Real‑Time ECG Monitoring System Using AD8232 Sensor and

Arduino UNO for Biomedical Applications

B. A. V. N. Hasini

, Mekala Varun

, N. V. S. Sanjana

, Abburi Sai Keerthi

Sai Mani Deepika Somayajula

and Gayathri Ramaswamy

Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Bengaluru‑560035,

Karnataka, India

Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham, Bengaluru‑560035, Karnataka, India

Keywords: ECG Monitoring, AD8232 Sensor, Arduino UNO, Real‑Time Signal Processing, Biomedical Signal

Acquisition, Cardiovascular Health, Noise Filtering, Wearable Health Monitoring.

Abstract: The increasing prevalence of cardiovascular diseases necessitates the development of accessible and cost-

effective solutions for continuous electrocardiogram (ECG) monitoring. This study presents a real-time ECG

monitoring system utilizing the AD8232 ECG sensor interfaced with an Arduino UNO microcontroller to

measure and analyze the electrical activity of the heart. The AD8232 sensor, equipped with integrated signal

conditioning capabilities, extracts, amplifies, and filters bio-potential signals while mitigating noise

interference, ensuring accurate ECG readings. The Arduino UNO serves as the primary processing unit,

facilitating data acquisition and transmission for real-time visualization. The ECG signal is displayed using a

serial plotter or the Processing IDE, enabling immediate observation and analysis. The hardware

implementation involves a structured integration of the AD8232 sensor with the Arduino UNO, accompanied

by a comprehensive circuit diagram for ease of replication. This system provides a reliable and cost-efficient

approach to real-time ECG monitoring, offering potential applications in remote healthcare and early cardiac

anomaly detection.

1 INTRODUCTION

Cardiovascular diseases (CVDs) are accountable for

some of the major causes of death in the world and

are a main hindrance to public health. The covert

character of the diseases necessitates that they be

continuously monitored and diagnosed early to allow

timely intervention and improved patient outcomes.

As the prevalence of cardiac diseases increases, so

does the need for novel and lower-cost methods of

real-time monitoring of cardiac electrical activity.

Standard electrocardiogram (ECG) monitoring

methods are largely limited to clinical settings, which

limits accessibility and results in a lag in detection of

potential abnormalities.

The Wireless ECG Monitoring System,

developed employing the AD8232 Sensor interfaced

with the Arduino UNO, meets this fundamental

requirement with the help of contemporary electronic

parts to provide an economical, convenient, and

accessible solution for live electrocardiogram

monitoring. The system attempts to balance

traditional healthcare practices and emerging

technological innovations, thereby ensuring

continuous monitoring accessible to various socio-

economic classes. This is congruent with the overall

objective of encouraging preventive healthcare

practices and combating the incidence of

cardiovascular diseases.

At the center of the system is the AD8232 ECG

sensor, a small and versatile chip that is capable of

capturing, amplifying, and filtering the heart

biopotential signals. The sensor is paired with the

Arduino UNO microcontroller, which amplifies and

sends the signals for graphical representation. The

ECG signals are visible in real time by the users on a

serial plotter or the Processing IDE, which provides

an easyto-use interface for heart activity analysis.

Furthermore, the wireless design of the system

greatly enhances its portability, making remote

monitoring feasible in many settings, from home

health care to community health care projects.

Hasini, B. A. V. N., Varun, M., Sanjana, N. V. S., Keerthi, A. S., Somayajula, S. M. D. and Ramaswamy, G.

Real-Time ECG Monitoring System Using AD8232 Sensor and Arduino UNO for Biomedical Applications.

DOI: 10.5220/0013892900004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

113-118

ISBN: 978-989-758-777-1

113

Aside from its technological innovation, the

system serves the interests of society since it enables

individuals to take an active role in the management

of their cardiovascular health. Continuous monitoring

that enables the identification of abnormalities early

on can greatly minimize the risks of heart diseases

and enable timely medical intervention. The cost-

effectiveness and reproducibility of the system make

its principle a suitable solution for mass application,

especially in developing regions. By integrating real-

time ECG monitoring with wireless communication,

this system is a paradigm shift in healthcare from

reactive treatment to proactive health maintenance.

Not only does it improve patient autonomy, but it

helps towards the larger vision of accessible and

preventive healthcare for everyone.

2 RELATED WORKS

Sangeethalakshmi K. et al. 2023 develop an IoT-

based realtime health monitoring system.

Methodology uses an ESP32, sensors, a mobile app,

and GSM for continuous monitoring. Results ensure

reliable patient management by healthcare

professionals. Future work includes advanced

sensors, better UI, and scalability.

Sadad et al. 2023 proposed an efficient ECG

image classification using a lightweight CNN with an

attention module and IoT. Results show high

accuracy with reduced computation, improving real-

time processing. Future work includes advanced

attention mechanisms and expanding the IoT

framework.

Xu et al. 2020 introduced a framework for ECG,

utilizing small, capable devices for sensing,

processing, and communicating. Integrates sensors

and embedding devices for secure data transmission.

Shows the feasibility of using IoT for secure and

efficient ECG monitoring. Future work could focus

on enhancing security measures and improving

scalability.

Yeh et al. 2021 integrated IoT-based ECG

monitoring with deep neural networks for remote

healthcare. Results showed improved accuracy and

efficiency in heart condition classification. Future

work aims to enhance robustness with diverse data

and advanced algorithms.

Hasan et al. 2020 introduced an ECG device using

Blynk app for heart disease diagnosis. It enables real-

time ECG data collection, transmission, and alerts for

abnormalities. Future work includes advanced ML for

predictive analytics and monitoring more vital signs.

Obaidur et al. 2022 developed ECG device for

rural healthcare in Bangladesh. It uses IoT sensors,

microcontrollers, and cloud computing for remote

heart monitoring. Future work includes adding health

parameters, improving security, and expanding

coverage.

Gawsalyan et al. 2022 introduced ANNet, real-

time detection in wearables of IOT. Using LSTM and

MLP, it ensures power-efficient processing of ECG

features. Future work aims to improve robustness to

artifacts and adaptability across demographics.

Morello et al. 2022 developed an IoT-based ECG

monitoring system for cardiac diagnosis in smart

cities. It demonstrated effective real-time detection of

cardiac issues. Future work includes improving

accuracy, scalability, and integrating machine

learning for better diagnostics.

O. Ankireddypalli et al. 2024 present a

piezoelectric-powered smart irrigation system for

urban sustainability. Footstep energy powers

irrigation based on real-time soil moisture data,

reducing water use by 30%. The system ensures

reliable automation, and future enhancements include

cloud integration and machine learning for efficiency.

Adithi et al. 2019 develop a low-cost robotic

mapping system using an ultrasonic sensor. The robot

scans a 180-degree area and plots real-time radar

maps. It efficiently detects motion via Bluetooth

control. Future work includes GPS tracking and

wireless communication.

M.Shyam et al. 2024 presented an health

monitoring system wearables. It collects and

transmits real-time vital signs securely. Results

confirm accurate monitoring. Future work focuses on

enhanced security and remote care.

Pradeep et al. 2017 propose an IoT-based

sustainable water management system for rural areas.

They analyze water scarcity issues in Gudipadu

Cheruvu and design an automated distribution and

storage system. The results demonstrate effective

regulation of water usage. Future improvements

include enhanced scalability and real-time

monitoring.

M.Tejaswi et al. 2023 discuss the implementation

of IoT-based precision agriculture for optimizing

farming operations. They use a NodeMCU, DHT11,

and soil moisture sensor to develop an automated

watering system. The results show improved water

management and increased crop yields. Future

enhancements include advanced AI-driven analytics

for better decision-making.

Ramaswamy et al. 2023 present a brain tumor

detection model using a modified Link-Net with SE-

ResNet152, achieving 99.2% accuracy. Future work

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focuses on improving feature fusion and integrating

additional pre-trained models.

Ramaswamy et al. 2022 also propose an

Optimized Gradient Boosting model for Type-2

Diabetes Mellitus detection, achieving 94.5%

accuracy. Future improvements include additional

clinical features and advanced ensemble techniques.

3 METHODOLOGY

3.1 Components Used

The implementation of the Wireless ECG Monitoring

System utilizes the following hardware components

(table 1):

Table 1: Components Used.

Components Description

Arduino UNO A microcontroller board based

on ATmega328P, used for

reading sensor data and

rocessin

nals.

USB Power Cable Provides power supply to the

Arduino board from a computer

or ada

ter.

Pulse Sensor Optical sensor that detects

heartbeat by measuring blood

flow variations through green

ht absor

tion.

AD8232 Heart

Rate Monitor

An analog front-end module that

extracts, amplifies, and filters

ECG signals for accurate heart

activity monitoring.

LM35

Temperature

Sensor

A precision temperature sensor

that outputs a voltage

proportional to temperature in

°C, used for body temperature

measurement.

16×2 LCD

Display

A character-based display used

to show ECG readings, heart rate

(BPM), and temperature in real

time.

Connecting Wires Essential for establishing

connections between various

components and the Arduino

oard.

Potentiometer Used to adjust the contrast of the

LCD display for better visibility.

LED Provides visual indications, such

as power status or alerts for

abnormal ECG readings.

3.2 Software Used

The system employs the following software tools for

coding, visualization, and data processing:

• Arduino IDE 2.0.3 – Used for coding,

editing, and uploading the program onto the

Arduino microcontroller.

• Processing IDE – Employed for graphical

user interface (GUI) visualization of the

ECG signals.

3.3 Technical Aspects

• Pulse Sensor: The Pulse Sensor works by

emitting green light (550 nm) onto the user’s

finger and detecting the level of reflected

light using a photo sensor. Green light is

absorbed by oxygenated hemoglobin in

arterial blood, allowing pulse to be detected

by sensing changes. Illumination and photo

sensor levels persist while the system detects

heartbeat pulses precisely.

• LM35 Temperature Sensor: The LM35 is a

low-voltage, high-accuracy centigrade

temperature sensor produced by Texas

Instruments. It is a voltage-output

temperature sensor that is linearly

proportional to temperature in degrees

Celsius (°C). Specifically, this sensor does

away with the requirement for external

calibration, with ±0.5°C accuracy at room

temperature and ±1°Cover its full operating

range of -55°C to +155°C.

• Arduino UNO: The Arduino UNO is a

microcontroller board based on the

ATmega328P. It features:

• 14 digital input/output pins (6 can

be used as PWM outputs)

• 6 analog inputs

• 16 MHz ceramic resonator

• USB connection, power jack, ICSP

header, and reset button The board

is capable of operating via a USB

connection, an AC-to-DC adapter,

or a battery.

• AD8232 Heart Rate Monitor: The AD8232

sensor is a cost-effective solution that was

specifically intended to monitor cardiac

electrical activity. It is utilized to acquire

and amplify biopotential signals, thereby

producing an analog ECG output that is

employable for real-time monitoring.

• 16×2 LCD Display: A 16×2 LCD is capable

of showing 16 characters on one line and a

Real-Time ECG Monitoring System Using AD8232 Sensor and Arduino UNO for Biomedical Applications

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total of two lines at its command. It shows

every character in a 5×7 pixel matrix and can

show 224 unique characters and symbols.

The LCD module has two registers:

• Command Register: Stores

commands and instructions given

to the LCD.

• Data Register: Holds the data

(characters) to be displayed.

The LCD facilitates on-screen visualization

of ECG data and other system parameters.

4 SYSTEM DESIGN

The design of the system of the ECG Graph

Monitoring project using the AD8232 ECG Sensor

and the Arduino platform involves the integration of

heterogeneous hardware components, such as the

Arduino UNO board and the AD8232 ECG Sensor,

and software code in order to monitor and present

ECG signals. The system design is divisible into three

main components: the hardware configuration, the

signal processing, and data presentation.

4.1 Hardware Setup

Hardware setup is done by plugging the AD8232

ECG Sensor into Arduino UNO board. AD8232

sensor detects electrical activity of the heart and

provides an analog signal. Pins of the sensor, such as:

These pins are connected to corresponding pins on the

Arduino for proper signal acquisition. Additionally,

the sensor pads are placed at specific body locations

(Right Arm, Left Arm, and Right Leg) to ensure

optimal signal detection. The connections are made

using a breadboard for ease of prototyping. Figure 2

shows the hardware setup. The Table 2 shows Pin

descriptions.

Table 2: Pin Descriptions.

Pin Name Description

GND Ground connection

3.3V Power su

OUTPUT Analo

ECG si

nal out

ut.

LO- Lea

-off detection pin

LO+ Lea

-off detection pin.

4.2 Circuit Diagram

The connections between the AD8232 ECG Sensor,

Arduino UNO, and external components are shown in

the below diagram. The Arduino is programmed to

read the analog signal from the AD8232 sensor using

the analogRead() function. The obtained signal is

then processed to detect any lead-off condition,

indicated by the symbol”!”. If a lead-off condition is

detected, the system notifies the user by displaying a

blue line. Figure 1 shows the circuit diagram.

4.3 Signal Processing

If the incoming data is valid, the system processes the

analog signal to calculate the heart rate (BPM). This

is achieved by measuring the time interval between

successive peaks in the ECG waveform. The BPM

value is then sent to the connected computer for

further visualization.

4.4 Data Visualization

The processed data, including the ECG waveform and

BPM, is transmitted from the Arduino to a computer

using a serial connection. On the computer side, a

Processing sketch is employed to receive and

visualize the data in real time. The Processing sketch

performs the following tasks:

• Reads incoming data from the Arduino.

• Plots the ECG waveform dynamically using

the line() function.

• Displays the BPM periodically on the screen

Figure 1: Circuit Diagram.

5 RESULTS AND EVALUATION

ECG Graph Monitoring project with the AD8232

ECG Sensor and Arduino aims to create a cost-

effective system for monitoring and analyzing the

electrical activity of the heart. The AD8232 ECG

Sensor is employed to capture the heart’s electrical

signals, which are then processed and displayed using

an Arduino microcontroller. The project offers an

accessible solution for individuals to observe and

analyze their ECG signals at home, enabling early

detection of potential heart-related issues. The system

utilizes a simple circuit connection between the

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AD8232 sensor and Arduino, making it user-friendly

for electronics enthusiasts and beginners. The real-

time ECG data is visualized on the Arduino’s serial

plotter or Processing IDE, providing a graphical

representation of the heart’s electrical activity.

Figure 2: Hardware Setup of the ECG Monitoring System.

Inference: By implementing this project, individuals

can gain insights into their heart’s health and

potentially identify irregularities or abnormalities in

the ECG signal. The system’s affordability and

simplicity make it an accessible tool for personal

health monitoring. It serves as a valuable educational

project for learning about ECG signals, sensor

interfacing, and Arduino programming. While the

project provides a useful monitoring tool, it’s

important to note the disclaimer in the article,

emphasizing that the AD8232 module used is not a

medical device and is not intended for medical

diagnosis or treatment. Users should consult

healthcare professionals for accurate medical

assessments and diagnoses. Figure 3 shows the

inference result and figure 4 shows the graphical user

interface.

Circuit Diagram: The connections between the

AD8232 ECG Sensor, Arduino UNO, and external

components are shown in the below diagram. The

Arduino is programmed to read the analog signal

from the AD8232 sensor using the analogRead()

function. The obtained signal is then processed to

detect any lead-off condition, indicated by the

symbol”!”. If a lead-off condition is detected, the

system notifies the user by displaying a blue line.

Figure 3: Inference Results from the ECG Monitoring

System.

Figure 4: Graphical User Interface Displaying Real-Time

ECG Signals.

6 CONCLUSIONS

The AD8232 ECG Sensor and Arduino-based ECG

Graph Monitoring project is an inexpensive and

simple method of continuous monitoring of cardiac

health. With the use of minimum-cost hardware along

with open-source software, the system allows to

detect cardiac ailments in an early stage, thus helping

to stem the global problem caused by cardiovascular

diseases. Its application ranges from urban areas,

where it allows for regular monitoring, to rural areas

that do not have easy access to healthcare facilities,

and it can serve as a first-line diagnostic tool. The

open-source nature of the project encourages

innovation, which results in further advancement in

affordable healthcare technology.

In the future, wireless communication modules

like Bluetooth or Wi-Fi can facilitate real-time remote

monitoring with instant connectivity. In addition,

integration of machine learning algorithms for

autonomous ECG analysis can enhance predictive

diagnostics, providing valuable insights into potential

heart conditions. Integrations with healthcare experts

could also be utilized to validate and calibrate the

system for clinical use. With these enhancements, the

project has immense potential to evolve into an

advanced telehealth solution, facilitating proactive

cardiac care in home and clinical settings.

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