12 (2022) 100223
Contents lists available at ScienceDirect
Biosensors and Bioelectronics: X
journal homepage: www.journals.elsevier.com/biosensors-and-bioelectronics-x
A dynamic reconfigurable wearable device to acquire high quality PPG
signal and robust heart rate estimate based on deep learning algorithm for
smart healthcare system
Bui Ngoc-Thang a, *, Thi My Tien Nguyen b, Trong Toai Truong c, Bang Le-Huy Nguyen d, Tuy
Tan Nguyen e
a
Institute of Engineering, HUTECH University, 475A Dien Bien Phu Street, Ward 25, Binh Thanh District, Ho Chi Minh City, Viet Nam
Department D of Pediatrics at the Hospital for Tropical Diseases, Ho Chi Minh City, Viet Nam
c
3C Manufacturing Machinery Ltd., Ho Chi Minh, Viet Nam
d
National Renewable Energy Laboratory, Golden, CO, USA
e
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
PPG signal
Motion artifact
Wearable healthcare
Deep learning
HR estimation
Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR),
saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are
increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of
wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper pro­
poses a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is
significantly improved and power consumption on wireless module is minimized. In the proposed architecture,
parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. More­
over, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove
various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation
framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term
memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal
correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For vali­
dation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals
with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and
muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA
and minimize the power consumption on wireless transfer module of wearable PPG devices.
1. Introduction
The global internet of medical things (IoMT) market is projected to
grow up dramatically from $30.79 billion in 2021 to $187.60 billion in
2028(Business, 2022). In recent, IoMT has become more important and
provide new services to patients with reduced medical costs(Federico
Guede-Fernández et al., 2020, Qingguo Chen et al., 2020, Xiqiu Hu et al.,
2020). Moreover, IoT in medical is considered as an effective way to
provide medical aid for monitoring, analysing, detecting of health pa­
rameters, and predicting diseases as a precaution (Deboleena Sadhu­
khan et al., 2019; Vishal Chaudhary et al., 2022). Wearable devices for
health monitoring have had outstanding development and directly
support clinical diagnostics by providing high accuracy parameters of
human vitals, especially PPG devices(Ahmadreza Attarpour et al., 2019,
CHON, 2019, Xiqiu Hu et al., 2020). The PPG measures noninvasively
the number of reflection or pass-through photon based on changes of
blood volume under tissue layers at low-cost(Bhirawich Pholpoke et al.,
2019, CHON, 2019, Ngoc Thang Bui et al., 2019, Simhadri Vadrevu
et al., 2019). Typical, PPG sensor collects signals by using a pair of LED
transceivers (e.g., infrared, red, and green) attached onto a skin’s sur­
face(Elsamnah F et al., 2019, Vishal Chaudhary et al., 2021; Subbiah
Alwarappan et al., 2022). The amplitude of PPG signals depends on the
* Corresponding author.
E-mail addresses: bn.thang@hutech.edu.vn (B. Ngoc-Thang), bsmytien88@gmail.com (T.M. Tien Nguyen), truongtrongtoai@robot3t.com (T.T. Truong),
bangnguyen@ieee.org (B.L.-H. Nguyen), tuy.nguyen@nau.edu (T.T. Nguyen).
https://doi.org/10.1016/j.biosx.2022.100223
Received 13 June 2022; Received in revised form 4 August 2022; Accepted 12 August 2022
Available online 23 August 2022
2590-1370/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
B. Ngoc-Thang et al.
Biosensors and Bioelectronics: X 12 (2022) 100223
strength of signal from the transmitted LED The modern PPG sensors are
usually small size, compact, and easy to use, so PPG sensor is increas­
ingly widely used in various mobile and wearable applications (Iakovlev
D et al., 2018, May JM et al., 2019). There are a lot of wearable devices
applications such as SpO2 meter, smart rings, smart watch, etc. which
are dependent on analysis of PPG signal characteristics(ReşitKavsaoğlu,
2015, Fujita et al., 2019).
However, quality of PPG signal is susceptible to human activities and
surrounding environmental factors, especially MA noises (Choon-Hian
Goh et al., 2020). Removing these noises from PPG signal is the biggest
challenge in wearable devices since they are main causes which have
highest effects to quality of PPG signal (Fine et al., 2021; Meng Rong
et al., 2021). MA usually exists in PPG signals because of the
motion-induced moving between wearable sensors and human skin
(Joseph Azar et al., 2021). A lot of research is focusing on noise
removing for PPG signal (Yongbo Liang et al., 2018; Joseph Azar et al.,
2021). These techniques can be classified into two approaches: (1)
applying filters and improving signal collecting performance using
adaptive filter, deep learning algorithms, etc.; or (2) using more gyro
sensor to calibrate PPG signals (CHANKI PARK et al., 2019). Their key
disadvantage is the high computing burden that increases the cost and
power consumption of PPG device(Ngoc Thang Bui et al., 2020; Vishal
Chaudhary et al., 2022). The outbreak of the covid-19 pandemic has
required many high-quality PPG devices for monitoring and diagnosing
vital signs(Munjral S et al., 2022). Most PPG devices use cheap
non-rechargeable batteries and don’t have the ability to connect to other
peripherals(Qingguo Chen et al., 2020; Ngoc Thang Bui et al., 2021].
Besides, smart devices can collect PPG signals but causing many in­
conveniences to users being monitored for a long time. In addition, the
power consumption on WiFi/Bluetooth Low Energy (BLE) modules of
wearable devices is relatively large(Simhadri Vadrevu et al., 2019;
Qingguo Chen et al., 2020). Our proposed PPG architecture with dy­
namic signal processing algorithm can increase accuracy and achieve
low-power consumption concurrently. Therefore, it overcomes the
above disadvantages to improve the quality of healthcare services.
There has been a breakthrough development of DL algorithms and
applications in recent years(Liu et al., 2020; Shantanu Sen Gupta et al.,
2021; Viet Nguyen-Le et al., 2022). The DL algorithms combined with
PPG signal measurement have also been widely applied in the health­
care field(ReşitKavsaoğlu, 2015, Brosnan Yuen et al., 2019; Choon-Hian
Goh et al., 2020; Sun et al., 2021) However, DL algorithms have dis­
advantages, such as the need for the big datasets and high computation
cost on model training (Ye Qiu et al., 2021). The dataset preparation is
often time-consuming and requires careful data selection to ensure
quality (Elyas Sabeti et al., 2019; Joseph Azar et al., 2021). To solve this
problem, the proposed method evaluates and selects PPG signals during
real-time measurement. HR is also one of the most important vital signs
because it indicates the health status of the body (CHANKI PARK et al.,
2019). Many algorithms have been applied to calculate HR with
high-noise PPG signa l(Brosnan Yuen et al., 2019, Arunkumar et al.,
2020). However, these algorithms also require about 10s to calculate the
HR in 1 min with a relatively clean PPG signal. The proposed method
can calculate HR within a short time by the acquisition of PPG data that
users are more comfortable.
Overall, our research has the following contributions:
3) We also propose a deep learning algorithm based on CNN-LSTM to
calculate HR based on high quality PPG signal with signal acquisition
time of only 5s.
The rest of the paper is organized as follows. Section 2 briefly out­
lines the background of this work. section 3 presents the experiment
setup and results of the whole PPG quality assessment system. The dis­
cussion is presented in Section 4. Finally, Section 5 concludes the paper.
2. Materials and methods
In this section, we describe the proposed architecture for collecting
PPG signals to estimate vital signs (i.e. Supplementary Information
Fig. S1), the standards to evaluate PPG signal quality, the applicability to
select high quality PPG signal, the main purpose of obtaining high
quality PPG data, and the solution to achieve low power-consumption of
a wearable PPG device. We also proposed a DL model to estimate HR
with the high accuracy.
2.1. High quality PPG signal selection method
A typical PPG signal containing two main components: alternating
current (AC) and direct current (DC). The AC component is created by
the synchronized variation of the blood fluctuating with HR (Yan et al.,
2017, Elsamnah F et al., 2019) Many standards can be used to evaluate
signal quality, but the signal-to-noise ratio (SNR) is the simplest and
most prevalent. Yongbo Liang et al. have tried to optimize parameters
and selected best filter for the PPG signal (Wiseman et al., 2015). (S K
MukhopadhyayMukhopadhyay et al., 2015) has shown that the Che­
byshev II filter with the order 4th is the most suitable for noise filtering
for PPG signals (M. et al., 2016, Yongbo Liang et al., 2018). Mohamed
Elgendi presented the quality assessment standards with eight criteria
for PPG signal (Ye Qiu et). Applications of PPG signal are mainly focused
on HR calculation, BP estimation, blood glucose estimation, stroke
warning, etc.(Pimentel et al., 2016, Liang Y et al., 2018, Fujita et al.,
2019; Fine et al., 2021; Sun et al., 2021)
To evaluate PPG signal quality, we divide PPG signals into 2 groups
as follows:
1. Group 1 (G1) | Accepted for diagnosis: PPG signals with the systolic
and diastolic waves can be displayed clearly.
2. Group 2 (G2) | Unacceptable for diagnosis: PPG signals with the
systolic and diastolic waves are not displayed, and they are not
suitable for feature extraction.
The PPG signals in group G1 are usually meaningful and suitable for
clinical diagnostic applications. Therefore, the proposed algorithm only
focus on collecting and analysing signals belonging to the G1 group. We
systematically analyse the shape standards with the goal of quality
improvement of the PPG waveforms (Tania Pereira et al., 2020; Oliver
Zhang et al., 2021). These information need to help in the selection of
the high quality PPG data. Four standards were selected to analyse
supplementary information (Note S1).
2.2. Real-time reconfiguration of PPG sensor parameters
1) We design and demonstrate a complete wearable devic)e that col­
lects PPG signals in real time over WiFi/BLE. The device can
configure the PPG sensor parameters to suit the skin characteristics
of each user in real-time.
2) We propose a signal quality evaluation method combining the first
and second derivatives of PPG data to select high-quality PPG sig­
nals. Only good quality PPG signals are transmitted to a receiving
device thereby optimizing power consumption on the device and
prolonging the battery life.
The main component of PPG device is the PPG sensor (MAX30102
(Maxim, 2014) which includes four special functions: 1) HR and SpO2
measurement applications (by using Red LED (λ = 660 nm) and IR (λ =
880 nm)); 2) Change the amplitude of PPG signal (by changing current
through, Red and IR LED); 3) Increase accuracy of PPG signal (the
sampling frequency can be changed from 50 Hz to 3200 Hz); 4); Smooth
PPG signal and reducing the sampling frequency (applied the ring buffer
up to 32 samples, and the sampling frequency up to 32 samples).
The AC amplitude of PPG signal is directly dependent on the current
through the LED. In addition, the sampling frequency and the average
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Fig. 1. (a) The architecture of HR monitoring system includes adaptive wearable device and DL algorithm, (b) diagram of proposed method for MCU firmware with
dynamic control LED’s current, (c) The detail explanation of proposed algorithm for selecting blocks high-quality PPG signal (i.e. Supplementary Information
Fig. S2, Table S1).
number of samples per sample are also affect the smooth of the PPG
shape(Elsamnah F et al., 2019]. To overcome these limitations, the PPG
device focus on controlling the current through the LED, selecting the
best value of samples’ sampling frequency, and sample averaging for
PPG data. Fig. 1(b) shows the proposed algorithm for controlling LED
current and reconfiguration the parameter of PPG sensor in real-time.
Fig. 1(a) shows the proposed architecture which contains PPG device
and software for processing PPG data. The PPG device contain a smart
control which can change device parameters in real-time toward col­
lecting high quality PPG data. Fig. 1(b) shows the proposed method for
changing the parameters of PPG sensor as two following steps: step 1)
changing the LED current to increase the amplitude of PPG signals (the
value written to register 0 × 0C, 0x0D of MAX30102); step 2) changing
the average sample number to smooth PPG signals. The key point of this
algorithm is to calibrate the sample number to calculate the mean based
on the first derivative. To verify these parameters, the software evalu­
ates the PPG signal by using the SSQI standard and user checking. The
average value of the sample number is given based on actual tests on the
MAX30102 sensor. However, due to the limited sampling frequency and
energy-saving of healthcare wearable device, the PPG device usually
chooses value in the range 1–8. These values are then passed through the
BLE module and added to the FIFO configuration register (address 0x08)
in sensor MAX30102. The process is done after the user confirm to
accept the PPG signal as G1 and transfer the configuration parameters to
MAX30102 through the BLE module and MCU. Fig. 1(c) shows an
example of proposed algorithm. The high-quality PPG signals is selected
by utilizing the evaluation standard for original, first and second
derivative of PPG data. For example, the signals evaluated in step 1 are
moved to step 2 to determine the first derivative and second derivative
values (i.e., coefficient SSQI ) which can provide key criteria of good
signals selection (i.e. Supplementary Information Fig. S3).
2.3. Proposed deep learning algorithm for estimation HR with high quality
PPG data
In this paper, we propose the process architecture for reliable HR
monitoring based on deep learning algorithm, using only 5-s singlechannel PPG signals (i.e. Supplementary Information Fig. S4, Note S2,
Table S4). Fig. 2(b) shows the proposed structure model based on CNNLSTM which has been successfully applied to single-channel PPG signal
suitable with one dimensional (1D) PPG data input for HR monitoring
(HR–CNN–LSTM)(Choon-Hian Goh et al., 2020).
3. Results and discussion
In this section, we re-evaluate the proposed PPG system which are
contain PPG device and DL algorithm. The test has two steps: (1) we
evaluate some PPG signals in real-time and the threshold of SSQI , KSQI ,
basSQI, KSQI SD by calculating with PhysioNet database(Goldberger
et al.,); (2) the software estimate HR with 5s PPG signal from device by
using DL algorithm. The software was written by using Python v3.9 and
Pycharm IDE v.2021.
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Fig. 2. (a) The process of DL algorithm for HR estimation, (b) the architecture of DL algorithm, (c) the Bland–Altman plot between the true HRtrue , HRest as (− 1.8,
1.8)bpm, and the correlation coefficient for HRtrue and HRest as the linear expression Y = 0.99x+0,91.
Table 1
The comparison of accuracy, algorithm, and standard with previous studies.
Items
This
work
Machine learning algorithm
Ref(Joseph Azar et al., 2021]
Ref(M. et al.,
2016]
Ref(Yongbo Liang
et al., 2018]
Ref(May JM et al.,
2019]
RNN & Autoencoder
Signal quality standard
Dataset for training
Yes
53
Accuracy
Real-time measurement and
amplitude control
Signal quality result
95%
Both
High
10,212 batches clean and 3149
batches noise
90%
Medium
Yes
106
Yes
219
Yes
10
High + Medium
Only Real-time
measurement
High + Medium
86%
High +
Medium
3.1. PPG signal data evaluation and power consumption of device
Ref(Elyas Sabeti
et al., 2019,]
Support Vector
Machine
234,739
83%
High + Medium
of BLE module as reducing the working time of BLE module.
Unlike other studies that focus on analysing only 1 cycle of PPG
signal, our study analyses the signal in 5s period. The selected PPG signal
can use for medical applications. Besides, the operating parameters of
the MAX30102 sensor are also flexibly adjusted to suit different human
bodies and reduce the current consumption of PPG device during
operation. Table 1 compared this work with related works.
Our proposed has the highest accuracy for selecting very highquality signals for analysis. However, the proposed only uses the small
dataset to find the threshold to select the accepted signal compared with
the machine learning method. The adjustment parameters of the
MAX30102 sensor also improve the quality of the PPG signal.
Fig. 2(a) shows detail the hardware and software to collect and
analyse of PPG signals in real-time. The software collects and displays
about 30 s with a sampling frequency of 125 Hz (i.e. total of 60000
samples divided into 96 blocks). The individual data blocks consisting of
625 samples (5 s) will be analysed, evaluated, and displayed accepted
blocks. As Fig. S3(a) shows, the HR system acquire 21s of original PPG
signal which include high and low quality PPG data. To divide the PPG
data become the blocks data which has 625 samples (i.e. 5 s) before
calculate SSQI and KSQI . In Fig. S3(b), the high quality PPG was selected
with those two rules as follow SSQI and KSQI (i.e. SSQI = 0.8 > 0.7 and
KSQI = 2.2 > 2.0) and automatically remove the PPG blocks with low
SSQI and KSQI . Fig. S3(c) shows that calculate KSQI the first derivative of
the selected PPG blocks for confirmation high-quality PPG block as
follow rule (i.e. KSQI SD = 3.6 > 2.0). Furthermore, after applying 4
rules for selecting PPG signal that the accepted PPG data block is always
shorter than the original PPG data for reducing the power consumption
3.2. Real-time high-quality PPG signal evaluation and selection
Fig. 3 depicts a complete model of the high-quality PPG signal
acquisition and selection system. The hardware consists of two modules:
The first module collects the PPG signal and transmits to the receiving
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Fig. 3. The details of experiments of smart PPG device with volunteers: (a) The proposed PPG device and reference Pulse Oximeter attach on left hand of volunteer;
(b) detail hardware module of PPG device (i.e. wireless charger module, ESP32 main processing module, MAX30102 with finger clip), (c) the hardware blocks
diagram of PPG device, (d) real-test to processing automatic change LED’s currents of PPG sensor during operation in real-time for increasing LED’s current to
achieve the high amplitude of PPG signal as follow 4 steps, (e) detail explanation of each step during calibrated PPG signal, (f) the comparison of 3 PPG signals
different LED’s current from 1.2 mA to 4.6 mA. (g) the comparison of 4 PPG signals with the same LED’s current (4.6 mA) and different sample averaging from 1
to 16.
device. The main components of hardware are the MCU device (ESP32)
and the PPG sensor (MAX30102). Table S2 describes the connection and
interface module of this design. The second device that receives data
from the wearable device and transfers to the PC includes the ESP32
module and the USB2COM.
Fig. 3(e, d) show the detailed operation process of automaticallycalibrated PPG signals by changing the LED currents to get the best
PPG value in real-time (i.e., compared with raw signal (ex. step 1),
selected signal with step 4 could provide enhanced PPG signal with
increased amplitude and less noise influence. The results show that the
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AC amplitude from PPG signals increases dramatically (around 5 times),
the SNR increases over 9 times while the LED current increases 2.7
times. The PPG signal at steps 1 and 2 are rejected signals that become
accepted at steps 3 and 4. The PPG signal at step 4 is a high-quality signal
for clinical diagnostic. The advantage of this process is to obtain the
most suitable current consumption value for the sensor and the best PPG
signal quality.
Fig. 3(f) shows the best PPG signal with different current though LED
(i.e. the blue signal at LED current as 4.6 mA to achieve highest
amplitude and smooth shape). Fig. 3(g) shows the comparison among
four PPG signals with different average sampling. These signals show the
different shapes of the systolic and diastolic parts which are the key
feature of PPG signal. The key advantage of the PPG sensor is that it can
be changed by using the I2C interface. The test results show that the
suitable value of average sampling is from 1 to 16.
We also added more measured data of key performances such as
power consumptions and key operation modes. In Table S3, we compare
the current consumption of PPG devices in 3 different operating modes
(i.e. scanning, connected, transfer data). The current consumption of
PPG device in connected mode is 16% less than data transfer mode, that
reducing data transmission time is also an effective solution to reduce
power consumption of PPG device.
The next generation of sensor-systems integrated state-of-the-art
technique such as internet of things (IoTs), fifth generation (5G)
communication and artificial intelligence (AI) strategies. These tech­
nique is not only increased the computational ability but also increased
the power consumption of device. Reducing number of processing data
to achieve higher performance and saving power have revolutionized
applications of sensors in healthcare, wearable electronics, safety,
environment, defences, and agriculture which can drastically transform
the conventional sensing strategies. Furthermore, the new types of
chemical sensor (i.e. temperature sensor, glucose sensor, etc.) have
higher selectivity and stability which reduce the size of device and save
time-to-market for designing cost effective commercial devices(Xiupeng
Gao et al., 2020; Vishal Chaudhary et al., 2022).
4. Conclusions
In this work, we demonstrate a smart health care system that can be
employed as a HR estimation with high-quality PPG signal. Therefore,
the proposed PPG device can optimize the power consumption during
operation to increase the battery life and remove MA monitoring
physiological parameters. We develop a HR monitor system integrating
a PPG device to remove MA and select the high-quality PPG signal using
a DL algorithm in HR estimations. The accuracies of HR estimations (i.e.
error of HR from (− 1.8 to 1.8) bpm and the R2 value as 0.99) and
operation time of device are greatly improved by using adaptive selec­
tion method to reduce current consumption of sensor (i.e. 1.2 mA–4.8
mA) and proposed DL model. As a further extension, the DL algorithm
can be also integrated to wearable monitoring devices to remove MA
and estimate HR variation in wearing wristbands or finger-cuffs. Taken
together, integration of proximity/pressure/temperature sensing, etc.
with small size/light-weight wearable device promising and attractive
for various human body monitoring applications.
Funding
This work was funded by Vingroup Joint Stock Company and sup­
ported by Vingroup Innovation Foundation (VINIF) under project code
VINIF. 2020. NCUD. DA059. Vietnam.
CRediT authorship contribution statement
Bui Ngoc-Thang: Conceptualization, Methodology, Software,
Formal analysis, Resources, Data curation, Writing – original draft,
Visualization. Thi My Tien Nguyen: Conceptualization, Validation,
Data curation, Visualization. Trong Toai Truong: Funding acquisition,
Project administration, Supervision, Conceptualization, Investigation,
Writing – review & editing. Bang Le-Huy Nguyen: Writing – original
draft, Writing – review & editing, Visualization. Tuy Tan Nguyen:
Writing – original draft, Writing – review & editing, Visualization, All
authors have read and agreed to the published version of the
manuscript.”.
3.3. Evaluation deep learning algorithm for HR estimation of PPG signal
The proposed HR-CNN-LSTM model is programmed in Keras 2.8.0
framework and IDE Pycharm v.2021 where execution of training and
testing are performed utilizing Nvidia Quadro M1000 GPU with 2 GB
dedicated memory, deployed in a workstation with a 64-bit Ubuntu
operating system (18.04), an Intel Core i7 @2.7 GHz × 8 and 32 GB of
RAM.
The proposed software processing data show Fig. 2(a) which includes
three main parts (i.e. pre-processing data, preparing dataset and
training, testing, deploying model). Fig. 3(b) shows the DL model with
the backbone as CNN-LSTM networks. Detail information can be also
found in the supplementary information (Fig. S2).
To evaluate the performance of the proposed DL model, we used the
Bland–Altman plot and the scatter plot between to descript the rela­
tionship between HRtrue and HRest for indicating the errors between
HRtrue and HRest . Fig. 2(c) shows the Bland–Altman plot between
(HRtrue + HRest )/2, and (HRtrue − HRest )/2 to descript the efficiency of the
proposed HR–CNN–LSTM model. Bland–Altman plot shows over 95%
limit of an agreement from (− 1.8, 1.8) bpm. Fig. 2(d) shows the scatter
plot between HRtrue and HRest with a fitted line Y = 0.99X + 0.91, where
X is HRtrue and Y is HRest . The correlation coefficient is 0.9969, and R2 is
0.9950, indicating that the proposed DL model provides a high accuracy
method for estimating the HR with only 5 s PPG signal at sampling
frequency 125 Hz. This is especially meaningful for real-time health
monitoring applications that need to monitoring body parameters
continuously.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.biosx.2022.100223.
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