Prediction of Oxygen Saturation from Graphene Respiratory Signals

with PPG Trained DNN

Bojana Koteska

1 a

, Ana Madevska Bogdanova

1 b

, Teodora Vi

centi

2 c

, Stefan D. Ili

2 d

Miona Tomi

3 e

and Marko Spasenovi

2 f

Faculty of Computer Science and Engineering (FCSE), ”Ss. Cyril and Methodius” University, Skopje, North Macedonia

Center for Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy,

National Institute of the Republic of Serbia, University of Belgrade, 11001 Belgrade, Serbia

School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia

ﬁ

Keywords:

Oxygen Saturation, Graphene, PPG, Deep Learning Model.

Abstract:

This paper explores the feasibility of using wearable laser-induced graphene (LIG) sensors to estimate oxygen

saturation (SpO2) as an alternative to traditional photoplethysmography (PPG) oximeters, particularly in mass

casualty triage scenarios. Positioned on the chest, the LIG sensor continuously monitors respiratory signals in

real-time. The study leverages deep neural network (DNN) trained on PPG signals to process LIG respiratory

signals, revealing promising results. Key performance metrics include a mean squared error (MSE) of 0.152,

a mean absolute error (MAE) of 1.13, a root mean square error (RMSE) of 1.23, and an R

score of 0.68. This

innovative approach, combining PPG and respiratory signals from graphene, offers a potential solution for 2D

sensors in emergency situations, enhancing the monitoring and management of various medical conditions.

However, further investigation is required to establish the clinical applications and correlations between these

signals. This study marks a signiﬁcant step toward advancing wearable sensor technology for critical health-

care scenarios.

1 INTRODUCTION

The real-time monitoring of health promises a fun-

damental transformation in preventing and managing

diseases. This impact is especially important in triage

scenarios involving a large number of casualties, as

it prioritizes the most severely injured individuals by

quick identiﬁcation, using START-like triage systems

(Benson et al., 1996). Notably, the capacity to contin-

uously monitor essential indicators like heart rate, res-

piration, blood oxygen saturation (SpO2), and blood

pressure in real time can be a lifesaver in critical sit-

uations. Detecting abnormalities in these parameters

in a timely manner offers an early alert for potential

medical emergencies, allowing healthcare practition-

https://orcid.org/0000-0001-6118-9044

https://orcid.org/0000-0002-0906-3548

https://orcid.org/0000-0002-3460-6137

https://orcid.org/0000-0002-1721-9039

https://orcid.org/0009-0001-4233-0249

https://orcid.org/0000-0002-2173-0972

ers to respond swiftly (Na et al., 2021).

In the triage process, the initial assessment of

patients to determine their health status and priori-

tize treatment based on the severity of their condi-

tion (Benson et al., 1996), the measurement of SpO2

holds a crucial role. The primary objective of as-

sessing this vital parameter is to identify individu-

als who are experiencing severe respiratory distress

or shock - patients in shock exhibit low SpO2 lev-

els. The conventional method for measuring SpO2

involves the use of a PPG sensor, which analyzes

the light absorption characteristics of oxygenated and

deoxygenated hemoglobin. Nevertheless, there are

certain limitations associated with PPG-based SpO2

measurements. One drawback is that the accuracy of

PPG-based SpO2 measurements may not match those

obtained from arterial blood gas analysis or pulse

oximetry using dedicated sensors (Castaneda et al.,

2018).

In this paper, we are investigating the idea of using

wearable mechanical deﬂection sensors for SpO2 es-

timation as an alternative for oximeters with PPG sig-

Koteska, B., Bogdanova, A., Vi

centi

c, T., Ili

c, S., Tomi

c, M. and Spasenovi

c, M.

Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN.

DOI: 10.5220/0012354100003657

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

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pages 739-746

ISBN: 978-989-758-688-0; ISSN: 2184-4305

739

nals, as a part of integrated wearable patch-like sen-

sors (Koteska et al., 2022).

There are numerous sensors that can monitor vi-

tal parameters in real-time, including electrocardio-

gram (ECG) sensors for monitoring heart rate, oxime-

ters for monitoring SpO2, and blood pressure sen-

sors for measuring blood pressure (Dias and Paulo

Silva Cunha, 2018; Majumder et al., 2017). These

sensors can be integrated into wearable devices, al-

lowing for continuous monitoring of vital parameters

outside of traditional clinical settings.

Wearable mechanical deﬂection sensors measure

changes in mechanical deﬂection, such as chest or

abdominal movements, to estimate respiration rate

and volume. These sensors are non-invasive and can

be easily integrated into wearable devices such as

belts or patches, making them suitable for continu-

ous monitoring of respiratory parameters, as well as

other vital parameters (Vi

centi

c et al., 2022). Laser-

induced graphene (LIG), an emerging material re-

cently used in mechanical deﬂection sensors, is an ex-

cellent candidate for wearable respiration monitoring

(Song et al., 2023; Chen et al., 2019). LIG is a type

of graphene that is created by irradiating a polymer or

other organic material with a laser. The laser energy

causes the material to carbonize and transform into a

graphene-like structure. LIG is a ﬂexible piezoelec-

tric material, meaning that the electrical resistance of

the material changes when it is bent. Hence, electri-

cal signal from LIG is directly related to mechanical

deﬂection, a property that can be utilized to measure

motion of the human body and its parts.

In the work of Vi

centi

c et al., (Vi

centi

c et al.,

2022), it was established a correlation between the

LIG signals and the heart rate (among other parame-

ters) using the HeartPy toolkit implemented in Python

(Van Gent et al., 2019).

Our current research aims to explore an additional

application of LIG signals as a potential simulation

for PPG signals, by extracting the features from LIG

signals derived from HeartPy as an input in the deep

neural network (DNN) model trained on PPG sig-

nals, to estimate oxygen saturation (SpO2). While

PPG signals and LIG signals are not directly cor-

related, there is a potential to combine these tech-

nologies for novel biosensing applications, since there

is a relationship between the magnitude of respi-

ratory peaks and the oxygen concentration (Rasch-

Halvorsen et al., 2019). As of our knowledge, there

aren’t any recently published studies that have speciﬁ-

cally investigated the use of LIG in biosensors for de-

tecting photoplethysmography (PPG) signals. Lean-

ing towards the implementation of the LIG as a 2-D

sensor, we decided to explore its ability to serve as an

SpO2 estimator, using an ANN model built on PPG

signals.

The paper is organized as follows. Section 2 de-

scribes the production of the graphene sensor respi-

ration monitoring process. It also describes the train-

ing and testing databases and the used DNN model

for SpO2 estimation. Section 3 elaborates on the ob-

tained results and model evaluation metrics. The ﬁ-

nal section 4 presents concluding remarks and future

work.

2 MATERIALS AND METHODS

2.1 Graphene Sensor Production

LIG was produced by scanning a CO2 laser beam

across the surface of polyimide tape, as in (Wang

et al., 2020). The utilized laser DBK FL-350 had a

maximum power of 60 W, with power set to 18%,

a scanning speed of 400 mm/s, and a resolution of

800 DPI. The devices were formed by laser-writing

LIG in the shape of rectangles with dimensions 1 X

3 cm. The graphene was transferred to double-sided

adhesive medical tape (Duplomed 8411, Lohmann,

GmbH, Neuwied, Germany). To establish electrical

connections with the LIG, conductive copper tapes

were afﬁxed to the device’s ends. Copper tapes

were then soldered to wires, which were subsequently

linked to the measuring device. The sensor was se-

curely adhered to the body at seven distinct locations,

as illustrated in Figure 1.

Figure 1: Positions on the body where the sensor was af-

ﬁxed.

The wires from the LIG sensor were connected to

a Keithley 2450 SMU, which was interfaced with a

desktop computer.

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

740

2.2 Respiration Monitoring

Three healthy subjects, comprising one male and two

females aged between 20 and 30 years, were included

in the measurement of LIG respiration signals, as

our primal interest is triaging healthy injured victims

of mass victim incidents. As mentioned in Subsec-

tion 2.1, the LIG sensor was attached in one of the

seven different locations during the rested position

(1). We also considered the technique of holding the

breath, to obtain the values of critical SpO2 domain

(SPO2<95%) (Chan et al., 2021; Parkes, 2006).

For measurements when the SpO2 was lowered

below 95%, the subjects held their breath for 30 sec-

onds, resuming breathing after that, as in Figure 2.

The ﬁrst 30 seconds of breathing after holding breath

were recorded. As a reference, SpO2 was measured

with the Onyx II oximeter (Nonin Medical, Plymouth,

MN, USA).

The measurements were performed in a constant

current mode, where a current of 0.1 mA was main-

tained, and the voltage was recorded for a duration

of 30 seconds, as a volunteer subject sat in a chair

and breathed normally. The measaruments were re-

peated order to ensure the reliability and consistency

of the collected data, thereby enhancing the robust-

ness of our ﬁndings. The voltage variation in time as

breathing is monitored is shown in Figure 2.

Figure 2: Voltage variation in time, as breathing is moni-

tored with the LIG sensor.

The experimental steps starting from LIG sen-

sor creation to the respiration signal processing, are

shown in Figure 3.

2.3 Training Dataset

The research utilized the BIDMC PPG and Respira-

tion Dataset, which can be accessed at the follow-

ing URL: https://physionet.org/content/bidmc/1.0.0/.

This dataset was collected from 53 critically ill pa-

tients receiving treatment at the Beth Israel Deaconess

Medical Centre in Boston, MA, USA. It comprises

signals and numerical data extracted from a subset of

the widely recognized Physionet’s MIMIC II Wave-

form Database. The dataset includes physiological

parameters from adult patients aged 19 to 90. Specif-

ically, the ECG, PPG, and impedance pneumography

signals are 8 minutes in duration and sampled at a

rate of 125 Hz. Additionally, the heart rate, respi-

ratory rate, and blood oxygen saturation level (SpO2)

are sampled at 1 Hz.

Most of the patients in the BIDMC database have

normal oxygen saturation (>= 95). The SpO2 values

for each patient are shown in Figure 4.

The data was acquired in Python pickle format us-

ing the wfdb.rdsamp method from the native Python

waveform-database (WFDB) software package and

was saved to local storage. For further details, you

can refer to the following link: https://github.com/

MIT-LCP/wfdb-python.

To predict SpO2 from shorter signals we needed

to train the model on shorter PPG segments. The 8-

minute PPG signals from the BIDMC database were

split into 10-second segments, given that some com-

mercial SpO2 sensors provide SpO2 readings at ap-

proximately 10-second intervals (as mentioned in the

study (Shao et al., 2015)).

To create a dataset with PPG signals of 10-second

duration, the following steps were taken. For every

patient in the original BIDMC database, the PPG sig-

nal comprises 60001 individual samples, accompa-

nied by 480 corresponding SpO2 records. The origi-

nal PPG signal is segmented into 10-second chunks,

and for each chunk, the 10th record in the SpO2 se-

quence is selected, as the goal is to predict the SpO2

value based on the previous 10 seconds of the input

signal. Consequently, this conﬁguration generates 48

PPG signals (calculated as 60001/125/10) from each

patient, and the SpO2 value associated with each PPG

signal is considered.

To enhance the quality of the PPG waveforms

before preparing the PPG signals for feature extrac-

tion, preprocessing of the PPG signals is performed.

Firstly, the signals are normalized to have a zero mean

and unit variance. Subsequently, to eliminate high-

frequency noise and baseline wandering, a 4th-order

Butterworth band-pass ﬁlter with cut-off frequencies

of 0.5Hz and 8Hz is employed. Finally, the Hampel

decision ﬁlter is used to remove any outliers present in

the PPG signal. The selection of these preprocessing

steps and ﬁlters is based on the procedure described

in (Slapni

car et al., 2019).

The last step before the deep learning model gen-

Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN

741

Figure 3: Experimental steps.

Figure 4: Scatter plot of the SpO2 values per patient in the training dataset.

eration is to perform PPG signal extraction with the

Python toolkit HeartPy (Van Gent et al., 2019). The

process method is employed to perform robust pre-

processing of the input signal and generate a list of

features that are utilized for training the models to

identify signiﬁcant patterns in SpO2 predictions. The

HeartPy Toolkit is designed to produce the 13 features

presented in Table 1.

The dataset contained 2544 records, but 84

records were deleted because there were NaN values

for some features. The ﬁnal dataset contains 2460

records, and it is made up of the extracted features

from the 10-second long PPG signals and the match-

ing SpO2 values as a result of the preceding steps.

2.4 Testing Dataset

For the creation of the testing dataset, we used the

novel database consisting of 40 LIG respiratory sig-

nals.

The majority of SpO2 measurements in the LIG

respiration dataset are normal (>= 95 %), but also

there are several measurements below 95% as can be

seen in Figure 5.

The same procedure employed for the training

dataset was utilized to extract the features from the

LIG respiration signals.

2.5 Deep Learning Model

This research employed a supervised deep learning

approach, utilizing a Deep Learning Artiﬁcial Neu-

ral Network (ANN) to estimate the SpO2 value as a

regression problem. The ANN is designed to output a

single value representing the predicted SpO2 value,

while the input is a matrix containing 13 features

extracted from the input signal using the HeartPy

toolkit.

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

742

Table 1: Description of HeartPy Features.

Feature Description

bpm Heart rate in beats per minute.

IBI Mean distance of intervals between heartbeats.

SDNN Standard deviation of intervals between heartbeats.

SDSD Standard deviation of successive differences between adjacent R-R intervals.

RMSSD Root mean square of successive differences between adjacent R-R intervals.

pNN50/pNN20 Proportion of differences greater than 50ms/20ms.

MAD Median absolute deviation.

SD1, SD2, S, SD1/SD2 Derived from Poincare analysis and represent the breathing rate.

Figure 5: Scatter plot of the SpO2 values in the testing dataset.

The training dataset of 2460 records described in

the section 2.3 was split in the following ratio: 90%

of the data was used for training and 10% for the vali-

dation. Before splitting the data, data standardization

was performed.

The testing dataset containing 40 records is de-

scribed in the subsection 2.4.

The implementation of the deep learning ANN in-

volved the use of the Keras API written in Python,

which runs on top of the TensorFlow machine learn-

ing platform. The architecture of the Deep Learning

ANN employed in this study is illustrated in Figure 6.

The Keras library’s Sequential module was em-

ployed to build a sequence of ANN layers that are

stacked in consecutive order. To specify the number

of neurons, the Dense Keras module is used to deﬁne

each layer. A dropout layer is also added to the model

to prevent overﬁtting. As depicted in Figure 6, the

deep learning neural network is fully connected, com-

prising four hidden layers with a particular number of

neurons and one output layer with only one neuron

that predicts the SpO2 value.

We utilized the 13 input features as predictors in

the input data for the Sequential model, which is then

passed to the subsequent layers. To perform calcula-

tions within each neuron, we employed the Rectiﬁed

Linear Unit (ReLU) function as the activation func-

tion, which is the most widely used activation func-

tion according to (Kleine B

uning et al., 2020). The

ReLu function produces an output of zero if the input

value is less than zero; otherwise, it is equal to the

provided input value. To compute the loss, we used

Mean Squared Error (MSE) as it is the most com-

monly used loss function for regression, as per (Kol-

bæk et al., 2020).

To determine the optimal accuracy, we conducted

a tuning process on the ANN model, exploring dif-

ferent combinations of the ’epoch’ and ’batch size’

values. Speciﬁcally, we used Grid Search Cross-

Validation to test various values for these hyperpa-

rameters. The ’batch size’ parameter refers to the

number of training examples used in a single for-

ward/backward pass, while ’epochs’ indicates the

number of times the learning algorithm runs through

the entire training dataset.

2.6 Regression Metrics

To assess the accuracy of the model, we used the

standard regression metrics: MAE (Mean absolute er-

ror), MSE (Mean Squared Error), RMSE (Root mean

squared error), R

(R-squared), and RMLSE (Root

Mean Log Squared Error).

The Mean Absolute Error (MAE) provides an

average of the absolute differences between the ac-

tual (y

) and predicted ( ˆy

) values. It is given by the

Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN

743

Figure 6: Deep Learning ANN architecture.

formula:

MAE =

∑

i=1

− ˆy

The Mean Squared Error (MSE) quantiﬁes the

average of the squared differences between actual and

predicted values:

MSE =

∑

i=1

− ˆy

)

The Root Mean Squared Error (RMSE) is the

square root of the MSE and is expressed as:

RMSE =

√

MSE

R-squared (R

), a widely-used metric, measures

the proportion of variance in the dependent variable

explained by the model. Its formula is:

= 1 −

residual

total

where SS

residual

is the sum of squared residuals, and

total

is the total sum of squares.

The Root Mean Log Squared Error (RMLSE)

is particularly useful when dealing with a wide range

of target variable values. It is calculated as:

RMLSE =

∑

i=1

(log(1 + y

) −log(1 + ˆy

))

3 RESULTS

Table 2 presents the actual and predicted SpO2 values

from the testing dataset.

Table 3 shows results from the model’s perfor-

mance evaluation metrics in predicting SpO2 from

LIG respiratory signals.

Table 2: Comparison of actual and predicted SpO2 values

for the testing dataset.

Actual

SpO2

Predicted

SpO2

Actual

SpO2

Predicted

SpO2

95 92.91 98 96.04

90 91.94 98 96.13

99 97.19 100 98.27

98 96.44 98 96.52

97 95.55 98 96.61

100 98.62 98 99.38

97 95.64 97 98.34

96 97.34 97 95.68

92 93.32 91 92.26

98 96.74 98 96.80

96 97.19 97 98.14

97 95.88 96 97.04

98 96.97 97 95.97

99 100.00 98 98.02

99 98.11 95 95.85

98 97.27 97 97.68

98 97.32 98 98.55

98 97.49 99 98.54

98 97.60 97 96.77

98 97.84 97 97.85

Table 3: Model Evaluation Metrics.

Metric Value

MSE 1.52

RMSE 1.23

RMLSE 0.005

MAE 1.13

0.68

The DNN model’s performance in predicting

SpO2 from respiratory signals shows highly promis-

ing results. An MSE of 1.52 indicates that, on av-

erage, the model’s predictions closely match the ac-

tual SpO2 values. The MAE, standing at 1.13, reveals

BIOSIGNALS 2024 - 17th International Conference on Bio-inspired Systems and Signal Processing

744

a low average absolute difference between predicted

and actual SpO2 values. Additionally, the RMSE

value, which is derived from the MSE and equals

1.23, provides a comprehensible metric in the orig-

inal SpO2 scale, signifying that the model’s predic-

tions align well with the true values. There was no

difference observed in the data between the three sub-

jects.

Moreover, the R

value of 0.68 demonstrates the

model’s ability to explain roughly 68% of the variance

in SpO2, indicating a good ﬁt to the underlying data

patterns.

4 CONCLUSION

This paper investigates the application of a wearable

laser-induced graphene respiration sensor for SpO2

estimation as a substitute for PPG-based oximeters,

foremost used as a tool in a triage process in mass

casualty events. The LIG sensor is placed in 7 differ-

ent positions on the individual’s chest to facilitate the

real-time monitoring of respiratory signals.

The obtained promising results for estimating

SpO2 with LIG signals processed by HeartPy have

shown another possible utilization of the wearable

mechanical deﬂection sensors as a part of integrated

patch-like sensors. The neural network’s performance

shows potential, as indicated by regression metrics,

including a mean squared error (MSE) of 0.152, a

mean absolute error (MAE) of 1.13, a root mean

square error (RMSE) of 1.23, and an R

score of 0.68.

By combining PPG and respiratory signals from

graphene, we offer an idea for developing 2D sensors

for emergency situations, leading to better monitor-

ing and management of various medical conditions.

However, further research is needed to explore the

potential correlations between these signals and their

clinical applications, as well as realistic performance

under application in the ﬁeld. For example, motion

artefacts may appear in signals, and additional ﬁlter-

ing may need to be applied to remedy it.

The study limitation includes the small number of

instances where SpO2 was measured below 90%, as

we faced challenges in obtaining access to individuals

with respiratory difﬁculties.

Ethical Considerations

The signals were recorded and conducted with ap-

proval from the Ethics Committee.

ACKNOWLEDGEMENTS

This work was supported in part by the NATO Sci-

ence for Peace and Security Program under project

SP4LIFE, number G5825. We also acknowledge sup-

port by the Serbian Ministry of Science, Technolog-

ical Development, and Innovations, contract num-

ber 451-03-47/2023–01/200026. This work was sup-

ported in part by the Faculty of Computer Science

and Engineering in Skopje, North Macedonia under

project BIOX.

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