The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
Detection and Elimination of Different
Types of Interference in ECG Wave
G. Divya Priya*
*Master of Engineering, Embedded System Technologies, Sri Shakthi Institute of Engineering and Technology, Anna University, Coimbatore,
Tamilnadu, INDIA. E-Mail: div1907{at}gmail{dot}com
Abstract—In this fast world, medical field requires improvement in the process. It must be fast and simple.
ECG monitoring plays a vital role in medical field. The PQRST waveform of the ECG signal is to be analyzed
to evaluate the performance of the heart and the contribution of each portion of the PQRST complex to sort out
the abnormal functioning of the heart through the study of different types of arrhythmia. This paper consists of
ECG wave detection and eliminating of EMG noise and other interference. Heart rate detection is used here to
detect the ECG wave based on discrete wavelet transform. It gives 100% performance even under NonOrdinary conditions with less interference and noise compared to the acquired signal using Matlab.
Keywords—ECG Signal; ECG Wave; Interference; NOTCH Filter; PQRST Waveform.
Abbreviations—Electrocardiography (ECG); Fast Fourier Transform (FFT); Recursive Least Squares (RLS);
Short Term Fourier Transform (STFT); Signal to Noise Ratio (SNR); Sino Atrial (SA).
I.
T
INTRODUCTION
ECHNOLOGY has brought many changes with the
increasing needs and thereby the medical field has
witnessed tremendous improvements in the last few
decades. In spite of this fact prevention is critical for
cardiovascular diseases and ECG is the most undisputed and
widely used tool to detect and diagnose them. The
electrocardiography deals with study of the electrical activity
of the heart muscles. The potentials originated in the
individual heart muscles are added to produce the ECG wave
pattern. The electrocardiogram reflects the rhythmic electrical
depolarization and depolarization of the myocardium
associated with the contractions of the atria and ventricles.
The shape, time interval and amplitude of the ECG give
details of the state of the heart. Electromyographic noises are
the significant factor in the analysis of the ECG waveform
[De Chazal & Reilly, 2003].
Apart from their enormous impact in older people life
expectancy, cardiovascular diseases are also the main cause
of death for the population among 44 and 64 years and
detecting their symptoms in time is critical to avoid
irreparable damages or death. Nevertheless, methods and
systems to acquire an ECG signal with good enough quality
in a fast and easy-to-use manner, so that they can be used in
domestic or other non-clinical environments, are nowadays
far from common. The main objective is to analyses the
performance of the PQRST complex based on heart rate
detection a by the application of discrete wavelet transforms
and to regenerate the ECG waveform by using efficient
filtering techniques using Matlab software [Cuiwei Li et al.,
ISSN: 2321 – 2403
1995; Arumugam, 2002; Korhonen & Parkka, 2003]. Hence
methods to reduce interference and to produce a better quality
ECG signal are discussed here.
II.
FUNCTIONING OF THE HEART
The heart is divided in to four chambers. The top two
chambers are atria and lower two chambers are ventricles.
The right atrium receives blood from the veins and pumps it
in to right ventricle. The right ventricle pumps the blood into
the lungs where it is purified and oxygenated. The oxygen
enriched blood enters the left atrium from which it is pumped
in to the left ventricle. Then the left ventricle pumps the
blood in to arteries through aortic valve for circulation
throughout the body.
Figure 1: Generation of ECG Signal
© 2014 | Published by The Standard International Journals (The SIJ)
10
The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
Sino Atrial (SA) node is situated in the wall of the atrium
and near the entry of the venacava, also called as cardiac
pacemaker and generate impulses at the normal rate of the
heart, about 70 beats per minute at rest. The rate is governed
by the autonomic nervous system being increased by the
sympathetic nerves and decreased by the parasympathetic
nerves. The action potential contracts the atrial muscle and
the impulse spreads through atrial wall during a period of
about 0.04 seconds to the Atria-ventricular node which is
located at the lower part of the wall between the two atria.
This node delays the spread of excitation for about
0.11seconds. Thus the AV node acts as a delay line to
provide timing between the action of the atria and ventricles
[Linh et al., 2003].
III.
RELATED WORK
The earlier method of ECG signal analysis was based on time
domain method. But this is not always sufficient to study all
the features of ECG signals. So, the frequency representation
of a signal is required. To accomplish this, FFT (Fast Fourier
Transform) technique is applied. But the unavoidable
limitation of this FFT is that the technique failed to provide
the information regarding the exact location of frequency
components in time. As the frequency content of the ECG
varies in time, the need for an accurate description of the
ECG frequency contents according to their location in time is
essential. This justifies the use of time frequency
representation in quantitative electro cardiology [Minami et
al., 1999].
The immediate tool available for this purpose is the Short
Term Fourier Transform (STFT). But the major draw-back of
this STFT is that its time frequency precision is not optimal.
Hence we opt a more suitable technique to overcome this
drawback.
Among
the
various
time
frequency
transformations the wavelet transformation is found to be
simple and more valuable [Piotrowski & Rozanoeski, 2010].
The wavelet transformation is based on a set of analyzing
wavelets allowing the decomposition of ECG signal in a set
of coefficients. Each analyzing wavelet has its own time
duration, time location and frequency band. The wavelet
coefficient resulting from the wavelet transformation
corresponds to a measurement of the ECG components in this
time segment and frequency band.
IV.
SPECIFICATION OF ECG
Amplitude:
P-wave—0.25mV
R-wave—1.60mV
Q-wave—25% R wave
T-wave—0.1 to 0.5mV
Duration:
P-R interval: 0.12 to 0.2
Q-T interval: 0.35 to 0.44s
S-T interval: 0.05 to 0.15s
ISSN: 2321 – 2403
P-wave interval: 0.11s
QRS interval: 0.09s
The normal value of heart beat lies in the range of 60 to
100 beats/minute. A Slower rate than this is called
bradycardia (Slow heart) and a higher rate is called
Tachycardia (Fast heart). If the cycles are not evenly spaced,
an arrhythmia may be indicated. If the P-R interval is greater
than 0.2 seconds, it may suggest blockage of the AV node
[Moraes et al., 2002].
1st degree AV block: Due to prolonged conduction time.
2nd degree AV block: Due to conduction of few pulses
instead of all from atrium.
3rd degree AV block: Due to asynchronous action of
atrium and ventricle.
Adams-stokes attack: Due to sudden attack of the total
block.
Bundle block: Due to improper conduction of the
stimulus to the ventricle.
Atrial Fibrillation: Due to fast beating rate (300500beats/min) of the atrium. Here ventricles beat very slowly
[Thong et al., 2004].
Ventricular Fibrillation: Due to fast beating rate of the
ventricles. No pumping of blood to different parts of the
body. Thus Electrocardiography can diagnose any form of
arrhythmia or disturbances in heart rhythm.
V.
METHODOLOGY USED
Normally four interference occurs in ECG signal. They are,
1.Baseline Wandering, 2. 50Hz power line interference,
3.Motion artifact, 4. Electromyogram. To remove this noise
following method is used.
Figure 2: Methodology
VI.
NOTCH FILTER
A Notch filter is used to eliminate the 50Hz power line
interference. It is filter that passes all frequencies except
those in a stop band centered on a center frequency. A closely
related Knowledgebase item discusses the concept of the Q of
a filter. This Knowledgebase item focuses on high Q notch
filters - the type that eliminates a single frequency or narrow
band of frequencies. A closely related type of filter – a band
reject filter, is discussed in a separate knowledgebase item.
The amplitude response of a notch filter is flat at all
frequencies except for the stop band on either side of the
center frequency. The standard reference points for the rolloffs on each side of the stop band are the points where the
amplitude has decreased by 3 dB, to 70.7% of its original
amplitude.
© 2014 | Published by The Standard International Journals (The SIJ)
11
The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
VII.
ADAPTIVE FILTER
An adaptive filter is a filter that self-adjusts its transfer
function according to an optimization algorithm driven by an
error signal. Because of the complexity of the optimization
algorithms, most adaptive filters are digital filters. Adaptive
filters are required for some applications because some
parameters of the desired processing operation are not known
in advance. The adaptive filter uses feedback in the form of
an error signal to refine its transfer function to match the
changing parameters [Hu et al., 1997; Reddy, 2005].
In a transversal filter of length N, as depicted in figure 2,
at each time n the output sample y[n] is computed by a
weighted sum of the current and delayed input samples
Figure 3: Adaptive Filter
has the following initial value
:
VIII. RLS ALGORITHM
RLS algorithm is proposed for removing artifacts preserving
the low frequency components and tiny features of the ECG.
Least square algorithm aim at the minimization of the sum of
the square of the difference between the desired signal and
the filter output. It gives excellent performance when
working in time varying environments.
Step 1: Calculates the output signal y(n) of the adaptive
filter.
The RLS algorithm consist of primary signal d(n) which
in this case is the ECG signal, secondary signal x(n) which in
this case is the power line noise. The filter produces an output
y(n) is given by,
Here, the c*k[n] are time dependent filter coefficients
(we use the complex conjugated coefficients c*k[n] so that
the derivation of the adaption algorithm is valid for complex
signals, too).
Step 2: Calculates the error signal e(n) by using the
following equation:
where δ is the regularization factor. The standard RLS
algorithm uses the following equation to update this inverse
correlation matrix.
The RLS (recursive least squares) algorithm is used to
remove the Baseline Wandering. The RLS algorithm is
algorithm for determining the coefficients of an adaptive
filter. The RLS algorithm uses information from all past input
samples (and not only from the current tap-input samples) to
estimate the (inverse of the) autocorrelation matrix of the
input vector. The RLS algorithm, whose convergence does
not depend on the input signal, is the fastest of all
conventional adaptive algorithms. This algorithm has less
computational complexity and good filtering capability.
Step 3: Updates the filter coefficients by using the
following equation:
where
is the filter coefficients vector and
gain vector.
is defined by the following equation:
is the
where
is the forgetting factor and P(n) is the inverse
correlation matrix of the input signal.
Figure 4: Block Diagram of RLS Algorithm
IX.
WAVELET TRANSFORM
The wavelet transform is a mathematical tool for
decomposing a signal into a set of orthogonal waveforms
localized both in time and frequency domains. It decomposes
signals as a superposition of simple units from which the
original signals can be reconstructed.
ISSN: 2321 – 2403
© 2014 | Published by The Standard International Journals (The SIJ)
12
The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
The basic Wavelet Transform has the following form:
where Ψ(t) is a mother wavelet function. It acts as a window
function to localize the integration. Notice that Ψ(t-b)/a is a
dilated and shifted version of the mother wavelet function; a
is the dilation factor and b is the translation factor. In the
Wavelet Transform, a one dimensional signal x(t) is mapped
to a two dimensional function Wx(a, b).
X.
DWT DECOMPOSITION
A Discrete Wavelet Transform (DWT) is any wavelet
transform for which the wavelets are discretely sampled. The
DWT decomposition produces coefficients, which are
functions of the scale (of the wavelet function) and position
(shift across the signal). We manipulate wavelet in two ways
viz., translation and scaling. In the translation the wavelet
along the time axis is shifted and adapts to slow down the
wavelet activity. In the scaling, fast activity, sharp spikes are
captured. In our approach we use three level discrete wavelet
transform. This is called compactly supported orthonormal
wavelets. Discrete Wavelet Transform (DWT) has two filters,
a low pass filter (LPF) and a high pass filter (HPF). They are
used to decompose the signal into different scales.
10.1. Scale Factor
The scale factor ā either dilates or compresses a signal. When
the scale factor is relatively low, the signal is more contracted
which in turn results in a more detailed resulting graph.
However, the drawback is that low scale factor does not last
for the entire duration of the signal. On the other hand, when
the scale factor is high, the signal is stretched out which
means that the resulting graph will be presented in less detail.
Nevertheless, it usually lasts the entire duration of the signal.
analysis the filter decomposes the signal into frequency
bands. In the wavelet synthesis the filter reconstructs the
decomposed signal back into the original bands as shown in
figure 5.
Four level discrete wavelet decomposition is performed
using different wavelet transforms. The wavelet transform
decomposes the ECG signal into different frequency scales
where the ECG characteristics waveforms are indicated by
zero crossings. The wavelet transform used in ECG signal
processing, breaks down the ECG signal into scales and
makes it easier to analyze the ECG signal in different
frequency ranges. The Signal to Noise Ratio (SNR) for the
various wavelets and at the various levels of decomposition is
calculated and compared.
XI.
R-WAVE EXTRACTION
The final signal (de-trended and de-noised) contains only
high amplitude spikes that denote the onset of R-waves. Then
by observing the average amplitudes of the R-waves, a
threshold voltage level (TP) is set up. The data sets, we
characterized, show that some noisy spikes come just after
the R-waves, which are approximately of same amplitudes as
R-waves. These may be due to noises from supply lines and
some other kinds of interferences. Generally, heart rate for a
normal adult varies within the range 60-120 beats/min. So
any noisy spikes that appear within 180 samples after the Rwaves are ignored and assumed. Before the occurrence of a
R-wave, the slope of the signal is positive and after the Rwave, the slope is negative. Again, any upward excursion that
exceeds the TP is taken as an R-wave. Thus, by calculating
two slope values, one upward & one downward, an R-wave
can be detected. Consecutive R-waves are detected using the
same technique. Thus, the heart rate is calculated
continuously as long as R-waves are encountered. If R-wave
is missing somewhere, the corresponding slope values can’t
be found [David Cuesta-Frau et al., 2002; Costas Papaloukas
et al., 2003; Surawicz & Knilans, 2008; Piotrowski &
Rozanoeski, 2010; Clapers Joan Gomez & Ramon Casanella,
2012].
Figure 5: DWT Decomposition
The output coefficients of the LPF are called
Approximation while the output coefficients of the HPF are
called Detail. The Approximation signal can be sent again to
the LPF and HPF of the next level for second-level
decomposition; thus we can decompose the signal into its
different components at different scale-levels. In the wavelet
ISSN: 2321 – 2403
Figure 6: Recorded ECG Waveform
© 2014 | Published by The Standard International Journals (The SIJ)
13
The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
Figure 7: ECG Waveform with Power Line Noise
Figure 11: R- Wave Extractions
XII.
Figure 8: ECG Waveform with Baseline Wander
CONCLUSION
Thus the generation of ECG signal is analyzed and a system
for processing real time ECG signals has been developed.
The notch filter removes the power line noise at 50HZ and
the adaptive filter removes the base line wandering using
RLS algorithm. Motion artifacts are also removed by the
application of continuous wavelet transform and R wave is
detected to calculate the heart beat.
In this detection process, the importance of using a
particular type of linear transform, wavelet transform has
been highlighted using which noise is filtered. After R-wave
extraction, heart rate has been calculated and based on the
heart rate arrhythmia is determined. Its all done by using
Matlab software which is used for processing the signal
efficiently and effectively. This method provided excellent
performance measures even at very high noise level.
REFERENCES
[1]
[2]
Figure 9: Output after Notch and Adaptive Filter
[3]
[4]
[5]
[6]
Figure 10: Output of Final Stage
[7]
ISSN: 2321 – 2403
Cuiwei Li, Chongxun Zheng & Changfeng Tai (1995),
Detection of ECG Characteristic Points using Wavelet
Transforms”, IEEE Transactions on Biomedical Engineering,
Vol. 42, No. 1, Pp. 21–28.
Y.H. Hu, S. Palreddy & W.A. Tompkins (1997), “Patient
Adaptable ECG Beat Classifier using a Mixture of Experts
Approach “, IEEE Transactions on Biomedical Engineering,
No. 44, Pp. 891–900.
K. Minami, H. Nakajima & T. Toyoshima (1999), “Real-Time
Discrimination of Ventricular Tachyarrhythmia with FourierTransform Neural Network”, IEEE Transactions on Biomedical
Engineering, Vol. 46, No. 2, Pp. 179–185.
David Cuesta-Frau, Juan C, Perez-Cortes, Gabriela Andrea
Garcia, Daniel Navak (2002), “Feature Extraction Methods
Applied to the Clustering of Electrocardiographic Signals. A
Comparative Study”, Proceedings of 16th International
Conference on Pattern Recognition, Vol. 3, Pp. 961–964
Dr.M. Arumugam (2002), “Biomedical Instrumentation”,
Anuradha Agencies.
J.C.T.B. Moraes, M.O. Seixas, F.N. Vilani & E.V. Costa
(2002), “A Real Time QRS Complex Classification Method
Using Mahalanobis Distance, IEEE Computers in Cardiology,
Pp. 201–204
Costas Papaloukas, Dimitrios I. Fotiadis, Aristidis Likas &
Lampros K. Michalis (2003), “Automated Methods for
Ischemia Detection in Long Duration ECG”, Cardiovascular
Reviews & Reports, Vol. 24, No. 6.
© 2014 | Published by The Standard International Journals (The SIJ)
14
The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 2, No. 1, January-February 2014
[8]
[9]
[10]
[11]
P. De Chazal & R.B. Reilly (2003), “Automatic Classification
of ECG Beats using Waveform Shape and Heart Beat Interval
Features”, Proceedings of the IEEE International Conference
on Acoustic, Speech and Signal Processing (ICASSP’03), Hong
Kong, China, Vol. 2, Pp. 269–272.
T.H. Linh, S. Osowski & M. Stodolski (2003), “On-Line Heart
Beat Recognition using Hermite Polynomials and Neuro-Fuzzy
Network”, IEEE Transactions on Instrumentation and
Measurement, Vol. 52, No. 4, Pp. 1224–1231.
I. Korhonen & J. Parkka (2003), “Health Monitoring in the
Home of the Future”, IEEE Engineering in Medicine and
Biology Magazine, Vol. 22, No. 3, Pp. 66–73.
T. Thong, J. McNames, M. Aboy & B. Goldstein (2004),
“Prediction of Paroxysmal Atrial Fibrillation by Analysis of
Atrial Premature Complexes”, IEEE Transactions on
Biomedical Engineering, Vol. 51, No. 4, Pp. 561–569.
ISSN: 2321 – 2403
[12]
[13]
[14]
[15]
D.C. Reddy (2005), “Biosignal Processing and its
Applications”, Tata McGraw Hill.
B. Surawicz & T. Knilans (2008), “Electrocardiography in
Clinical Practice: Adult and Pediatric”, Saunders.
Z. Piotrowski & Rozanoeski (2010), “Robust Algorithm for
Heart Rate
(HR) Detection and Heart Rate Variability
(HRV) Estimation”, Acoustic and Biomedical Engineering,
Vol. 118, No. 1, Pp. 131–135.
Clapers Joan Gomez & Ramon Casanella (2012), “A Fast and
Easy to Use ECG Acquisition and Heart Rate Monitoring
System Using a Wireless Steering Wheel”, IEEE Sensors
Journal, Vol. 12, No. 3, Pp. 610–616.
© 2014 | Published by The Standard International Journals (The SIJ)
15