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REAL TIME
MONITORING
OF CARDIAC
SIGNALS
THE NATIONAL INSTITUTE OF ENGINEERING
DEPT OF ELECTRICAL AND ELECTRONICS ENGINEERING
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TEAM
REAL TIME MONITORING OF CARDIC SIGNALS
AKARSH S M (4NI19EE004)
MEDAVARAM LAKSHMI PRATHYUSHA(4NI19EE057)
LIKITHA S(4NI19EE049)
AISHWARAYA B S(4NI19EE013)
GUIDE
DR. OMKAR S POWAR
DEPT. OF EEE
NIE,MYSURU
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CONTENTS
• ABSTRACT
• INTRODUCTION
• LITERATURE SURVEY
• PROBLEM STATEMENT
• OBJECTIVES
• METHODOLOGY
• BLOCK DIAGRAM
• RESULTS AND CONCLUSION
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ABSTRACT
• Heartconditions of the humanbody can be detected by the electricalsignal from the heart which is
called electrocardiogram(ECG)
• The electrocardiogram(ECG) is widely used for monitoring heartconditions of each individual, and
much effort has been put into automatic arrhythmiadiagnosis. Many people get sick or even die
because of the inability to monitor their heartconditions properly
• This serious issue can be prevented by monitoring the heartconditions regularly. In this work, a
system is proposed by using AD8232 single lead heartbeat sensor to extract the ECG signal
• The obtained data is wirelessly transmitted between two or more ESP32Microcontrollers which
can be accessed via Computers. As a result, any health specialist can check the patient’s heart
condition at any time and can take necessary steps
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INTRODUCTION
• In this work, an effective system has been proposed with latest sensors
which can transmit data for real-time monitoring
• Real-time monitoring of heart is preferable by which patient’s heart
condition can be monitored by specialist or doctor and can take
immediate measures.
• Heart rate monitoring is done using AD8232 single lead heart rate sensor
or ECG sensor which has sensor pads. These sensor pads are used to
check heart’s rhythm and electrical activity
• The main component of this system is ESP32 microcontroller. This
microcontroller collects the sensor data and transmits the data wirelessly
for real-time monitoring
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FILTERS INTRODUCTION
• Baseline wander in ECG is mainly due to the movement of the person and it appears as low-frequency artifacts. To suppress
these signals, we use high pass filters which are capable of suppressing these artifacts
• Digital filters are used in this system which performs mathematical operations on a discrete and sampled time signal. This is
largely used in signal processing and simplest way to analyse the signal using these filters.
• The filters used in this system are Butterworth filter, Chebyshev filter and Kalman filter. These filters are being used to
reduce the noise and distortions
• BUTTERWORTH FILTER : Butterworth filter is used to have a signal response as flat as possible and it is also known as
maximally flat magnitude filter. Third order lowpass Butterworth filter is used so that the accuracy of signal response will be
nearer to the ideal values
• CHEBYSHEV FILTER : Chebyshev filter is applied as there is fast attenuation of unwanted signals. These can show better
signals than Butterworth and less ripples over the bandwidth
• KALMAN FILTER : Kalman filters are used for the systems which are continuously changing in nature. These can
estimate the signals based on the previous state making these more applicable for real-time problems
• NORMALIZATION : The normalization technique is used which means scaling the signals in identical power level
interms of its amplitude
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PROBLEM STATEMENT
To transmit ECG data wirelessly
To plot real-time data in python
To stabilize the ECG signals using filters
To analyze the real-time ecg signals
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OBJECTIVES
Real time monitoring using wireless communication through ESP32
Make system easy for Real time monitoring
To filter the noise of ECG data using digital filters
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METHODOLOGY
1)ECG sensor Data OUTPUT in ARDUINO and ESP 32
1)ESP – ESP COMMUNICATION
1)ESP TO SERIAL DATA COMMUNICATION TO PYTHON
SERIAL PLOTTER
DATA LOGGING FOR STANDING , RESTING AND WALKING,SLOW JOGGING
APPLYING DIGITAL FILTERS: BUTTERWORTH, CHEBYSHEV , KALMAN FILTER
AND NORMALIZATION
EVALUATION OF ECG SIGNAL
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1) ECG SENSOR DATA OUTPUT IN ARDUINO AND ESP 32
The ECG sensor is connected to the Arduino and ESP 32 and verified the output in the Serial monitor.
The baud rate used is 115200. The Sample rate is 328.
2) ESP – ESP COMMUNICATION
The ESP-ESP communication is required as the there was need in the wireless transmission. We used
ESP-Now Protocol where we achieved good sample rate is same as transmission .
3) ESP TO SERIAL DATA COMMUNICATION TO PYTHON
The data from the microcontroller is written to the serial port where the other devices can read the
data and further processed . Here we are using the Pyserial library to read the data and storing in
the data set.
4) SERIAL PLOTTER
The data collected from the serial monitor is plotted in python using the PyQt Graph library .
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6) APPLYING DIGITAL FILTERS: BUTTERWORTH, CHEBYSHEV , NORMALIZATION , KALMAN FILTER
Since we have the noise in the ECG data its necessary to denoise and normalize the data. In this
work we have used Normalization, Butterworth , Chebyshev and Kalman filters.
5) DATA LOGGING FOR STANDING , RESTING AND WALKING,SLOW JOGGING
Since the filters cannot be applied directly without modelling it , we have taken the data sample of the
project setup in csv and log file extension format.
7) EVALUATION OF ECG SIGNAL
After the filtering, the output is evaluated using SNR ratio
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RESULTS
• The ECG output is having distortion and contains noise in it, by applying various
filters like Butterworth, Chebyshev and Kalman noise of ECG data can be reduced.
• Data logging of ECG data of 3 phases were obtained:
• Walking phase data
• Resting phase data
• Running phase data
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WALKING DATA APPLIED WITH
BUTTERWORTH,CHEBYSHEV AND
KALMAN FILTER
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CONCLUSION
• After applying different filters, SNR (Signal to Noise Ratio) of different
filters were obtained.
• The filter with high SNR value is more efficient for filtering
• Here Kalman filter was having high SNR value compared to other
filters, So this filter remove the noise efficiently.
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REFERENCES:
• [1] H.K. Chatterjee, R. Gupta, M. Mitra , “A microcontroller-based system for real-time heart rate estimation from ECG
signal”, Annual IEEE India Conference (INDICON) , 2012 ,doi:10.1109/INDCON.2012.6420766.
• [2] Roberto Pasic, Ivo Kuzmanov, Kokan Atanasovski, “ESP-NOW communication protocol with ESP32” , Journal of
Universal Excellence (JUE ) , 2014 , DOI: 10.37886/ip.2021.019.
• [3] Ryan A. Zhan, “Development of novel software and hardware for wind turbine condition monitoring”, California
polytechnic state university, San luis obispo,2021
• [4] Hamid Gholam-Hosseini, Homer Nazeran, Karen J, “ECG Noise Cancellation Using Digital Filters”, IEEE 2012 Annual
IEEE India Conference (INDICON),2012, doi:10.1109/INDCON.2012.6420766
• [5] Kiran Kumar Patro , “De-Noising of ECG raw Signal by Cascaded Window based Digital filters Configuration”, 2015
IEEE Power, Communication and Information Technology Conference (PCITC) ,2015, doi:10.1109/pcitc.2015.7438145
• [6] R Sameni MB Shamsollahi1 ,M Babaie-Zadeh , “Filtering Noisy ECG Signals Using the Extended Kalman Filter Based on
a Modified Dynamic ECG Model” , IEEE Computers in Cardiology, 2005 ,doi:10.1109/cic.2005.1588283
