Adaptive Learning Control and Monitoring of Oxygen Saturation for
COVID-19 Patients
Lubna Farhi, Rija Rehman and Muhammad Ammar Khan
Department of Electronic Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan
Keywords: Oxygen Saturation, Adaptive Learning Controller, PID Controller, COVID-19.
Abstract: This paper proposes an adaptive learning control and monitoring of oxygen for patients with breathing
complexities and respiratory diseases. By recording the oxygen saturation levels in real-time, this system uses
an adaptive learning controller (ALC) to vary the oxygen delivered to the patient and maintain it in an
optimum range. In the presented approach, the PID controller gain is tuned with the learning technique to
provide improved response time and a proactive approach to oxygen control for the patient. A case study is
performed by monitoring the time varying health vitals across different age groups to gain a better
understanding of the relationship between these parameters for COVID-19 patients. This information is then
used to improve the standard of care supplied to patients and reducing the time to recovery. Results show that
ALC controlled the oxygen saturation within the target range of 90% to 94% SpO2, 77% and 80.1% of the
time in patients aged 40 to 50-year-old and 50 to 60-year-old, respectively. It also had faster time to recovery
to target SpO2 range when the concentration dropped rapidly or when the patient became hypoxic as
compared to manual control of the oxygen saturation by the healthcare staff.
1 INTRODUCTION
The start of the year 2020 introduced the globe into
an unprecedented time of biological turmoil, the likes
of which has not been seen since the black plague.
SARS-COV-2 is a strain of virus that once infects a
patient, results in the disease known as COVID-19
(WHO coronavirus-2019/technical, 2019). COVID-
19 was declared a global pandemic by the World
Health Organization (WHO) on 11th March 2020
(WHO coronavirus-2019/events, 2019). As of
November 2020, approximately 54 million people
have been infected by this virus in the world, out of
which 1.3 million people have died
(worldometers.info, 2019). Meanwhile, roughly
350,000 people have been afflicted in Pakistan,
amongst which approximately 7,000 have passed
away from the SARS-COV-2 virus
(worldometers/Pakistan, 2020).
The reason why COVID-19 is considered so
threatening is because currently there are no available
vaccines that can provide protection against the strain
of virus that causes this disease. It is also highly
infectious and affects the lungs thereby causing
Severe Acute Respiratory failure. Once a person is
infected, they experience various symptoms amongst
which the prominent ones are loss of taste sensation,
high sustained fever, and difficulty in breathing.
However, out of all of the aforementioned symptoms,
the latter is the most problematic as it can lead to the
patient experiencing acute hypoxemic respiratory
failure or chronic respiratory failure. Due to the lack
of antibody vaccines and such deadly symptoms, the
National Institute of Health (NIH), World Health
Organization (WHO) and Centre for Disease Control
(CDC) have outlined supportive care guidelines
where healthcare providers are required to observe
the patient under isolation and provide necessary care
to relieve the symptoms as much as possible through
pain medication, rest and adequate food supplement.
By monitoring a patient’s health vitals such as
oxygen saturation (SpO2), body temperature, pulse
rate and blood pressure, health care facilities may be
able to determine the progress of a patient’s recovery.
Body temperature is noted to observe the state of
fever, while pulse rate and blood pressure are
monitored to ensure that the patient is not having
trouble breathing. Lastly, SpO2 is necessary to
monitor to ensure that the patient does not become
hypoxemic and that the lungs are functioning
properly. Oxygen saturation (SpO2) mentions to the
volume of oxygen that is in blood. The body needs an
explicit amount of oxygen in blood to function
appropriately. Oxygen consumption within the body
184
Farhi, L., Rehman, R. and Khan, M.
Adaptive Learning Control and Monitoring of Oxygen Saturation for COVID-19 Patients.
DOI: 10.5220/0010381701840190
In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS, pages 184-190
ISBN: 978-989-758-490-9
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
is Oxygen consumption = (Arterial Oxygen-Venous
Oxygen) * Blood flow. The oxygen-haemoglobin
dissociation is a function of the partial pressure of
oxygen (PO2). Haemoglobin will be 100% saturated
with oxygen if PO2 =100 mmHg “Each gram of
haemoglobin is capable of carrying 1.34 mL of
oxygen. The solubility coefficient of oxygen in
plasma is 0.003. This coefficient represents the
volume of oxygen in mL that will dissolve in 100mL
of plasma for each 1 mmHg increment in the
PO2.” Oxygen Content = (0.003 × PO2) + (1.34 ×
Haemoglobin × Oxygen Saturation) (Kaufman,
2020).
Currently, health care providers monitor SpO2
and control the supply of oxygen to critical care
patients by manually adjusting the supply of oxygen
from the cylinder or source. This is not only
inefficient, but is also risky, prone to error and in
cases of a high number of patients can lead to
overloading of the staff and healthcare system.
Therefore, to reduce the burden on the healthcare
system and facilitate quicker recovery this
methodology was proposed, which utilizes an
adaptive learning controller that would monitor and
change the oxygen saturation for patients hospitalised
with COVID-19.
Automated systems have been previously shown
to have better outcomes on patients as compared to
manually controlled systems. This was demonstrated
by Alexander et. al. in their study (Alexandre, 2020).
They proved that when a post-surgery patient’s
anaesthesia is automatically controlled, they not only
recover quicker but also have fewer post-surgery
complications as compared to manual anaesthesia
delivery control. In addition, the benefits of the use of
automated systems to continuously monitor health
vitals of recovering patients was discussed by
(Appelboom et al, 2014) in their paper. They
proposed and demonstrated that wearable technology
can improve the quality of supportive care through
continuous monitoring of vitals. These vitals can then
be reported to health professionals who will have a
more detailed history of their patient resulting in a
well-defined and succinct care plan. Furthermore,
Kaushal et al highlighted the benefits of using
automated technology for healthcare in their study
(Kaushal, 2002). They analysed the impact of
information technology and automation on the full
spectrum of healthcare delivery from diagnosis to
post-operative care and concluded that IT integration
into healthcare systems not only reduce
complications but also reduce the burden on the
healthcare staff. Similarly, by James et al showed that
through automation intervention, medical staff’s
workload can be drastically reduced resulting in
fewer errors and improved work-life balance (James,
2013).
In this paper we are taking the same approach as
the previously mentioned research papers and are
conducting a study regarding the efficacy of
automated oxygen monitoring and saturation-control
for COVID-19 patients. An adaptive learning control
system will be utilized to monitor and control the vital
signs i.e. SpO2 and pulse rate and temperature of
COVID-19 patients requiring critical care. In such
scenarios where patients’ condition is rapidly
changing in response to the medical treatment or
ventilation supportive care, it is risky as well as time
consuming for hospital staff to continuously monitor
their progress. Moreover, a rapid increase in COVID-
19 cases is also leading to overloading the systems
and staff leading to a reduction in the quality of
supportive care. An adaptive control model could
make the monitoring of vital signs more efficient and
accurate for staff, while also keeping in consideration
the SOPs for COVID-19. This approach could
ultimately improve the recovery time of patients
thereby reducing the load on hospitals.
2 METHODOLOGY
The approach to the proposed methodology was
twofold develop a robust control system and
integrate it with health vitals. This required that the
adaptive learning controller not only have accurate
and reliable control, but it must also be able to intake
continuous variable oxygen data and appropriately
adjust the output in real-time.
Since the controller is
responsible for adjusting a sensitive parameter that
has a direct impact on the patient’s health, it must
have the capability for minute adjustments while also
being able to learn the oxygen variation to minimize
errors. The following sections explain how the
controller was developed and combined to monitor
and adjust oxygen in real-time.
2.1 Adaptive Learning Controller
(ALC)
To achieve the precise results, input must control the
optimized values of gain of PID controller. Noise
disturbances influence that are not modelled, make it
complex to maintain the PID control gains by
𝜃 = 𝐾

𝐾

𝐾
at optimal values throughout. It
may turn into a serious issue to sustain the quality of
controller, to solve this issue, an adaptive learning
PID controller has proposed that enhance the
controller performance and improved accuracy due to
its memory feature. In PID controller 𝜇 , 𝑦 and 𝑒
denote the control input, output and error signal,
Adaptive Learning Control and Monitoring of Oxygen Saturation for COVID-19 Patients
185
conventional control of PID can be express as
follows:
𝜇

𝑖
= 𝜇
𝑖
𝐾
𝑒
𝑖1
𝐾
𝑒
𝑛


𝐾
𝑒
𝑖1
 𝑒
𝑖
,𝑖 0,𝑁1
(1)
Figure 1: Adaptive learning controller.
Where,
𝑒
𝑖
=𝑦
𝑖
𝑦
𝑖
, 𝑖
0, 𝑁  1
.
(2)
Applying (1) in the initial trial, showed that the
control input is similar as in the PID controller. In the
second trial of actual system, responses were not
according to the system output values, hence error
was integrated with the second input of the system.
This is the change analysed between output value
𝑦
𝑖
and actual system output in the initial trial, in
this way (1).
Proposed adaptive learning controller generated
control input in this manner just after the second trial.
So, the suggested learning control system can be
expressed by,
𝜇
𝑖
= 𝜇

𝑖
𝐾
𝑒

𝑖1
𝐾
𝑒

𝑛


𝐾
𝑒

𝑖1
𝑒

𝑖
,
𝑤ℎ𝑒𝑟𝑒 𝑖 0,𝑁  1
(3)
𝑒

𝑖
= 𝑦
𝑖
𝑦

𝑖
(4)
This can be clearly seen in Figure 1. Having learning
operation based on the previous states, it is expected
to achieve the stable enhanced control results due to
the learning based control technique.
2.1.1 ALC using Recursive Least Square
(RLS) Algorithm
The adaptation mechanism is as follows. After the
detection of some error between standard and
measured SpO2, Controller response has decayed the
transient period. PID controller parameter vector to
be tuned in the controller is by 𝜃 = 𝐾

𝐾

𝐾
in
eq. (1). In eq. (4) where y
k
is the closed-loop response
under the controller parameters y
d
is the actual time
response of the controlled system.
Based on the RLS algorithms, we tune the
parameters 𝜃 which are the PID gain values so that
the following performance index J is minimized
=𝑦
𝑖
𝑦

𝑖

(5)
Where N is the number of time-response samples.
RLS is an algorithm which recursively finds the
optimal estimate (𝑘) of the controller parameter by
using 𝜃(𝑘−1)
2.2 Oxygen Control and Deliverance
Oxygen saturation (SpO2) is monitored via an
oximeter designed to take reading with a sampling
rate of 500 Hz (reading taken every 2ms). The
oximeter utilizes an IR LED and a photodiode that are
difference between the actual concentration and the
desired oxygen saturation levels. These decisions are
based on the information shown in Table 1. Below a
SpO2 of 85%, the patient is hypoxic and requires
immediate attention from the healthcare staff. For this
reason, the controller is tasked to sound an alarm, call
emergency, and maximize the oxygen output to the
patient to ensure that the lungs are getting enough
oxygen. Between 85% and 90% oxygen saturation
levels, the patient is considered to be on the cusp of
critical attention which is why an attendant is required
to be on-site while the controller maintains maximum
oxygen output. Once the SpO2 levels have reached
90% to 94%, then the oxygen is said to have been in
a safe range where conservative oxygenation shall be
employed. In this case, oxygen delivery shall be
gradually reduced such that the SpO2 level is
maintained at 94% or greater than 94% which is the
target (
Hansen, 2018)
.
Table 1: Controller parameters 𝜃 = 𝐾

𝐾

𝐾
by
adjustment of Oxygen regulator to attain the Saturated
Levels.
Oxygen Saturated Levels (Health line, 2019)
SpO₂<85% 85% < SpO₂ ≤ 90% 90% < SpO₂ < 94% SpO₂= target
Call
emergency,
sound alarm
and Maximize
oxygen output.
Max. Parameters
Adjustment,
attendant presence
required.
Min. parameters
Adjustment
Maintain
parameters
BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms
186
2.3 Process Flow Diagram
Figure 2 represents the overall integration of the
adaptive learning controller with the oxygen
deliverance and monitoring system. Upon receiving a
reading from the input sensor (oximeter) from the
patient, the controller calculates an error value E. This
error is determined by comparing the patient’s SpO2
levels with the standard required (minimum of 94%
oxygen saturation) in a normal patient. Then, on the
basis of this error value the adaptive learning
controller provides the oxygen delivery by adjusting
its controller gains. This leads to a change in the SpO2
levels of the patient which are then used to calculate
the error again and adjust the oxygen delivery until
this iterative process results in an error of E=0. This
signifies that the patient’s oxygen saturation is above
94% and they are stable at which point the controller
maintains its settings to provide a constant supply
oxygen.
Figure 2: Flow diagram of adaptive learning control and
monitoring of Oxygen saturation.
Adaptive Learning (ALC) controller is using the
recursive least square (RLS) algorithm. RLS
algorithm is used to update the PID gains in real time
(as system operates) to force the actual system to
behave like a desired reference model.
It shows that the adaptive learning controller
adjusts the PID parameters i.e. gains of PID controller
Kp, Ki and Kd of oxygen regulator to attain the
saturated Levels. The error generated is
proportionally related to the variation of SpO2 from
the target value. In case, SpO₂ < 85% or 85% < SpO₂
90% the amount of error generated is large which
causes the Kp to increase thus increasing the
regulating speed of the motor to provide a faster
response. Additionally, Kd increases to its maximum
value to reduce overshoot and maintain the speed of
regulating motor. Lastly, Ki increases to reduce
steady state error and control the overshoot to
maintain the stability of valve control and
correspondingly, oxygen levels.
For oxygen saturation level 90% < SpO₂ < 94%
minimum gain adjustments will be required and
similarly when error is zero and SpO2 = target level
then gains of PID controller will be sustained on their
existing values.
3 EXPERIMENTAL SETUP
The designed circuit provides a new and improved
respiration system which automatically regulates the
fractional inspired oxygen to a patient.
The system hardware consists of a
Microcontroller (Arduino Uno), Pulse
oximetry sensor (Pulse oximeter max 30100), LCD,
servo motor, keyboard and other components (sound
indication and LED indication). The hardware setup
of oxygen control is illustrated in Figure 3.
Figure 3: Circuit diagram and measured signal with sound
and LED indication. Alarm is triggered when SpO₂ < 85%.
Adaptive Learning Control and Monitoring of Oxygen Saturation for COVID-19 Patients
187
Pulse oximetry sensor measures the oxygen
saturation of a patient's blood. This device consists of
a red and an infrared light source, photo detectors, and
a probe to transmit light through a translucent,
pulsating arterial bed, typically a fingertip or
earlobe that uses 5V/3.3V serial communication. The
dissolved oxygen measurement is triggered by
receiving a measurement via the RX port of the
Arduino while the motor control is provided by the
TX port. The sensor echoes the command and
appends the measured oxygen concentration. If the
measured oxygen concentration is below a certain
threshold i.e. SpO₂ < 94% a valve is opened which
will supply additional oxygen to the patient through a
connected oxygen supply.
4 RESULT AND ANALYSIS
In the presented approach, a comparison between
automatic and manual control was used to
demonstrate the efficacy of adaptive learning
controller for oxygen concentration in COVID-19
patients. It was observed that the automatic mode via
ALC control was the better option as it allowed the
patients, for all age groups, to recover in less amount
of time. The automatic mode also took a conservative
oxygenation approach where only enough oxygen
was provided to bring the patient back to 92% - 96%
SpO2. This approach has been proven to be a better
option towards the needs of patients suffering from
Acute Respiratory Failure as it does not overload the
lungs or blood saturation of the patients. In contrast,
a liberal approach that is often taken by manual
adjustment of oxygenation, where a high pressure of
oxygen is provided when it is unnecessary, can result
in detrimental effects on the health of the patient and
in some cases even lead to an increase in mortality
rate (Shenoy, 2020).
This study comprised of observing and surveying
different age group of high-risk 20 patients,
particularly ages 40-60, suffering from COVID-19 in
2020. As a result, the percentage of time spent within
the target SpO2 range was observed for the
aforementioned age groups.
The data (Figure 4) indicates that the automated
oxygenation methodology is a better approach than
manual control for a specifically prescribed interval.
In this study, the target range for oxygen saturation
was defined as 90% to 94% SpO2. The graphs show
that for both age groups of 40-50 and 50-60, ALC
controller performed significantly better by
maintaining the saturation level within the target
range 77% and 80.1% of the time. Meanwhile, the
manual methodology was only able to keep the
(a) Patient age group from 40 to 50 years.
(b) Patient age group from 50 to 60 years.
Figure 4: Fraction of time with Oxygen saturation levels of
(a) 40 to 50 and (b) 50 to 60 COVID-19 Patients. It provides
a detailed comparison of the percentage of time spent by
patients in various oxygen saturation ranges when their
oxygenation was controlled manually or via ALC method.
It can be observed that for patients aged 40-50, the
automatic mode opted for a more liberal oxygenation
approach to bring the patient’s SpO2 levels within target.
BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms
188
patients within target saturation 49.5% and 59.6% of
the time for ago groups 40-50 and 50-60 respectively.
Additionally, it can also be seen that for patients
within 40-50 age groups, the manual control by staff
took a more liberal oxygenation approach despite its
potential drawbacks. There can be several reasons
that can range from the severity of the oxygen
required by the patient to the fact that the staff is busy
and overloaded which is why they prefer to set at high
pressures to ensure that the patient does not become
hypoxic in their absence. On the other hand,
automatic controller spent more fraction of time
above target range for 50-60 age groups thus
indicating a more liberal approach as compared to
manual control. Older patients often struggle with
breathing and other respiratory limitations that can be
further exacerbated through COVID-19. In this case,
the learning behavior of the ALC controller is
emphasized as it is using a proactive approach to
maintain high oxygenation to prevent patients from
becoming hypoxic. It is also important to note that the
percent of time spent by patients at the saturation
level of hypoxia (SpO2 < 85%) or approaching
hypoxia (85% < SpO2 90%) was significantly
lower for automatic control as compared to manual
control by staff.
Figure 4 also indicates that the patient remains
within the target range of oxygen saturation for a
longer duration when the oxygen is controlled using
the adaptive controller as compared to manual
control. This is beneficial for the patient because now
they are receiving the optimal level of oxygen for a
longer duration resulting in less pressure on their
lungs therefore reducing the load.
Figure 5 is a time response graph. The graph
shows that the automatic controller brings the patient
back to the target oxygen saturation in a shorter
amount of time as compared to the manual control.
This shows that if the oxygen varies, then it is quickly
returned to the required amount resulting in less effect
on the lungs. Less load on the lungs and quicker
response time can lead to faster patient recovery.
Figure 5: Time response of adaptive learning controller and
manual control by staff.
ΔT= T(O2) T(O1), where T(O2) is the time at
which the oxygen returned to target range and T(O1)
is the time at which oxygen levels dropped/rose from
the target range. Automatic Control should have
lower delta T while manual should have higher
indicating that the automatic control, continuously
adjusts the oxygen levels resulting in faster response.
The step response of adaptive learning controller
tuning shown in Figure 6 show that the system will
reach the stability quickly than the system under the
conventional PID controller and the peak overshoot is
decrease, where the system takes short time to reach
the steady state and that the system got good response
as shown in Figure 6.
Figure 6: Step responses of Adaptive Learning PID
controller Vs simple PID controller.
Table 2: The comparison of different techniques and with
respect to accuracy and complexity of the techniques
applied with other controllers.
References (Zhang,
2015)
(Iobbi,
2006)
(Dong,
2012)
Proposed
Technique Smart
phone
and
browser
server
controlle
d with a
valve
Fuzzy
Contro
l with
PID
Learning
PID
controller
Accuracy High High High High
Complexity High High High Low
5 CONCLUSIONS
The proposed system is designed to provide a
proactive supportive care to COVID-19 patients
instead of reactive care. The key difference between
the two types of care is the fact the former is
Adaptive Learning Control and Monitoring of Oxygen Saturation for COVID-19 Patients
189
predictive of the variation in oxygen saturation level
of the patient and therefore can make decisions before
or instantaneously to prevent any further detriment of
the patient’s condition. This study focused on the age
groups between 40-50 and 50-60 as they are most
susceptible to chronic respiratory or acute hypoxic
respiratory failure caused by SARS-COV-2. An
adaptive learning controller was used to monitor and
control the oxygenation of these patients and the
response to recovery was recorded and compared
with manual control of oxygenation by healthcare
staff.
It can be seen from Figure 4 that patientsSpO2
levels were maintained within the target range for
77% and 80.1% whereas for manual control the time
spent by patients within target range was a mere 49.55
and 50.6% for 40-50 year olds and 50-60 year olds
respectively. This is a clear indicator that the
automated control methodology not only maintains
the concentration more consistently, but it also
provides fine adjustments (shown in Figure 5) to
counter any variations that it has experienced in the
past through its predictive algorithm. Figure 6 also
shows that the controller achieves steady state
without a high over-shoot which is beneficial for the
patient as in the case of rapid health deterioration, it
is imperative that the controller be able to meet the
accurate demand of the patient as quickly as possible.
Finally, the PID approach is not only accurate but it
is also easy to implement as compared to other
approaches thus making it cost effective and easy to
implement in case of emergencies as in the case of the
current pandemic.
The results demonstrated that the automatic
control methodology had two major advantages that
are considered key to faster patient recover. The first
advantage is that it was able to prevent patients
becoming hypoxic by quickly adjusting oxygenation
and predicting their oxygen saturation variation based
on their SpO2 variation history. Secondly, the
automatic controller was able to maintain the patients
in the target range for a greater amount of time thus
ensuring that their oxygen concentration levels
remain consistent for greater durations of time. These
two combined benefits can be attributed to faster
recovery of patients as it leads to less stress on their
lungs.
REFERENCES
https://www.who.int/emergencies/diseases/novel-coronavi
rus-2019/technical-guidance/naming-the-coronavirus-
disease-(covid-2019)-and-the-virus-that-causes-it.
https://www.who.int/emergencies/diseases/novel-coronavi
rus-2019/events-as-they-happen.
https://www.worldometers.info/coronavirus.
https://www.worldometers.info/coronavirus/country/Pakist
an.
Alexandre Joosten, Joseph Rinehart, Aurélie Bardaji,
Philippe Van der Linden, Vincent Jame, Luc Van
Obbergh, Brenton Alexander, Maxime Cannesson,
Susana Vacas, Ngai Liu, Hichem Slama, Luc Barvais;
Anesthetic Management Using Multiple Closed-loop
Systems and Delayed Neurocognitive Recovery: A
Randomized Controlled Trial. Anesthesiology 2020;
132:253–266
Appelboom, G., Yang, A., Christophe, B.R., Bruce, E., &
Connolly, E. (2014). The promise of wearable activity
sensors to define patient recovery. Journal of Clinical
Neuroscience, 21, 1089-1093.
Kaushal R, Bates, (2002), DW,Information technology and
medication safety: what is the benefit? BMJ Quality &
Safety 2002;11:261-265.
James, K. L., Barlow, D., Bithell, A., Hiom, S., Lord, S.,
Oakley, P. & Whittlesea, C. (2013). The impact of
automation on pharmacy staff experience of workplace
stressors. International Journal of Pharmacy
Practice, 21(2), 105-116.
Kaufman DP, Kandle PF, Murray I, et al. Physiology,
Oxyhemoglobin Dissociation Curve. [Updated 2020 Jul
26]. In: StatPearls [Internet]. Treasure Island (FL):
StatPearls Publishing; 2020 Jan-. Available
from:https://www.ncbi.nlm.nih.gov/books/NBK49981
8/
Zhang, Y., Liu, H., Su, X., Jiang, P., & Wei, D. (2015).
Remote mobile health monitoring system based on
smart phone and browser/server structure. Journal of
healthcare engineering, 6.
Iobbi, M. (2006). U.S. Patent Application No. 10/907,693.
Dong, X., Jian-qu, Z., & Feng, W. (2012). Fuzzy PID
control to feed servo system of CNC machine
tool. Procedia Engineering, 29, 2853-2858.
https://www.healthline.com/health/normal-blood-oxygen-
level#tools-for-measurement
Hansen, E. F., Hove, J. D., Bech, C. S., Jensen, J. U. S.,
Kallemose, T., & Vestbo, J. (2018). Automated oxygen
control with O2matic® during admission with
exacerbation of COPD. International Journal of
Chronic Obstructive Pulmonary Disease, 13, 3997.
Shenoy, N., Luchtel, R. & Gulani, P. Considerations for
target oxygen saturation in COVID-19 patients: are we
under-shooting?. BMC Med 18, 260 (2020).
https://doi.org/10.1186/s12916-020-01735-2
https://www.digikey.in/product-detail/en/tt-electronics-op
tek-technology/OP165A/365-1046-ND/498673.
BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms
190