2022.07.03.30
Files > Volume 7 > Vol 7 No 3 2022

Previous / Index / Next
Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results

Eduardo Cepeda1
, Diego H. Peluffo-Ordóñez2,3
,
Paúl Rosero-Montalvo4
, Miguel A Becerra5
, Ana C.
Umaquinga-Criollo6
, and Lenin Ramírez1






1 School of Biological Sciences and
Engineering. Yachay Tech University. Urcuquí-Ecuador; eduardo.cepeda@sdas-group.com lramirez@yachaytech.edu.ec
2 Modeling, Simulation and Data Analysis (MSDA)
Research Program, Mohammed VI Polytechnic University, Lot 660, Hay Moulay
Rachid Ben Guerir, 43150, Morocco. peluffo.diego@um6p.ma
3 Faculty of Engineering, Corporación
Universitaria Autónoma de Nariño, Carrera 28 No. 19-24, Pasto, 520001,
Colombia. diego.peluffo@aunar.edu.co
4 IT University of Copenhagen, Denmark; paul.rosero@sdas-group.com
5 Institución
Universitaria Pascual Bravo, Medellín-Colombia; miguel.becerra@sdas-group.com
6 Técnica del Norte
University, Ibarra-Ecuador; acumaquinga@utn.edu.ec
Available from: http://dx.doi.org/10.21931/RB/2022.07.03.30
ABSTRACT
Real-time vital signs monitoring,
particularly heart rate, is essential in today's medical practice and research.
Heart rate detection allows the doctor to monitor the patient's health status
to provide immediate action against possible cardiovascular diseases. We
present a possible alternative to traditional heart rate signal monitoring
systems, a cardiac pulse system using low-cost piezoelectric signal
identification. This system could benefit health care and develop continuous
pulse waveform monitoring systems. This paper introduces a heartbeat per minute
(BPM) cardiac pulse detection system based on a low-cost piezoelectric ceramic
sensor (PCS). The PCS is placed under the wrist and adjusted with a silicone
wristband to measure the pressure exerted by the radial artery on the sensor
and thus obtain the patient's BPM. We propose a signal conditioning stage to
reduce the sensor's noise when acquiring the data and make it suitable for
real-time BPM visualization. As a comparison, we performed a statistical test
to compare the low-cost PCS with types of traditional sensors, along with the
help of 21 volunteers. Experimental results show that the data collected by the
PCS, when used for heart rate detection, is highly accurate and close to
traditional sensor measurements. Therefore, we conclude that the system efficiently
monitors the cardiac pulse signal in BPM.
Keywords:
Heart
rate; Piezoelectric, BPM; Pulse Detection.
INTRODUCTION
Heart rate detection is critical for health care and
plays a crucial role in preventing cardiovascular disease. In recent years, the
number of people with cardiovascular problems has raised1. By studying these
signals, diseases and physiological changes in the human body can be detected
using sensors connected to a computer or heart rate system2. The radial pulse
contains rich information about the human cardiovascular system3. Heart rate monitoring
is of great importance in preventing disease. Increasing medical costs due to increased
patients with noncommunicable diseases (NCD) have become a critical problem
worldwide4. One of the main NCDs
is cardiovascular disease.
Portable devices are convenient for monitoring
biosignals and physical activities in daily life. Monitoring biosignals using
portable devices can contribute to early disease detection5. Electrocardiography
(ECG) is the established method to record the human heart rate6. Although several
biomedical devices detect heart rate, a portable heart rate system based on
piezoelectric identification could be a possible alternative to traditional
sensors. In this paper, we propose a cardiac pulse system using PCS; this
sensor is widely used in recent studies and fast processes. The PCS is used in studying
pulsed disturbances of condensed media7 due to its high
sensitivity, high robustness, and low cost8. In the last two
decades, interest in developing new technologies with the use of piezoelectric
sensors has expanded9. The need for a
low-cost sensor is due to the high demand and low supply of these devices in
cardiovascular disease prevention.
In the past, heart rate alteration was not considered
one of the risk factors for cardiovascular disease, but now every doctor must
have access to a heart rate measuring device to monitor the patient, as heart
rate is the main factor influencing the effectiveness of heart disease
treatment10. To ensure that the
proposed system provides reliable results, we compared it with an XD-58C pulse
sensor11 and an Innovo oximeter12, testing 21 volunteers
between 19 and 22 years of age.
MATERIALS
AND METHODS
The section details the materials used in developing
the cardiac pulse system. The device detects the signal by piezoelectric
identification, reduces signal noise, and amplifies the signal to observe the
individual's BPM on LCD screen. Our main contribution to this paper is using a
PCS for cardiac pulse measurement. The energy passing through the PCS is
generated from slight body movements13. Piezoelectric sensors
have already been used to measure a specific body part14. Figure 1 displays a
block diagram of the procedure.

Figure
1. Block
diagram representing the methodology. The pulse system design consists of four
main stages: (a) electronic device design, (b) signal conditioning, (c) BPM
evaluation, (d) results in visualization.
Subsection
Figure 2 displays the prototype design and its
components: PCS, Arduino one, LCD screen, pass-band filter, and signal
amplification. Figure 2 also shows the hardware connection: the incoming
communication between the PCS and the Arduino (red arrow), and the outgoing
communication between the Arduino and the LCD (yellow arrow)15. Table 1 describes the
electronic item used16. Finally, Figure 3
shows the system developed in Proteus17.

Figure
2. Pulse
system hardware using piezoelectric signal identification. The red arrow
reflects the incoming communication to the Arduino, and the yellow arrow
reflects the outgoing communication from the Arduino to the LCD.

Figure
3. The
pulse system's technical design, which includes the sensor signal input,
resistors, capacitors, logic gates, and LCD display, was created in proteus
software.

Table 1. Description of the electronic
components of the pulse system.
Signal conditioning
We designed a bandpass filter and a signal amplifier to
correct heart rate measurement and reduce the noise produced by the PCS. The
bandpass filter consists of a low and high pass filter. Filter pass-through
information limiters are used to limit the passage of specific frequencies
located within a given bandwidth and to attenuate those outside this width22,23. Thus, we get a signal
with less noise and an optimal appreciation for BPM calculation and
measurement.
BPM measurement
According to to8, when the PCS undergoes
slight deformation, electrical charges are generated in the sensor's flat area,
and in this way, pressure changes in the sensor can be captured. When a person
uses the PCS under the wrist, the sensor will suffer a slight deformation due
to the movements generated by blood circulation in the radial artery. Once we
obtain this pressure data through the PCS, this information has to be analyzed
to measure the BPM of the person24. The BPM is calculated
by obtaining the maximum points per minute of the filtered and amplified heart
pulse wave from the PCS reading [Figure 2]. As shown in Figure 4, the radial
artery originates along with the ulnar artery; this artery can be divided into
three segments: the proximal segment in the forearm, the middle segment, and
the distal segment that passes from the wrist to the dorsal side of the hand25. For the sensor to have
optimal functionality, it must be placed over the radial artery in the distal
part of the forearm, and the patient must be at rest so that sudden movements
do not affect the signal obtained by the PCS.
Results Display
The cardiac pulse
analysis is performed on an Arduino board, which allows us to add an LCD
screen, and, together with our programming, shows the patient's BPM on the
screen. Arduino Serial Plotter26, with the help of a
computer, shows the heart rate wave in real-time. As shown in Figure 5-a, the
heart rate wave without signal conditioning is difficult to analyze for
measuring the BPM. Figure 5-b shows the heart pulse wave with signal
conditioning that allows the BPM to be calculated. Figure 4 shows the correct
PCS position to obtain the necessary information to measure the heart pulse.

Figure
4. Proper
placement of the piezoelectric sensor.

Figure
5. This
figure shows the behavior of the signal, which changes due to the proper use of
the bandpass filter to reduce noise efficiently. (a) Heart pulse wave without signal conditioning and (b) Heart pulse wave with signal
conditioning plotted on Arduino serial plotter.
Experimental
Framework
The data collected from volunteers between 19 and 22
years of age. It was chosen to employ a sample of healthy persons, in a defined
age range, without preexisting diseases, decreasing the variability between age
groups to study the reaction of the sensor versus commercial analogs. The PCS
was placed on the radial artery with a flexible and comfortable silicone echo
bracelet. The patient was seated and stayed comfortable while the heart rate
measure was taken.
The patient does not move during the heart rate
measurement because of the important relationship between the movement or the
external manipulation of the sensor and the person under study. We compared
each patient's heart rate using a table with the results from the PCS, the
XD-58d heart pulse sensor, and the Innovo oximeter. We assess the quality of
the measure using the Repeated Measures Analysis of Variance (rANOVA) statistical
approach, which evaluates the differences between several variables' values27. It allows us to
analyze the data and shows whether the PCS is statistically acceptable compared
to traditional cardiac pulse measurement sensors.
RESULTS
AND DISCUSSION
Data collection, BPM visualization, and statistical visualization
We assessed the heart rate of 21 participants between
19 and 22 years of age, who exhibited a positive evaluation of the data. Figure
6 displays the suggested pulse system and demonstrates that it successfully
estimates the volunteer's BPM. The signal acquired by the piezoelectric
behavior sensor estimates the pressure exerted by the blood circulating through
the radial artery, so if this signal has a variation known as cardiac pulse,
this signal has noise due to what passes through the band pass filter and ends
up in the Arduino, which is in charge of displaying the individual's BPM on the
LCD display. Table 2 presents the data received from participants at BPM.

Figure
6. Location
of the piezoelectric ceramic sensor attached to the silicone bracelet on the
wrist. Signal conditioning stage, Arduino, and LCD screen displaying the BPM.

Table 2. BPM from patients
between 19 and 22 years of age, measured with piezoelectric analysis, XD-58C
pulse sensor, and Innovo oximeter.
Software is used to represent the results of sample
data more optimally. Figure 7 shows a histogram of the patient's BPM. We infer
that the data follow a normal distribution. Most of the recorded values are
within the 70 to 80 BPM range.

Figure
7. BPM
Histogram; a normal data distribution is observed; thus, the ANOVA statistical
method is carried out.
The measure of comparison and quality
We check the quality of the measurements using the
repeated measures ANOVA (rANOVA) statistical method that validates the quality
of the results. We compare the resulting F-value with the F-value from the
distribution table. This comparison provides us with rich information about whether
or not there is a good relationship between the measurements from the three
types of sensors. Our null hypothesis proposes a relationship between the data
from the three types of sensors, whereas our alternative hypothesis establishes
that at least one the three types of sensors is significantly different from
the rest.
By comparing the values from each sensor, we obtain an
F value of 0.07; then, our null hypothesis is not rejected. This F value, when
compared with the distribution table F value, with a statistical alpha
significance of α = 0.05, shows that 0.07 = F < Fα = 3.52. This indicates
that the test of the relationship between the three types of sensors that
measure the pulse rate is statistically acceptable. We calculate the p-value in
relation to the rANOVA statistical method using the R software environment,
resulting in a p-value of 0.9326. Table 3 shows the results of the rANOVA,
inferring that the PCS is optimal for cardiac pulse detection. Figure 8 offers
a clearer view of why the null hypothesis is not rejected; there is a high
probability that the data provided by the PCS are reliable.

Figure
8. F-value
is falling outside of the rejection region.

Table 3. Analysis of Variance
ANOVA results.
Table 3. The subject F value of 9.92E-04 shows that
the volunteers' data values are very consistent, probably, because of the age
range of 19 to 22 years. Table 4 shows that the effect of going from the
oximeter to the PCS is 1 BPM, which is considered statistically irrelevant;
likewise, the effect of going from the XD-58C sensor to the PCS is 0.42 BPM and
considered irrelevant.

Table 4. Statistical Analysis.
We measured an effect size of 0.0035, which allows us
to compare the sum of squares and relate the variance percentage affecting the
response when we change from one sensor to the other. Thus, we observe a
positive impact on the total variability that has changed from one sensor to
the other.
Furthermore, we measured an effect size of 0.005 for
the volunteers, indicating a relationship between the volunteers' variance with
a minimum variance percentage. Thus, we conclude that the three sensors produce
statistically equivalent results for every individual. So, there is no evidence
from the statistical analysis that allows us to reject the null hypothesis.
The means of the measures from the three types of
sensors are considered statistically similar, as the null hypothesis wasn't
rejected. Figure 9 shows each sensor's measures using a box plot.

Figure
9. Statistical
box plot. BOM was measured with the three types of sensors, two conventional
ones for comparison with the piezoelectric ceramic sensor.
Figure 9 shows the comparison between the data from
the three types of sensors; we estimate they are equivalent. Therefore, we
conclude that the values registered by the PCS are statistically acceptable. In
figure 9, all the collected data have been considered; there are no
statistically unusual or strange values (outliers) that lead to readjustments. However,
the PCS shows a slight increase in its BPM measures, which means that although
the sensor is optimal for everyday use, it is still possible to improve it. The
sensor's operation can be retouched and optimized for future work, either by
hardware calibration or a software improvement. The slight increase is not
significant for the analysis presented in this paper.
DISCUSSION
The proposed system was
tested with statistical quality measurement methods and gave positive results
when calculating the heart pulse beats per minute in BPM. Compared with
traditional sensors that exist today, the PCS achieves reliable results in detecting
and observing the patient's BPM. The main objective of this paper is to introduce
a heart rate system that employs piezoelectric signal identification as an
alternative to traditional sensors; according to the GUM guide28, based on the usual
uncertainty error propagation rule, it offers us an uncertainty of 1.44 with 10
degrees of freedom, which suggests that the sensor may achieve the popular
market level, given that the oximeter is the most marketed globally. However,
due to the electromechanical properties of the PCS, the system works correctly
under the following condition: the patient has to stay and maintain the sensor
in its original position. The PCS has to remain unmoved during the heart rate
detection because there is a strong relationship between the patient's movement
and the heart rate detection. PCS indicates a little rise in its BPM readings,
which suggests that while the sensor is appropriate for everyday usage, there
is still potential for development, so for future work, the sensor performance
may be tuned and enhanced, either by hardware validation or by software
updating. As for the hardware, it is advised to use a Bakelite and add more
elaborated stabilizers and filters for this purpose; on the other hand, a
different software must be highly beneficial to make the data acquired more
trustworthy despite the rapid movement that the human may make.
CONCLUSION
In this paper, we
implement a heart rate system employing piezoelectric signal identification to
detect a patient's heart rate. The primary motivation for developing the system
was to propose a low-cost, portable and immediate solution to detect possible
cardiovascular diseases; the innovator oximeter costs around $20 on the market;
the suggested system without the Arduino costs less than or equivalent to $5
per unit since the electrical components used are meager cost as does its
sensor, which costs less than 50 cents. It is a possible alternative to the
current traditional sensor that detects the pulse signal. The proposed pulse
system can monitor the heart rate in real-time and thus prevent cardiovascular
diseases. We carried out an experiment with 21 patients to analyze the
functionality and performance of the proposed pulse system, measures of comparison
and quality were implemented, and these gave positive results; We propose a
comparison between the PCS, the XD-58C pulse sensor, and the Innovo oximeter,
demonstrating that the PCS is an exact and reliable heart rate sensor for
continuous pulse monitoring.
We conclude that the
system produces positive results for pulse rate monitoring as long as the
patient does not manipulate the sensor while it is monitoring the data; the
patient must not make any sudden movement not correct the device location. This
heart rate system is essential for people who want to know their heart rate's
current status and prevent cardiovascular diseases. An experienced doctor can
read this data and reach meaningful conclusions. However, not everyone can
detect abnormal values within the data; therefore, for future research, we
propose improving the system so that anyone can recognize the signs of abnormal
heart rates. We also propose adjusting the software or the hardware to correct
the non-significant increase that the PCS of the heart pule shows at the time
of measuring the BPM. Finally, we suggest fixing the wristband so that the
sensor remains stable and measures the BPM despite sudden arm, hand or wrist
movements.
Author Contributions: EC: conceptualization, methodology,
software, validation, formal analysis, investigation, resources, data curation,
writing-original draft preparation, DHPO, PRM, MAB, AUC, and LR: writing-review
and editing, visualization, supervision, project administration. All authors
have read and agreed to the published version of the manuscript.
Acknowledgments: This work is supported
by SDAS Research Group (www.sdas-group.com).
Conflicts
of Interest:
The authors declare no conflict of interest.
REFERENCES
1.
Callaway CW, Carson AP,
Chamberlain AM, Chang AR, Knutson KL, Lewis TT, et al. Heart Disease and Stroke
Statistics — 2020 Update A Report From the American Heart Association. 2020.
139–596 p.
2.
Keat LC, Jambek AB, Hashim U. A
Study on Real-Time Pulse Sensor Interface with System-on-Chip Architecture.
2016;281–6.
3.
M SR, Rao R. Experimental
investigation on the suitability of flexible pressure sensor for wrist pulse
measurement. 2018;
4.
Okano T. Multimodal
Cardiovascular Information Monitor Using Piezoelectric Transducers for Wearable
Healthcare. 2018;
5.
Bennett JE, Stevens GA, Mathers
CD, Bonita R, Rehm J, Kruk ME, et al. NCD Countdown 2030: worldwide trends in
noncommunicable disease mortality and progress towards Sustainable Development
Goal target 3.4. Vol. 392, The Lancet. 2018. p. 1072–88.
6.
Lohani M, Payne BR, Strayer DL.
A Review of Psychophysiological Measures to Assess Cognitive States in
Real-World Driving. 2019;13:1–27.
7.
Surkaev AL, Kul VG.
Investigation of a Pulsed Waveguide Piezoelectric Pressure Sensor.
2006;52:218–21.
8.
Peng M. Detection of Sleep
Biosignals Using an Intelligent Mattress Based on Piezoelectric Ceramic Sensors
†. 2019;1–17.
9.
Jiao P, Egbe KI, Xie Y, Nazar
AM, Alavi AH. Piezoelectric Sensing Techniques in Structural Health
Monitoring : A State-of-the-Art Review.
10.
Davidovic G, Iric-cupic V, Milanov
S. Associated influence of hypertension and heart rate greater than 80 beats
per minute on mortality rate in patients with anterior wall STEMI.
2013;6:358–66.
11.
XD-58C Pulse Sensor | Open
ImpulseOpen Impulse.
12.
INNOVO Fingertip Pulse Oximeter
User Manual - Manuals+.
13.
Dagdeviren C, Joe P, Tuzman OL,
Park K Il, Lee KJ, Shi Y, et al. Recent progress in flexible and stretchable
piezoelectric devices for mechanical energy harvesting, sensing and actuation.
Extrem Mech Lett. 2016;9:269–81.
14.
Curry EJ, Ke K, Chorsi MT, Wrobel
KS, Miller AN, Patel A, et al. Biodegradable piezoelectric force sensor. Proc
Natl Acad Sci U S A. 2018;115:909–14.
15.
Rosero-Montalvo PD,
Peluffo-Ordonez DH, Lopez Batista VF, Serrano J, Rosero EA. Intelligent system
for identification of wheelchair user's posture using machine learning
techniques. IEEE Sens
J. 2019;19:1936–42.
16. Vargas-muñoz
AM, Chamorro-sangoquiza DC, Umaquinga-criollo AC. Diseño de un prototipo de
bajo coste computacional para detección de arritmias cardiacas. 2020;470–80.
17.
PCB Design and Circuit Simulator
Software - Proteus [Internet]. [cited 2022 Apr 22]. Available from:
https://www.labcenter.com/
18.
Arduino Uno Rev3 — Arduino Online
Shop [Internet]. [cited 2022 Apr 22]. Available from:
https://store-usa.arduino.cc/products/arduino-uno-rev3/
19.
Piezo Element - SEN-10293 -
SparkFun Electronics [Internet]. [cited 2022 Apr 22]. Available from:
https://www.sparkfun.com/products/10293
20.
Basic 16x2 Character LCD - Black
on Green 5V - LCD-00255 - SparkFun Electronics [Internet]. [cited 2022 Apr 22].
Available from: https://www.sparkfun.com/products/255
21.
Differential Comparator - LM311 -
COM-13950 - SparkFun Electronics [Internet]. [cited 2022 Apr 22]. Available
from: https://www.sparkfun.com/products/retired/13950
22. Parente
FR, Santonico M, Zompanti A, Benassai M, Ferri G, D'Amico A, et al. An
electronic system for the contactless reading of ECG signals. Sensors
(Switzerland). 2017;17:1–10.
23.
Prutchi D, Norris M. Design of
Safe Medical Device Prototypes. Design and Development of Medical Electronic
Instrumentation. 2005. 97–146 p.
24.
Cevallos A, Solórzano A. Stem cell
activity in the repair of cardiovascular tissues. Rev Bionatura. 2019;4.
25.
Scalise RFM, Salito AM, Polimeni
A, Garcia-Ruiz V, Virga V, Frigione P, et al. Radial artery access for
percutaneous cardiovascular interventions: Contemporary insights and novel
approaches. J Clin Med. 2019;8.
26.
Arduino - Home [Internet]. [cited
2022 Apr 22]. Available from: https://www.arduino.cc/
27.
Schober P, Vetter TR. Repeated
measures designs and analysis of longitudinal data: If at first you do not
succeed-try, try again. Anesth Analg. 2018;127:569–75.
28. Pérez
M del M. Estimación de incertidumbres. Guía GUM. Rev
Española Metrol [Internet]. 2012;114,130. Available from: http://www.uv.es/~meliajl/Docencia/WebComplementarios/GuiaGUM_e_medida.pdf
Received: 30 April 2022 / Accepted:20 June 2022 / Published:15
Agoust 2022
Citation: E Cepeda, D H. Peluffo-Ordóñez,
P Rosero-Montalvo, M A Becerra, A C. Umaquinga-Criollo, L Ramírez. Heart Rate Detection using a
Piezoelectric Ceramic Sensor: Preliminary results. Revis Bionatura 2022;7(30) 2.
http://dx.doi.org/10.21931/RB/2022.07.03.30