Electronics Digital Signal Processing Report 2022

Electronics
Digital signal processing

Abstract:-
ECG signal is used to record the electrical activity of
the heart. These signals are of less amplitude and
affected by the noise, which includes the baseline
wander and power line interference. This study Aim
is to design the digital filters and implement them to
remove the unwanted noise. In this study, we create
filters such as Chebyshev filter design, Butterworth
Filter design, and Pole zero filter in the digital
domain. Responses of Different filters on the ECG
data are compared in terms of magnitude, stability,
and frequency response.
1. Introduction:-
The ECG (Electrocardiogram) is a significant bio-
electrical signal, which is used to diagnose various
diseases. The cardiologist used the ECG signals to
detect the cardiac system and heart conditions. ECG
is a graphical recording of the signal which displays
the time-varying voltage, which formed by the
myocardium throughout the cardiac cycle [4] .
During the diagnosis Cardiologist also looks for the
heart rate. In a digital signal, processing filters play
an essential role in removing the noise and unwanted
signal and extract the valuable information within
the specified frequency range. Many filters are used
to remove the baseline drift of the ECG signal. In
this study, we are used various types of digital filter
techniques to remove the noise. Digital Filters such
as Butterworth, pole-zero filter and Chebyshev are
used for the processing of the ECG signals [1].
Butterworth digital filter is used in an area in which
maximum passband flatness is needed. However,
other Chebyshev digital filter is used to optimize the
steeper roll-off. So these filters pass the ECG signal
without the attenuation [10]. Therefore these filters
are required for the conditioning of the analog signal
where distortion of signal is not needed.
1.2. ECG waveform Formation:-
Electrocardiogram contains the T wave, QRS
complex, and P wave. These waves produced by the
depolarization and depolarization of ventricles and
atria. The waveform of the ECG signal is showing in
the below figure.
Figure 1:- Represent the component of the ECG
waveform
1.3. Pre-processing Phase:-
Electrocardiogram signals are non-stationary signals
that include valuable information about the heart.
But information is degraded by the several noises
such as power line interference, motion artifacts,
baseline noise, electrosurgical noise, and
instrumental noise [9]. In ECG presence of noise can
distort the signal and provides less accuracy at the
output stage. The noisy signal comprises t of the
flattened ECG signal with high-frequency noise [6].
Therefore low-pass filter is used for the filter of the
signal. Low pass filter is generally used for the ECG
to reduce the high component of the noise, and to rid
of low-frequency vibration, adaptive filters are used.
Generally, the presence of noise will corrupt the
signal strength, for noise extraction digital filter is
used for the signal processing. Digital filters perform
various mathematical operations. The main goal of
the digital filter provides the desired magnitude
response |Hd (w)|, phase response ∠ Hd (w)|, and
tolerance specification how it is far from the ideal
output. Filter design related to the location of zeroes
and poles and the equivalent value of the filter
coefficient such as H (Z), H(w), and h|n|.Casual
filters have zero frequency response in the band of
the frequency. These filters have no sharp transition
between the stopband and the passband. Magnitude
response look likes as following.

Figure 2:- Magnitude Response of the Passband
and Stopband Filter
For designing the practical filter parameters such as
delta, angular frequency, and {ak},{bk} should
satisfying the all requirements of filters. We plot |H
(w)| by using dB (decibel), 20 log10 |H (w)|, and it
also expresses the ripple in Db. Stopband ripples do
not define for peak-to-peak value. Therefore a higher
magnitude response is essential in the stop-band
filter.
2 Designing of the digital filter by using
MATLAB
2.1Methodology:-
In this, we used a digital filter to remove unwanted
signal such as baseline wander and power line
interface. Baseline wander is an artifact of low
frequency in the ECG, which arises from breathing
[5]. In this methodology, various digital filter design
is used to obtain the output response such as Pole-
zero filter, Butterworth, and Chebbychev digital
filter.
3 Simulation and Results:-
MATLAB based simulation provides qualitative
examination for the performance of various filters.
The following part presents the MATLAB
simulation results of the different types of digital
filters used in this study. ECG signals were imported
from the ECG ID database.
Figure 3:- ECG Report from the database
Figure 4:-Original ECG Signal and FFT response
of the signal in the frequency domain
Figure 5: - Graph between the Transfer function
ZPK (Zero-pole gain) of the ECG signal
3.2.1 Butterworth Digital Filter:-It is the most
widely used filter to analyze the motion. It is an
elementary frequency-based filter, so it is speedy.
This filter is designed based on the flat frequency
response up to the passband. The roll-off of this
filter is slower as compared to the Chebyshev filter
[8].Because the Chebyshev filter provides the
steeper roll-off, so it requires a higher order to
implement specific stop -band parameters. LPB