Backend Design of Web-based ECG Signal Monitoring System

Arjon Turnip

, Muhammad Taufik

, Nanang Rohadi

, Emiliano

, Erwin Sitompul

, Ferlin Firdaus

Turnip

, George Michael T.

, Muhammad Ilham Rizqyawan

Bernardo Nugroho Yahya

Electrical Engineering Department, Padjadjaran University, Bandung, Indonesia

Study Program of Electrical Engineering, Faculty of Engineering, President University, Indonesia

Porlak Parna AgroTourism Company, Jakarta, Indonesia

Electrical Engineering, Institut Teknologi Nasional, Indonesia

Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Indonesia

Industrial & Management Engineering, Hankuk University of Foreign Studies, Republic of Korea

Keywords: Arrhythmia, ECG, MQTT, Rest Api, JSON, Frontend.

Abstract: Arrhythmias are disorders that occur in the heart rhythm. People with arrhythmias usually feel a heart rhythm

that is too fast, too slow, or irregular. The purpose of this study was to facilitate medical personnel in

monitoring the patient's heart rate by using a web-based ECG monitoring system in real time. The process of

this tool starts with the patient using a heart recording device, then the results of the heart recording are sent

to the database using the MQTT broker, then the data will be requested by the Frontend via the API and then

the API will check the data into the database. If the data is found, the data will be returned by the API in JSON

form and then displayed by JavaScript to the Frontend. Database was built using PostgreSQL with Rest Api

using Flask. From several experiments conducted, the results of 100% data return by Api in the form of JSON

were achieved.

1 INTRODUCTION

In 2017, more than 17.7 million people died from

cardiovascular disease (CVD) (World Health

Organization, 2017) so it is known to be the biggest

cause of death in the world. CVD is associated with

disorders of the heart and blood vessels that often

cause heart arrhythmias, strokes, hypertension, and

heart failure.

Arrhythmias are disorders that occur in the

rhythm of the heart. Arrhythmia sufferers can feel a

heart rhythm that is too fast, too slow, or irregular

(Alodokter, 2020). There are several types of

arrhythmias, including atrial fibrillation, AV block,

supraventricular tachycardia, ventricular extra

systole, and ventricular fibrillation.

One way to diagnose arrhythmias is by using an

electrocardiogram (ECG). An EKG is a common

diagnostic test used to evaluate heart function using

an electrical impulse detection machine. This

machine is used to detect any abnormalities or

malfunctions in the heart's electrical system. The

patient's heart is recorded using a device called an

electrocardiograph. Recording is usually done for 5 to

8 minutes. The monitoring process is carried out by

the doctor by looking at the waves that are displayed

on the monitoring screen which is usually printed on

paper.

Recently, there is an arrhythmia diagnostic tool

that can be used on patients for 24 hours or more to

record the heartbeat, namely BITalino. However,

doctors have difficulty monitoring the recording

process of the patient's heart, because they are not

always with the patient. For that we need an ECG

signal monitoring system that can make it easier for

doctors to monitor the results of the patient's heartbeat

record anytime and anywhere.

There is research on feature extraction for heart

rate classification from electrocardiogram recordings

which were carried out using Wavelet-based features

(Saragih et al, 2019; Amri et al, 2016; Rizqyawan et

al, 2016). In this study, feature extraction uses a

wavelet transform-based method with the Haar

wavelet coefficient of the ECG signal slice

representing one beat. The features are built from

each coefficient of the transformed decomposition of

the ECG signal slice with a simple statistical

Turnip, A., Tauﬁk, M., Rohadi, N., Emiliano, ., Sitompul, E., Turnip, F., Michael T., G., Rizqyawan, M. and Yahya, B.

Backend Design of Web-based ECG Signal Monitoring System.

DOI: 10.5220/0010371403170324

In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical (HIMBEP 2020), pages 317-324

ISBN: 978-989-758-500-5

317

approach, namely the mean, standard deviation of

kurtosis and skewness.

Another study is the detection of Arrhythmias

using ECG signals with the R-Peak detection method

(Setiawan et al, 2019; Turnip et al, 2018; Wijaya et

al, 2019). There are several signal processing

processes used, namely signal filtering, QRS complex

detection, signal squaring, complex detection of beats

per minute, and classification. In order to identify

heart defects, signal processing can be performed

using a variety of applications. Apart from

classification, a system for monitoring the patient's

condition is also needed. Research conducted by Syah

Alam et al (Alam, Hartanto and Pratama, 2019) in his

paper entitled Design of a heart rate monitoring

system using a Bluetooth-based electrocardiogram

and labview. The design is carried out using an

electrode sensor connected to the initial amplifier,

band pass filter, final amplifier, clamper circuit, and

Arduino Uno connected to the HC-05 serial Bluetooth

module and finally monitored using a Personal

Computer Labview. The server can access the ECG

monitoring application using Bluetooth with a

distance of 5 meters without obstruction and 3 meters

with a barrier. The application can display the ECG

recordings properly and display data visualization on

the PC server.

Novita Karolina et al (Novita Krolina, Ristanto

Hulu, 2019) created a web-based heart rate detection

system. The data from the heart rate recording are

processed and processed via Arduino. The data is sent

in the form of waves through the ESP8226 module,

which is in the form of Beat Per Minute (BPM).

Average results are obtained in the form of graphs and

numbers. The system displays the time during use and

the device reads the heart rate, then displays it on the

web which can be accessed at any time. This

monitoring tool has a sensor input response that

varies according to the condition of the heart rate read

on the pulse sensor. The design of the equipment used

consists of a series of sensors, an amplifier circuit for

heart rate, an ESP 8266 circuit and a microcontroller.

The design of a heart rate monitoring system was

also carried out by Ria Hariri et al (Hariri, Hakim and

Lestari, 2019; Turnip et al, 2018). They created a

heart rate monitoring system using the AD8232

sensor based on the Internet of Things which resulted

in a 100% successful percentage of sending data to

websites using the internet. However, there is an error

from the results of the tool calibration using the ECG

simulator by 1.7%. The measurement result data is

successfully monitored on a web application using an

android or a PC so that monitoring of heart conditions

can be done anywhere and anytime in real time

(Oktarino et al, 2019; Afriansyah et al, 2019).

In a study entitled IoT based Real Time ECG

Monitoring System using Cypress Wiced (Deshpande

and Kulkarni, 2017), ECG monitoring was carried out

using the Cypres IoT platform which is integrated

with the device. The method of collecting ECG data

is done using a non-intrusive sensor monitoring

wearable device where the data is sent directly to the

cloud using Wi-Fi with high data rates. Cloud IoT is

responsible for visualizing ECG data and storing data

for further analysis. The server used consists of 3

parts, namely: HTTP server, MQTT server, and

storage server.

There is another journal about Internet of Things-

based Systems entitled Design of Internet of Things

(IoT) systems for monitoring and predicting heart

attack symptoms (Supriyatna and Away, 2019). The

study used an AS8232 ECG sensor integrated with an

Arduino and Raspberry Pi microcontroller for the

purpose of monitoring a user's heart rate. The

microcontroller used is the Wemos d1 mini which

functions to perform computations for controlling the

Internet of Things system using the C programming

language. This microcontroller sends ECG sensor

data through the cloud and is received by the

Thingspeak server before being displayed on a

channel that has been servered so that it can be

accessed via a browser. This system has been

successfully built and tested. The results of this

system trial are heart rate data and buzzer data from

monitoring and predicting heart attack symptoms.

Furthermore, we need a system that is able to receive

real-time patient heart record results and

classification results.

Based on the above background, this study aims

to build a web-based ECG signal monitoring system

to make it easier for doctors to monitor patient heart

rate results in real time. This study has a problem

limitation, namely the system built is the back of the

whole system, where this section is directly related to

the microcontroller through the server or often known

as the backend system. Backend ECG signal

monitoring system built on a web basis using the

PostgreSQL database. The output of this backend

system is data in the form of a JSON API that can be

displayed in realtime. The process of requesting data

to the database uses the Flask API.

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2 METHOD

2.1 Web Service REST API Structure

The structure of the Rest API web service is a

structure that describes the data exchange process on

the system backend. The structure is shown in Figure

Figure 1: Web Service Structure.

The flow of data exchange starts from the website

application that requests data to the Rest API Server. Then

the server checks the requested data into the database. If the

requested data is in the database, the data will be retrieved

and forwarded by the API Server to be displayed on the

website application

2.2 System Architecture Design

The system architecture is a description of the flow of

system development components. In this study, the

architectural design of the ECG signal monitoring

system is shown in Figure 2.

Figure 2: System Architecture Design.

In Figure 2, there are several components of the

system, such as: devices, broker servers, backend

servers, database nodes, front end apps, and training

workstations. The architectural flow starts from the

device, which is a sensor attached to the patient's

body to record heart activity. The data is sent to the

broker server to be stored in the database via program

code on the backend server. The data is processed

based on a request on the front end of the monitoring

application by the doctor through the Rest API JSON

to be classified. The API request is processed using

the backend server program code which returns data

to be displayed on the front end. The classification

results are in the form of labels that are saved again

to the database. However, in this study only a backend

server and database node were built, such as the red

box in Figure 2.

2.3 Use Case Diagram

Use Case Diagrams are used to describe the features

that users can perform on the system, in this case the

functional requirements of the system. Users or actors

who play a role in the use of this system are doctors

and patients. Doctors can do several things on the

system such as: log in, view patient data, monitor

patient heart recordings. Patients can log in and see

the results of their own heart records.

The design of the use case diagram of the ECG

signal monitoring system can be seen in Figure 3.

Figure 3: Design Use Case Diagram.

2.4 Entity Relationship Diagram

Entity relationship diagram is a model that describes

the relationship between variables through their

attributes. The proposed ECG signal monitoring

system uses 4 tables as shown in Figure 4.

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319

Figure 4: Entity Relationship Diagram.

In Figure 4, the doctor table has a relationship

with the patient table with a cardinality of 1: n,

meaning that the doctor can monitor more than 1

patient and 1 patient is handled by 1 doctor. The

patient table is related to the record table with 1: n

cardinality where 1 patient can have 1 many

recordings and 1 data record can have 1 patient.

Furthermore, 1 recording table has a relationship with

1 heartbeat table with 1: 1 cardinality where 1 data

recording can have 1 heartbeat data and results in the

classification of artemia class.

2.5 Class Diagram

Class diagram is a system visualization that describes

the relationship between entities through the

functions of each entity. The proposed class diagram

uses 4 entities which are depicted in Figure 5.

Figure 5: Class Diagram.

Figure 5 describes each entity and its functions.

These functions will be implemented as functions

when coding the program. A flowchart is a sequence

of instructions depicted with symbols. In this study,

there are 3 types of data requests by the front end of

the monitoring system on the Rest API that have been

made on the backend system. The flowchart of the

patient data request process is shown in Figure 6.

In Figure 6, requesting patient data starts from the

front end requesting data using the API. Then the

backend system will check the request data to the

database. If the data exists, then the Patient data will

be returned using the API and displayed on the front

end application, but if the data does not exist, an error

message will appear or there is no data. While the

flowchart of the request data recording process for

patient data is shown in Figure 7.

Figure 7 shows the instructions for requesting all

data recording on selected patient data. Assuming the

doctor has selected the data of one patient, then in the

application, the front end requests all the data

recordings of the patient that has been selected using

the API. Then the function in the backend program

code will be executed and displays all the data

recordings of the selected patient. If found, the data

will be displayed in the form of an API, but if the data

does not exist, an error message will be displayed.

Figure 6: Flowchart proses request patient data.

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Figure 7: Flowchart of the entire data recording request

process.

The flowchart of the request data recording

process based on the selected filtering is shown in

Figure 8. Figure 8 describes the data recording

request instructions based on the filtered attributes,

such as patient id, date, time, and duration. The front

end requests data recording based on the filter on that

attribute. Then the function in the backend program

code will be executed and displays the data recording

based on the filter. If the data exists, it will appear in

the form of an API, but if the data is not there, an error

message will appear.

Figure 8: Flowchart of the data recording request process

based on filtering.

2.6 Data Retrieval Program Code

Program code is an implementation of class diagram

design. Each program code is wrapped into a function to

execute a backend system command, such as receiving data

requests from the front end and displaying it in the form of

an API in JSON format. The patient data retrieval program

code is a program code that executes the command

displaying all patient data. The code part of the patient data

retrieval program is shown in Figure 9. In Figure 9, the

program code is wrapped into the get_all () function and the

function executes the command displaying the data of all

monitored patients. The output of this function is patient

data in the form of an API

Figure 9: Code of patient data collection program.

A data recording retrieval program code is a

program code that executes a command displaying all

data recordings of the selected patient. The part of the

data recording program code is shown in Figure 10.

Figure 10: Data recording program code.

In Figure 10, the program code is wrapped into the

get_all () function and the function executes the

command displaying the patient record data of the

selected patient. The output of this function is a

patient data recording in the form of an API.

The data recording program code based on several

filter attributes will display the data recording based

on attributes such as: patient id, date, time and

duration. The part of the program code is shown in

Figure 11.

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321

Figure 11: Data recording program code based on patient

id, date, time, duration.

In Figure 11, the code for a data recording

retrieval program based on these attributes is packed

into the get_by_pasien () function and the function

executes the command displaying the data recording.

For example, the front end requests a patient data

recording with id 1, on August 8, 10:00:00, with a

duration of 30 seconds, it will display patient data

recordings from 10:00:00 to 10:00:30.

3 RESULTS AND DISCUSSIONS

Each data request results from the front end will be

returned and displayed by the Backend server in the

form of a JSON API. Furthermore, the data is

converted to be displayed on the front end

application.

3.1 Request Patient Data

The Backend system has provided the url endpoint

used by the front end to access the data. On requesting

patient data, the end point provided is

domain.com/api/v1/ppatient. This endpoint is used by

the front end to retrieve patient data in the form of a

JSON API. The output of this endpoint can be seen in

Figure 12.

3.2 Request All Recording Data

Backend system has provided url endpoint to access

all data recordings of selected patients, namely

domain.com/api/v1/recording. This endpoint is used

by the front end to retrieve and return patient data

recordings in the form of a JSON API. The output of

this endpoint can be seen in Figure 13.

3.3 Request Data Recording based on

Id_Pasien, Date, Time, Duration,

with Duration = 6

The backend system provides an endpoint for

displaying recorded data based on patient id, date,

time, and duration. The endpoint is

domain.com/api/v1/recording/get-by-

pasien?date={tanggal}&time={waktu}&duration={

durasi}& id_pasien={id_pasien}.

This endpoint is used by the front end to request

data recording based on patient_id, date, time and

duration, then the backend system will return and

display the data in the form of a JSON API. The

output from this endpoint can be seen in Figure 14.

Figure 14 is the result of requesting data at the

following endpoints:

domain.com/api/v1/recording/get-by-

pasien?date=01-

082020&time=01:34:27&duration=6&

id_pasien=4.

The endpoint accessed the patient's data recording

having id 4 with a duration of 6 seconds. Another

example is the use of an endpoint with multiple

attributes shown in Figure 15.

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Figure 12: Request for patient data in the form of a JSON

API.

The output uses the following endpoints:

domain.com/api/v1/recording/get-by-

pasien?date=01-

082020&time=01:34:27&duration=

1&id_pasien=4.

The endpoint accesses the data recording of a patient

having id 4 with a duration of 1 second.

Figure 13: Request data recording.

Figure 14: Request data recording based on filter 1.

Figure 15: Request data recording based on filter 2.

3.4 Request using Postman

Postman is an application that functions as a test site

for creating url endpoints. How to use this application

is to enter the url endpoint that has been created with

or without parameters. An example of its use is shown

in Figure 16.

Figure 16: Request using postman.

Figure 16 describes the url endpoint entered in the

Postman application with parameter id 4, August 1, 1

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323

minute 34 seconds 27 for 1 second, then produces

data in the form of API JSON.

4 CONCLUSIONS

Based on the results of development, testing, and after

conducting several experiments, it was found that the

Backend system that has been built can be used to

return and display the data requested by the front end.

The backend system can also be used to be connected

to a sensor device that is attached to the patient's

body. The Backend system displays data in the form

of an API with JSON data format. In further

development, a system by connecting sensor data

with the system's backend endpoint url, and the

development of an Android-based monitoring

application will be built.

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