
index,

 

x(n) = x(n) x(n?1)….x(n?L+1)t

 

e(n) = d(n)?wt(n) x(n)

 

with
d(n) being the so-called desired response available during
initial training period and ? denoting so-called step-size parameter.

In
order to remove the noise from the ECG signal, the ECG signal s1(n) with
additive noise p1(n) is
applied as the desired response d(n) for the adaptive filter. If
the noise signal p2(n),
possibly recorded from another generator of noise that is correlated in some
way with p1(n) is
applied at the input of the filter, i.e.,

 

x(n) = p2(n)

 

 The filter error becomes,

 

e(n) = s1(n) + p1(n) ? y(n)

 

 The filter output y(n) is
given by,

 

y(n) = wt(n)x(n)

 

Since the signal and noise are
uncorrelated, the mean-squared error (MSE) is,

 

Ee2(n) = E{s1(n) ? y(n)2} + Ep21(n)

 

Minimizing the
MSE results in a filter output that is the best least-squares estimate of the
signal s1(n).

 

 

 

V.
SIMULATION RESULTS

To
show that LMS algorithm is really effective in clinical situations, the method
has been validated using several ECG recordings with a wide variety of wave
morphologies from MIT-BIH arrhythmia database. The arrhythmia data base
consists of 48 half hour sets of two channel ambulatory ECG recordings, which
were obtained from 47 subjects including 25 men aged 32-89 years and women aged
23-89 years. The recordings were digitized at 360 samples per second per
channel with 11-bit resolution over a 10mV range. The generated noises in
MATLAB based on their frequency content, which is then added with the ECG
signal to get the noisy ECG. The output of the first stage is coupled to the
second stage where the noise free ecg signal can be obtained. We have considered
four different types of noises to corrupt our signal namely Power line Interference,
Baseline Drift, guassian noise and white noise. Results of LMS along with mean
square error (MSE) and peak signal –to-noise ratio are also shown. Six ECG
signal were obtained from this database to validate the results. The following
figures are the ECG signals corrupted by various noises.

 

 

Fig.
8: Pure ECG signal

 

 

Fig. 9: Noisy
ecg signal(random noise)

 

 

 

Fig.
10: Noisy ecg signal (Gaussian white noise)

 

 

 

Fig. 11: Noisy ecg
signal(baseline wander noise)

 

 

Fig.
12: Noisy ecg signal (powerline interference noise)

 

               

The two stage
adaptive filtering of the various noises is obtained as follows.

 

 

Fig.
13: LMS Result(of random noise)

 

 

Fig.
14: LMS Result(of Gaussian white noise)

 

 

Fig.
15: LMS Result(of Baseline wander noise

 

Fig.
16: LMS Result(of Powerline interfernce noise)

 

TABLE I. VALUES OF PERFORMANCE PARAMETERS
OF TWO STAGE ADAPTIVE FILTER FOR DIFFERENT TYPES OF NOISE

 

 
 
 
 
NOISES

 
RECONSTRUCTED SIGNALS

 
MSE

 
          PSNR

 
1ST
STAGE

 
2ND STAGE

 
1ST 
STAGE
STAGE
S

 
2ND STAGE

 
Random

 
0.0154

 
6.09e-10

 
16.1990

 
90.2149

Gaussian white
 

 
0.0148

 
1.33e-08

 
16.3748

 
76.7935

Baseline wander
 

 
0.0247

 
1.35e-08

 
14.1409

 
76.7579

Powerline interference
 

 
0.0038

 
9.06e-07

 
22.2214

 
58.4863

 

 

 

VI. CONCLUSION

 

In this paper, the problem of noise
cancellation from ECG signal using adaptive filters are proposed and tested on
real signals with different artifacts obtained from the MIT-BIH database. For
this, the input and the desired response signals are properly chosen in such a
way that the filter output is the best least squared estimate of the original
ECG signal.LMS algorithm work effectively in removing the noises from the ECG
signal.

 

 

 

 

 

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