Special Issue - 2018 International Journal of Engineering Research & Technology (IJERT)

ISSN: 2278-0181
PECTEAM - 2K18 Conference Proceedings

Portable ECG Electrodes for Detection of Heart

Rate and Arrhythmia Classification
1
K. Jeeva, 2 Dr. D. Selvaraj, 3Dr. S. Leones Sherwin Vimal Raj
1
PG Student, 2, 3 Professor, Department of Electronics and Communication Engineering
Panimalar Engineering College,Chennai, India

Abstract— Long term continuous monitoring of between QRS complexes. QRS complex can be detected using
electrocardiogram (ECG) in a free living environment provides for example algorithms from the field of artificial neural
valuable information for prevention on the heart attack and networks, genetic algorithms, wavelet transforms or filterbanks.
other high risk diseases. This paper presents the design of a Moreover the next way how to detect QRS complex is to use
real-time wearable ECG monitoring system with associated adaptive threshold . The direct methods for heart rate detection
cardiac arrhythmia classification algorithms. However, these are ECG signal spectral analyse and Short-Term
techniques are severely hampered by motion artifacts and are Autocorrelation method. Disadvantage of all these methods is
limited to heart rate detection. To address these shortcomings their complicated implementation to microprocessor unit for
we present a new ECG wearable that is similar to the clinical real time heart rate frequency detection. Real time QRS
approach for heart monitoring. Our device weightless and is detector and heart rate computing algorithm from resting 24
ultra low power, extending the battery lifetime to over a month hours ECG signal for 8-bit microcontroller is described in. This
to make the device more appropriate for in-home health care algorithm is not designed for physical stress test with artefacts.
applications. The device uses two electrodes activated by the The designed digital filters and heart rate frequency detection
user to measure the voltage across the wrists. The electrodes
algorithms are very simple but robust. They can be used for
are made from a flexible ink and can be painted on to the
ECG signal processing during physical stress test with muscle
device casing, making it adaptable for different shapes and
artefacts. They are suitable for easy implementation in C
users. Also show the result of heart rate of beats per minute
(bpm) based on the R-R interval (peaks) calculation. That
language to microprocessor unit in embedded device. Design of
means whether the heart function is normal or abnormal these methods has been very easy with Matlab tools and
(Tachycardia, Bradycardia). functions.

Keywords- Tachycardia; ECG; Bradycardia; butterworth;

A. Signal acquisition
I. INTRODUCTION
ECG signal for digital signal processing and heart rate
Electrocardiogram (ECG) represents electrical activity of calculation was acquired by measurement card with
human heart. ECG is composite from 5 waves - P, Q, R, S sampling frequency fs = 500 Hz. The first ECG lead was
and T. This signal could be measured by electrodes from measured. Analogue signal pre-processing was done on
human body in typical engagement. Signals from these simple amplifier circuit designated for ECG signal
electrodes are brought to simple electrical circuits with measurement. The circuit with ECG amplifier is fully
amplifiers and analogue – digital converters. The main described in. there is shown raw ECG signal sampled by
problem of digitalized signal is interference with other noisy measuring card. This signal was used as input signal for the
signals like power supply network 50 Hz frequency and digital filters and the heart rate detection algorithms
breathing muscle artefacts. These noisy elements have to be designing and testing.
removed before the signal is used for next data processing
like heart rate frequency detection. Digital filters and signal B. Digital signal processing with digital filters
processing should be designed very effective for next real- The main noise elements are power supply network 50
time applications in embedded devices. Heart rate frequency Hz frequency and breathing muscle movements. These
is very important health status information. The frequency artefacts have to be removed before the signal is used for
measurement is used in many medical or sport applications next data processing like heart rate frequency determination.
like stress tests or life treating situation prediction. One of The block schema of digital signal processing with digital
possible ways how to get heart rate frequency is compute it filters.
from the ECG signal. Heart rate frequency can be detected d
from ECG signal by many methods and algorithms. Many
algorithms for heart rate detection are based on QRS
complex detection and hear rate is computed like distance

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 Special Issue - 2018 International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
PECTEAM - 2K18 Conference Proceedings

Tompkins and Hamilton-Tompkins algorithms; feature

extraction from the detected QRS complexes, and
classification of the beats extracted from QRS complexes
using Back Propagation Neural Network (BPNN). The
application model was developed for ECG signal
classification under ‘Normal’ or ‘Abnormal’ heartbeats to
detect cardiac arrhythmia in the ECG signal[20]. The model
was trained with standard arrhythmia database of
Massachusetts Institute of Technology Division of Health
Science and Technology/Beth Israel Hospital (MIT-BIH),
and taking into account the Association for the Advance of
Figure 1. Digital signal processing and digital filters Medical Instrumentation (AAMI) standard.
The performance of the developed application model for
At the beginning, the mean value is removed from signal. classification of ECG signals was investigated using the
Then signal is normalized for unit maximum amplitude. MIT-BIH database. The accuracy of detection and
Network interference at 50 Hz frequency is removed by first extraction of the signal components and features (based only
filter. This filter type is biquad band stop. Advantage of this on the MIT-BIH database used) shows that the developed
filter is very narrow band stop which is created by poles and application model can be employed for the detection of heart
zeros location. Baseline wander was provided by means of the diseases in patients. Detection and delineation of
next filter. This filter is second order Butterworth filter set to Electrocardiogram has played a vital role in cardiovascular
frequency of 0.5 Hz. This filter is usually used in professional monitoring systems.
ECG filtering applications.
The enormous database of heart beats which characterize
II. RELATED WORK the heart disease, uncertainity, randomness in occurrence of
these beats necessitate the use of Rough set theory. Over the
Nowadays, people are getting more and more concern on years Rough set theory has been effectively used for
their own fitness conditions and it has become a digital removal of uncertainties and reduction of dataset .This paper
healthy lifestyle movement. A basic activity tracker has discusses an optimized rough set based algorithm for
given many data on the user’s movement to fulfill their daily detection of fiducial points for ten classes of ECG .Fiducial
goal of calories burned. An advance tracker can also points help determine the peaks, valleys, onset and offset of
measure heart rate with accurate activity intense level. the waves. Ten morphological features have been identified
However, consider if these fitness devices can be used for and investigation of efficiency of Rough set theory to reduce
clinical diagnosis by adding an ECG, then it can be a daily and extract the decision rules from the database has been
health monitoring or a health assisted device by connecting done.
it to the internet via a smartphone.
The experimental results show that the proposed method
To use an ECG as a wearable device, the electrode has sensitivity 48%; average specificity 96% and average
positions has to fulfill the clinical placement[1]. A new detection accuracy 91%.Methods involving the use of
biomedical electrodes placement is proposed in this paper to evolutionary algorithms have also been a powerful tool for
meet the practicality of a fitness lifestyle device but has a dealing with complex optimization problems[9]. Rough-fuzzy
medical ECG result for continuous heart monitoring in a approach accompanied with Ant colony optimization ,Particle
form of a necklace. The device has a single lead ECG swarm optimization and Genetic algorithm as search methods
analog front end that is connected to an ARM-Cortex M4 has also been studied. The results obtained by integrating
microcontroller. It uses a 4 GB memory card, rechargeable Multilayer Perceptron or Fuzzy-Rough neural network with
battery, and a Bluetooth Low Energy 4.0 to communicate fuzzy rough approach for attribute selection as well has shown
with an Android 4.3 smart phone. the highest accuracy of around 96%.
The test results were taken from a 32-year-old male
subject with normal heart condition. The signal acquired III. PROPOSED MODEL
from the electrode placement at the backside of the neck
shows Lead I waveform with 10% from the normal position Heart Rate is the number of times that our heart contracts or
amplitude value. The R-wave of every heartbeat can be seen beats in a minute. Main function of the heart is to maintain
for heart rate calculation. Therefore, it is able to do a daily adequate blood supply [Heart sends oxygenated blood to the
heart monitoring with a lifestyle device.Electrocardiogram body so that tissues could extract the oxygen for their use].
(ECG) is a graphic recording of the electrical activity Therefore the heart rate varies according to demands of body.
produced by the heart. The normal heart rate at rest for healthy adults, including older-
The accuracy of any electrocardiogram waveform aged adults and kids >10 years is between 60 and 100
extraction plays a vital role in helping a better diagnosis of heartbeats a minute. Resting heart is the heart rate is the rate at
any heart related illnesses[2]. We present a computer-aided which our heart beats when we are resting or relaxed. When we
application model for detection of cardiac arrhythmia in say normal heart rate, we mostly refer to resting heart rate.
ECG signal, which consists of signal pre-processing and With exertion, our heart rate goes up as the demand for oxygen
detection of the ECG signal components adapting Pan- increases and heart, by upping the rate of

Volume 6, Issue 02 Published by, www.ijert.org

 Special Issue - 2018 International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
PECTEAM - 2K18 Conference Proceedings

beating [and pumping blood] tries to meet the demand. Data acquisition is the process of sampling signals that
Another thing that happens is increased breath rate because measure real world physical conditions and converting the
lungs function to extract more oxygen from the inhaled air. resulting samples into digital numeric values that can be
Anxiety, fear, surprise also lead to increase in the heart rate. manipulated by system.
This is caused by release of adrenaline or epinephrine in the Data acquisition system typically convert analog
body, preparing us for fight or flight. The normal heart rate waveform into digital form for easy processing.
undergoes healthy variation, going up in response to some Data acquisition systems is advantage as we can store a
conditions, including exercise, body temperature, body lots of physical condition data in digital form.
position (such as for a short while after standing up quickly), Microcontroller
and emotion (such as anxiety and arousal). The electrode is interfaced with microcontroller. The
signals from the body is taken by the electrodes, the signals
are very weak hence it is given to the amplifier.
A. Heart rate and Pulse
Pulse is defined as the number of times arteries expand The front-end for the signal acquisition system is an
and contract in response to the heartbeat. This rate is exactly instrumentation amplifier. It has a very high common mode
equal to the heartbeat, the rate of heart contractions, because rejection ratio (CMRR) and high input impedance which is
these heart contractions cause the increases in blood required for capturing ECG signals.
pressure and the pulse in the arteries. Pulse, therefore, is a Along with the signal noise also gets amplified, this noise is
direct measure of heart rate. removed by band pass filter. Since the acquired signals are
weak it is given to the buffer amplifier. The signals are
The pulse volume may be affected by changes in the
digitized using Analog to Digital Converter(ADC).
arteries. Absence of pulse may indicate a problem in the
vessel. Then it is given to USB for mat lab processing.

ECG HARDWARE UNIT IV. SIMULATION RESULTS

This example shows how to detect the QRS complex of
ECG
MICRO electrocardiogram (ECG) signal in real-time. Model based
CONTROLLER
design is used to assist in the development, testing and
P.SOURCE
ACQUISITION
UNIT
ECG
deployment of the algorithm.
probe UARTHGG UART The electrocardiogram (ECG) is a recording of body
USB surface potentials generated by the electrical activity of the
heart. Clinicians can evaluate an individual's cardiac
MATLAB PROCESS condition and overall health from the ECG recording and
perform further diagnosis.
A normal ECG waveform is illustrated in the
following . Because of the physiological variability of the
QRS complex and various types of noise present in the real
ECG signal, it is challenging to accurately detect the QRS
complex.
The Noise sources that corrupt the raw ECG signals
include:
Figure 2. Block
diagram for heart rate Baseline wander
Power line interference (50 Hz or 60 Hz)
Hardware Description Electro myographic (EMG) or muscle
ECG probe noise Artifacts due to electrode motion
The ECG electrode is fixed on the module, that electrode Electrode Contact Noise
will continuously monitor the Heart rate.
The simulation results are in the heart rate calculator in
The ECG(electrocardiogram) Records the heart's matlab algorithm. In this project matlab 2013a is used. In
electrical activity: Heart beat rate, Heart beat rhythm, Heart this we get the result are using butterworth filter for
strength and timing. removing the noise.
The ECG Electrode is Lead and the signal recorded as A. ECG Waveform and ECG FFT Waveform
the difference between two potentials on the body surface is
called an "ECG lead". Each lead is said to look at the heart The figure 3 shows ECG waveform and ECG FFT
from a different angle. waveform for heart rate calculation and peak detection.
The ecg signals from the electrodes are fed into
microcontroller and pass the information to mat lab
processing.

Data Acquisition

Volume 6, Issue 02 Published by, www.ijert.org

 Special Issue - 2018 International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
PECTEAM - 2K18 Conference Proceedings

D. Peak Detector
The figure 6 shows peak detector waveform for heart rate
calculation and peak detection.

Figure 3. ECG waveform and ECG FFT waveform

B. .Total Filtered Signal

The figure 4 shows total filtered signal waveform for Figure 6. Peak Detector
heart rate calculation and peak detection.
These are the simulation results of peak detector and
heart rate calculator by using the butterworth filter for
removing the noise.

V. CONCLUSION
Combined use of MATLAB and Simulink is very useful
in ECG signal analysis. Different digital filters are used in
simulink to remove noise from raw ECG signal. The noise
free ECG signal obtained from filter circuit is used as input
for ECG analysis to find various intervals and peaks in
MATLAB environment. Many works are done in the field
Figure 4. Total Filtered Signal of ECG analysis and they involve complicated calculations
and hence difficult to design.
C. ECG Conditioned Result The algorithm used in this work is very efficient and
The figure 5 shows ECG conditioned result waveform simple, so it can be easily implemented on ECG signal. In
for heart rate calculation and peak detection. this case the waveform is divided into positive and negative
parts and each section is analyzed separately. Various peaks
are detected by finding local maxima and minima of the
signal and then setting minimum threshold limit for them
according to the standard values.
The results obtained can be used for clinical diagnosis by
the physician and will be very helpful in finding various
abnormalities in the heart.
This paper using the Butterworth filter. This Butterworth
filter used to remove the noise like interferences and while
using electrode that paste also a one type of noise ,so avoid
this type of noise this project using the Butterworth filter. In
future using the electrodes we get real time ecg signal after
that calculate the heart rate.
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 Special Issue - 2018 International Journal of Engineering Research & Technology (IJERT)
ISSN: 2278-0181
PECTEAM - 2K18 Conference Proceedings

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