Arduino based wireless tracking of Sleep Apnea through monitoring of Health parameters

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Arduino based wireless tracking of Sleep Apnea through monitoring of
Health parameters
Dibyendu Mandal1, Sandip Bag2, Swati Sikdar1, Rupankar Ghoshal3, Sujaya Das3, Udbhasita Pal3
1Assistant Professor, Department of Biomedical Engineering, JIS College of Engineering, Kalyani, India
2 Professor, Department of Biomedical Engineering, JIS College of Engineering, Kalyani, India
3Student, Department of Biomedical Engineering, JIS College of Engineering, Kalyani, India
-------------------------------------------------------------------------***--------------------------------------------------------------------
Abstract - When breathing repeatedly stops and
resumes while slumber, it is referred as sleep apnea.
Untreated conditions lead to vigorous snoring during sleep
followed with tiredness and can be fatal in future untreated
conditions. Obstructive sleep apnea (OSA) is characterized
by recurring bouts of partial or total upper airway
obstruction brought on by the pharyngeal airway
narrowing or collapsing while a person is still trying to
breathe. This decrease in airflow, which lasts for at least 10
seconds, causes a drop in blood oxygen saturation and
cortical arousals. The long-term effects of Obstructive Sleep
Apnea (OSA) eventually lead to heart failure, hypertension,
arrhythmia, and cerebrovascular damage. The
polysomnography (PSG) nightly sleep study, which is the
standard diagnostic method for OSA, is a laborious and
time-consuming practice that exacerbates the patient's
suffering. Since the introduction of computer-aided
diagnosis (CAD), researchers studying sleep disorders have
become increasingly interested in the automatic detection
of OSA since it affects both therapeutic and diagnostic
choices. The present work thrives to develop a wireless
system for tracking the various health parameters which
fluctuate during apnea connected through the cellular
phone module. The device has been tested and various data
has been recorded on which clinical remarks can be
analyzed for various data sets implemented on different
age groups. It has been noticed that people of age 40 years
or higher shows a higher chances of developing sleep
apnea. The data alteration in Heart rate, saturation of
partial pressure of dissolved oxygen in blood (SpO2) and
the snoring intensity marks the footprint of Apnea
condition in the patient.
Key Words: Obstructive Sleep Apnea, Arduino, Heart
Rate, SpO2, ECG
1. INTRODUCTION
Obstructive sleep apnea (OSA)-related sleep disorders
depend on unique disruptions of the upper respiratory
system's normal function and are associated to primary
hypersomnia, interrupted sleep, and sleep deficiency.
Among the most typical sleep disturbances, OSA is
characterised by recurrent episodes of partial or full
cessation of breathing while asleep. According to the
literature, it affects 17% of women and 34% of men in the
adult population. The primary symptoms develop with
behavioural changes that eventually result in breathing
pattern changes, sleeplessness, and may even cause
narcolepsy [1]. Depending on the patient's
pathophysiology, the length of the involuntary and
nocturnal respiratory halt can range from 20 to 40
seconds, but can also be as little as 10 seconds
(hypopnea), 2 minutes (apnea), or longer.
Disproportionate fat depositions in the muscles of the
pharynx tend to narrow the upper airway and are
responsible for apneic episodes in OSA patients. The
blood oxygen saturation typically falls during the apnea-
hypopnea episodes, which then sets off the autonomic
neural response and causes micro-arousals with
nocturnal gasping for air. 90% of OSA sufferers say they
have restless and erratic sleep, weariness, and a lack of
vitality. The preponderance of OSA patients are currently
underdiagnosed because a bigger percentage of them are
asymptomatic. Repeated apnea-hypopnea episodes cause
primary events like intermittent hypoxemia, micro
arousals, and increased intrathoracic pressure. These
primary events then start a cascade of interconnected
processes that contribute to the developmental causes of
secondary disease manifestations and disease end points,
as shown in Figure 1. The main cause of metabolic
dysfunction and high blood pressure in OSA patients is
sympathetic nervous system (SNS) activation. Sleep
apnea is a serious medical condition that can lead to
complications such as daytime weariness, diabetes,
kidney and liver difficulties, and cardiac issues [2, 3].
The underlying genetic propensity for the disease and
intricate interactions between anatomical,
neuromuscular, and other variables make up the
multifactorial pathophysiology of OSA. Menopause in
women, middle age, obesity, snoring, and a number of
craniofacial and oropharyngeal characteristics, such as a
large neck circumference, retro- or micrognazia, nasal
blockage, enlarged tonsils and adenoids, macroglossia,
and low-lying soft palate, are risk factors of OSA [5]. The
disorder is now better understood and addressed, owing
to developments in sleep medicine and the availability of
improved diagnostic techniques over the past 20 years.
There are numerous therapy options currently accessible,
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 03 | Mar 2023 www.irjet.net p-ISSN: 2395-0072

and the management of patients with OSA necessitates a
multidisciplinary approach. The most efficient and widely
used kind of treatment is positive airway pressure (PAP),
which has been around since the early 1980s. Several
upper airway surgical techniques, weight loss, and
mandibular advancement devices are further choices.
Fig -1: Association of OSA and Cardiovascular
disorders [4]
In this article, the wireless technique to monitor OSA is
addressed where the tracking system is conjunct with
cellular phone through Bluetooth connectivity.
2. MATERIALS & THE PROTOTYPE
For developing the prototype the following electronic
components had been utilized. The details of the
components are enlisted in Table 1.
Table -1: List of Components
Component
Ardiuno UNO Potentiometers (Trim pot)1 MΩ
MAX30102 Capacitors (Polar) 10 μF, 16V
ECG sensor 1N4148 (Diode)
Sound sensor IC: LM 555
Bluetooth module IC: LM 358
Pulse sensor Jumper wires
Breadboard Single strands wires
Resistors (MFR, 1%,
0.25 W) 10 KΩ
Piezo-buzzer
Thermistor 390 KΩ A DC voltage source +9V
Three separate layers are merged in this microcontroller-
based sleep apnea monitoring device for those with
sleeping disorders. A microcontroller unit that connects
the input layer and the output layer is the main layer. Four
separate sensors are coupled in the input layer to give the
Arduino UNO an analogue signal with which to measure
various sleep status indices. The Arduino UNO's serial
monitor and a mobile application that displays the digital
data that the microcontroller has translated are merged
into the output layer.
The framework consists of an Arduino UNO
microcontroller board, input, and output. The user may
see the converted digital data thanks to the Arduino board,
which is also connected to the output layer, the serial
monitor of the Arduino board, a Bluetooth module
connected to a mobile MIT App Inventor programme, and
other components. The system's fundamental block
diagram is displayed in Figure 2. The sensors
simultaneously transmit data to the Arduino UNO, and the
Arduino UNO simultaneously transmits the digital data it
has converted to the mobile application using the
Bluetooth module.
Fig- 2: Block Diagram of the prototype
The sensors simultaneously transmit data to the Arduino
UNO, and the Arduino UNO simultaneously transmits the
digital data it has converted to the mobile application
using the Bluetooth module. The circuit diagram and the
prototype has been shown in Figure 3a and b
respectively.
The main part of the system is the Arduino UNO, which is
based on the VR Microcontroller Atmega328. This
programmable microcontroller can be utilised in a variety
of projects because of its ability to interface to other
sensors or computers. It has 32KB of flash memory and
2KB of static random access memory, of which 13KB is
used to store the set of instructions as code. Moreover, it
has a 1KB EEPROM (electrically erasable programmable
read-only memory).

Fig- 3: a. Circuit Diagram b. Design of the prototype
Breathing issues and sleep apnea issues are closely
interrelated to the patient's heart rate. To track the
patient's heart rate while they are sleeping, the device has
a heart rate pulse sensor. One of the main risk factors for
sleep apnea is a rapid heart rate. A healthy person's heart
beats between 60 and 100 times per minute on average
(bpm). Everybody's heart rate is different. Those who are
more physically active typically have lower heart rates
than those who are less active. A prominent indicator of
sleeping difficulties is a higher and unusual fluctuation in
heart rate. During sleep apnea, partial pressure of
dissolved oxygen falls thus the Max 30102 sensor is
integrated in the circuit to monitor real time values. The
sound of snoring, a typical symptom of the disorder, also
varies from which concluding remarks can be drawn
related to the OSA. The system has an analogue sound
sensor that measures snoring noise in order to gauge how
loudly a person is snoring while they are sleeping. The
sound sensor is a small circuit board that converts sound
waves into electrical signals using a microphone and
some processing circuitry. The analogue pin of the
Arduino is used in the system to connect the sensor to the
board. For the mobile application, Arduino converts the
analogue data from the sensor into digital data. The
foundation of this IoT-based research is serial
communication. The system includes a Bluetooth module
called the HC-05 for serving the purpose. The connector
between the Arduino Uno and the Android app is the
Bluetooth module. This Bluetooth module functions in
two modes: one mode transfers or receives data to
another device, while the second mode operates in AT
command mode to set the destination device's settings as
the default. The user can access the digital data in the
mobile application after connection with a Bluetooth
device. The devices function at a current of 30mA and a
voltage range of 4V to 6V. The Bluetooth module's TX
(transmit) and RX (receive) pins handle serial
communication. The TX pin functions to send serial data,
and the RX pin functions to receive data from the
microcontroller. The Arduino's TX pin is linked to the
Bluetooth module's RX pin, and vice versa for the
Arduino's RX pin and Bluetooth module's TX pin. The
mobile application in the system receives digital data
from the Arduino after being paired with the Bluetooth
module.
3. RESULTS
The results obtained from various subjects of different
age groups have been tabulated in Table 1. The
corresponding data has been plotted in Figure 4 for better
readership and understanding the analysis drawn from
the outcome of the research.
Table 2: Variation of various parameters in subjects
Age Group,
Years
Heart
Rate, bpm
Levels of
SpO2, %
Snoring
Intensity,
dB
18-30 88 97 28
82 99 31
84 99 31
78 99 27
91 98 24
31-45 83 96 45
59 91 62
97 95 42
91 86 60
88 91 32
46-60 98 91 56
58 88 66
51 85 62
54 87 61
98 96 42
61 > 53 85 67
56 84 64
95 92 48
52 87 65
48 81 68

Fig-4: Observation of various health parameters for different subject of various age groups.
4. CONCLUSION
From the results, it has been observed that the tendency
of sleep apnea is in subject in upper age group, where
commendable fluctuations of SpO2 and Heart rate from
normal range has been noticed. Fewer cases of the
disorder have been noticed in younger age, which may
be due to other pathophysiological issues, likely, obesity,
genetic disorder, heart condition or wider neck region
(Penzel. 2002). With the aid of portable diagnostic
instruments, changes in oxygen saturation and heart rate
have been utilised to identify sleep apnea early on
(Roose et al. 1993). We found a statistically significant
positive connection between the degree of OSA and the
volume of snoring. Earlier studies on this association
relied on snoring reports from the self or from family
members. The current study is the first to evaluate the
association between OSA and snoring severity in a large
patient population using an objective evaluation of
snoring intensity. Due to the widespread incidence of
snoring in the general population, snoring is a poor
indicator of OSA (Flemons 1994). OSA is intimately
linked to a number of major disorders, including arterial
hypertension, cardiovascular disease, stroke, and
metabolic syndrome, in contrast to non-apneic snoring
(Neito 2000, Peppard 2000). Systemic hypertension is
thought to be a separate risk factor for sleep apnea
(Grote et al, 1999). Simple physical treatment can
effectively alleviate sleep apnea. The air pressure inside
the upper airways is raised by continuous positive
airway pressure (CPAP), which is administered through
the nose while wearing a tight mask (Sullivan et al.,
1981).
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