The document discusses the development of an adaptive noise canceller filter in MATLAB for electrocardiogram (ECG) signal analysis, focusing on various methodologies including band-pass filtering, adaptive filtering, and QRS detection using the Pan-Tompkins algorithm. It details the implementation of different filter types to enhance signal quality by eliminating noise and ensuring accurate detection of heart activity. The methodology aims to facilitate better clinical diagnosis by providing a more reliable ECG signal interpretation.

1. ADAPTIVE NOISE
CANCELLOR FILTER IN
MATLAB
Name -Vishav Singh Bandral
Roll No-20320424024
Mtech. EE
Desh Bhagat University
Under the Guidance of
Er. Kiranjit Kaur
Assistance Professor
EE Department

2. CONTENTS
• INTRODUCTION
• OBJECTIVES
• RESEARCH METHODOLOGY
• DESIGN A BAND PASS FILTER
• ADAPTIVE FILTERING
• QRS DETECTION TECHNIQUE USING PAN-TOMPKINS ALGORITHM

3. INTRODUCTION:-
• Electrocardiography is a medical topic that has piqued engineers' curiosity.
One of the most essential signals observed in heart patients is the
electrocardiogram (ECG). The electrocardiogram, or ECG, is an extremely
valuable medical device. The objective of an ECG is to assist clinicians in
quickly diagnosing human or animal heart activity and detecting aberrant
heart activities. The heart's job is to contract rhythmically, pump blood to
the lungs for oxygenation, and then return this oxygenated blood to the rest
of the body. The spread of electrical signals created by the heart
pacemaker, the Sinoatrial (SA) node, maintains and signals this precise
rhythm. Detecting such electrical activity in the heart can aid in the
detection of a variety of cardiac problems.

4. OBJECTIVES
• Rotary electric actuators can move the output shaft incrementally. In its
most basic form, a spinning actuator consists of a motor and a speed
reduction device. These AC and DC engines' voltage, frequency, strength,
and output may all be precisely regulated. The speed reducer is compared
to the needed speed, torque, and speed. The speed reducer's lifetime, duty
cycle, limit load, and accuracy are all elements to consider. Antifriction
bearings are commonplace on these speed reducers since they are
hardened, accurate spur gears. Compound gear reduction can be done in
both lightweight, multifunctional, and planetary forms, depending on the
load direction. The ambient temperature, angular rotation, torque, and rpm,
as well as control and input signals, are all part of the equation.

5. RESEARCH METHODOLOGY:-

6. DESIGN A BAND PASS FILTER :-
• A Band pass filter is created in this phase to filter out the noisy ECG signal.
The Band pass filter's specs are 20 Hz for the first stop band, 40 Hz for the
first pass band, 120 Hz for the second pass band, and 130 Hz for the second
stop band. 1000 Hz is the sampling rate. FDATOOL is used to create the
filter in MATLAB. The Kaiser window is employed. The windowing
method has its origins in signal processing, where the windowing operation
allows non-periodic signals to be analysed spectrally. It's easy to grasp
intellectually and to put into practise.

7. ADAPTIVE FILTERING :-
• Noise filtering, system identification, and speech prediction are all examples
of recent technological developments that use digital signal processing
(DSP). However, standard DSP techniques are insufficient to handle these
problems rapidly and with satisfactory results. To promote correct solutions
and a rapid convergence to that solution, adaptive filtering techniques must
be used. Adaptive filters can modify their impulse response to filter out
associated signals in the input signal. They don't require much or any prior
understanding of the signal and noise properties. Furthermore, adaptive filters
are capable of adapting. Signal tracking in non-stationary situations. The
error-performance surface is an adaptive filter that operates in a stationary
environment and has a consistent shape and orientation.

8. QRS DETECTION TECHNIQUE USING PAN-
TOMPKINS ALGORITHM

9. Hamilton and Tompkins (1985) described a real-time QRS detection
technique developed by Pan and Tompkins (1985). (1986). It
distinguishes QRS complexes based on slope, amplitude, and breadth
studies. The signal is sent through a band pass filter made up of cascaded
high-pass and low-pass integer filters to reduce noise. Differentiation,
squaring, and time averaging of the signal are the next steps.
(a) Band-Pass integer filter :-
• The band- pass filter for the QRS detection algorithm reduces noise in
the ECG signal by matching the spectrum of the average QRS
complex. It attenuates noise due to muscle noise, 60-Hz interference,
baseline wander, and T-wave interference.

10. The passband used is in the range of 20-40 Hz (7). The filter implemented in
this algorithm is a recursive integer in which poles are located to cancel the
zeros on the unit circle of the z plane. The filter implemented in this algorithm
is composed of cascaded high pass and low pass Butterworth IIR filters.
(b) Low pass filter:-
The filter has a rather low cut-off frequency of fc=40Hz, and introduces a delay
of 5 samples or 24ms. The filter provides an attenuation greater than 35dB at
60Hz. This Low pass filter effectively suppresses power-line interference from
the signal. The gain is 36. In order to avoid saturation, the output is divided by
32, the closest integer value to the gain of 36 that can be implemented with
binary shift arithmetic.

11. (c) High PASS Filter :-
• A high-pass filter (HPF) is an electronic filter that passes signals with a frequency
higher than a certain cutoff frequency and attenuates signals with frequencies
lower than the cutoff frequency. The amount of attenuation for each frequency
depends on the filter design. A high- pass filter is usually modeled as a linear
time-invariant system. It is sometimes called a low- cut filter or basscut filter in
the context of audio engineering. High-pass filters have many uses, such as
blocking DC from circuitry sensitive to non-zero average voltages or radio
frequency devices.
(d) Derivation Operator :-
• The derivative procedure suppresses the low frequency components of the P and
T waves, and provides a large gain to the high-frequency components arising
from the high slopes of the QRS complex. The derivative has a filter delay of 2
samples. The fraction 1/8 is an approximation of the actual gain of 0.1

12. (e) Squaring function :-
• The squaring operation makes the result positive and does non-linear
amplification of the output of the derivative operation. It also emphasizes
large differences resulting from QRS complexes, the small differences
arising from P and T waves are suppressed. The high frequency
components in the signal related to the QRS complex are further
enhanced.
(f) Setting a threshold value for R peak In this method components can
be removed using the process of thresholding that is by removing the
coefficients whose values are less than the value threshold. Threshold=
(max value – mean value) / 2 A 20 microvolts value is selected as a
threshold value.

13. (g) Averaging of Q to S value of only ECG signal :-
The ECG signal's Q to S values are averaged in this step to remove the
complete influence of noise signal and leave only pure ECG signal. Signals can
be averaged using either temporal or spatial methods.To begin, the signal of
interest must be consistent and repeatable. Second, the signal of interest must
be time-locked to a fiducial point that can be detected and acts as a timing
reference for the averaging process, such as the peak of the QRS complex.
Third, the signal of interest must be self-contained and remain such throughout
the averaging process. In this stage, the noise ECG signal's Q to S values are
averaged to remove the total influence of the noisy ECG signal, leaving only
pure ECG signal.

14. THANK YOU