My title - Sociedad Mexicana de Ingeniería Biomédica

ARTÍCULO DE INVESTIGACIÓN
Vol. 37, No. 2, May-Ago 2016, pp. 91-99
ib INGENIERÍA BIOMÉDICA
REVISTA MEXICANA DE
dx.doi.org/10.17488/RMIB.37.2.3
Respiratory Rate Detection by a Time-Based
Measurement System
E. Sifuentes, J. Cota-Ruiz, R. González-Landaeta
Departamento de Ingeniería Eléctrica y Computación, Universidad Autónoma de Ciudad Juárez, ChihuahuaMéxico.
ABSTRACT
This paper proposes a system that converts a time-modulated signal from a resistive sensor into a digital signal
with the goal to estimate the respiratory rate of a subject. To detect breathing, a known method based on a nasal
thermistor, which detects temperature changes near the nostrils, is used. In this work, the thermistor mounted
in a Wheatstone bridge, forms a RC circuit which is connected directly to a microcontroller, without using any
analog circuit or analog-digital converter. Thus, whenever the subject breathes, it causes a fractional change in
resistance x (∆R/R0 ) on the thermistor, and this produces a time-modulated signal that is directly digitized with
the microcontroller. Measurements were made on 23 volunteers, obtaining changes of x > 0.01. The temperature
resolution was 0.2 ◦ C, and the time response was 0.8 s, mainly limited by the thermistor properties; these features
were enough to obtain a well-defined waveform of the breathing, from which was easy to estimate the respiratory
rate by a compact, low cost and low power consumption system. Unlike interface circuits based on voltage or current
amplitude, with this kind of interface, the self-heating of the sensor is avoided since the thermistor does not require
any voltage or bias current.
Keywords: time-based measurements, Wheatstone bridge sensors, respiratory rate, temperature
measurement, nasal thermistor.
Correspondencia:
Rafael González Landaeta
Instituto de Ingeniería y Tecnología, Departamento de Ingeniería
Fecha de recepción:
16 de diciembre de 2015
Eléctrica y Computación, Universidad Autónoma de Ciudad Juárez,
Av. del Charro 450 nte., C.P.32310, Ciudad Juárez, Chihuahua,
México.
Correo electrónico: [email protected]
Fecha de aceptación:
7 de marzo de 2016
92
Revista Mexicana de Ingeniería Biomédica · volumen 37 · número 2 · May-Ago, 2016
RESUMEN
Este trabajo propone un sistema que convierte una señal modulada en tiempo, proveniente de un sensor resistivo,
en una señal digital con el fin de estimar la frecuencia respiratoria de un sujeto. Para detectar la respiración se utilizó
el método basado en un termistor nasal, el cual detecta los cambios de temperatura cerca de las fosas nasales. En este
trabajo, el termistor, montado en un puente de Wheatstone, forma un circuito RC que se conecta directamente a un
microcontrolador, sin necesidad de usar ningún circuito analógico, ni conversor analógico-digital. Así, cada vez que
el sujeto respire, provoca un cambio fraccional de resistencia x (∆R/R0 ) en el termistor, y esto produce una señal
modulada en tiempo que se digitaliza directamente con el microcontrolador. Se hicieron medidas en 23 voluntarios,
obteniendo cambios de x > 0.01. Se obtuvo una resolución en temperatura de 0.2 ◦ C y un tiempo de respuesta
de 0.8 s, limitado principalmente por las propiedades del termistor utilizado. Estas características demostraron ser
suficientes para obtener una forma de onda de la respiración bien definida, de la cual es sencillo estimar la frecuencia
respiratoria mediante un sistema compacto, de bajo costo y bajo consumo de energía. A diferencia de los circuitos
de interfaz basado en la amplitud de tensión o corriente, con este tipo de interfaz se evita el autocalentamiento del
sensor, ya que el termistor no requiere ningún voltaje o corriente de polarización.
Palabras clave: medidas basadas en el tiempo, sensores en puente de Wheatstone, frecuencia
respiratoria, medida de temperatura, termistor nasal.
INTRODUCTION
Respiratory rate (RR) is one of the
basic physiological parameters that can
help to assess the health condition of
a subject. Several works have proposed
different methods for detecting the breathing
waveform and have estimated parameters
of interest related to the ventilation of the
subject [1-3]. Nasal thermistor is a wellknown and accurate method for recording the
respiratory phase, which reacts to variations
in air temperature [4]. Formerly, this method
was used for assessing respiratory patterns
and nocturnal events in studies aimed to
diagnose sleep disorders [5]. Nowadays, it is
still a reference in several polysomnographic
studies in the diagnosis of obstructive sleep
apnea [6, 7].
Resistive
sensors,
as
thermistors,
conveniently are set in voltage dividers or
in a Wheatstone bridge, which are suited for
sensors with large resistance variations and
nonlinear response [8]. With an appropriate
configuration, it is possible to linearize the
response of such sensors with circuits that
provides an output voltage (or current) that
depends on both the resistance variations
of the sensor and the supply voltage (or
current). In these conventional circuits, it
is necessary some analog signal processing
stages to adapt the voltage range to that
of the analog-to-digital converter (ADC).
Furthermore, the polarization stage can
cause a self-heating problem, increasing the
uncertainty of the measurement process. To
enhance resolution, it has been proposed
synchronous demodulation, which adds extra
processing stages on the measurement system
[9].
This paper proposes a novel system
based on a direct sensor-to-microcontroller
(µC) interface circuit (time-modulated
circuit). The system is implemented by a
thermistor in a Wheatstone quarter-bridge
topology directly connected to a µC without
any analog processing circuits, nor ADC;
which results in a compact solution to
93
Sifuentes et al. Respiratory Rate Detection by a Time-Based Measurement System
detect temperature variations. Such a direct
interface circuit relies on measuring the
discharging time of a RC network that
includes the resistances of the sensor bridge,
and by means of a time-based equation, it is
possible to estimate the fractional resistance
change x (∆R/R0 ) of the sensor [10].
In order to demonstrate the feasibility
of the proposed method, we used the wellknown nasal thermistor technique to detect
the breathing by measuring the thermal
fluctuations near the nostrils of several
subjects. Although the proposed method has
been successfully applied to piezoresistive
and magnetoresistive sensors in full and
half-bridge topologies [11, 12] to detect DC
or quasi-DC signals, at the best of our
knowledge, such method has not been used
to sense AC magnitudes (such as respiratory
rate), which could be extended to other
medical applications.
SENSING APPROACH
Thermistor description
In order to detect the temperature variations
near the nostrils, it is necessary to know the
thermistor characteristics (e.g., sensitivity
and time response). Commonly, a normal
breathing of an adult subject is between
12 and 15 breaths per minute, that is, a
bandwidth up of 0.25 Hz [13]. Temperature
fluctuations (∆T ) due to the subject breath
depend on the environmental temperature,
and it rarely exceeds 20 ◦ C if the surrounding
temperature is 13 ◦ C [14, 15]. In order to
obtain a breathing waveform to estimate the
RR, we considered that the system must be
able, in principle, to have a resolution of
0.5 ◦ C.
The thermistor used in this study is
the NTCLE305E4202SB (VISHAY). It has a
negative temperature coefficient (NTC), and
the resistance RT at any temperature T (over
a 50 ◦ C span) can be determined by an
exponential law [8]:
RT = R0 e
B
1
T
− T1
0
,
(1)
where B is the characteristic temperature
of the material, R0 is the resistance of
the thermistor at a reference temperature
T0 , usually 25 ◦ C. Here, we considered
a temperature span of 25 ◦ C (15 ◦ C 40 ◦ C) where the relative sensitivity of the
thermistor (α) has a nonlinear dependence on
T:
B
α=− 2
(2)
T
From (2), α15 = -4.23 %/K, α25 = -3.95
%/K α40 = - 3.58 %/K, which corresponds to
a maximal relative non-linearity error of 15
% (calculated from the best-fit straight line).
Theoretically, for a temperature resolution
of 0.5 ◦ C, the fractional resistance change
of the thermistor must be 2 % (x = 0.02),
which implies an effective resolution of 6
bit. According to [10], the method used to
measure x has an effective resolution of 8 bit
for a measuring time of 10 ms, and it has
a theoretical resolution of 0.1 ◦ C. Nonlinear
errors modify the shape of the temperature
variations, but not the estimation of the RR
[16]. A thermistor behaves as low-pass filter,
and the bandwidth depends on the thermal
constant τs . Thus, a high value in τs can
produce a time delay in the estimation of the
RR. Commonly, τs is often provided by the
manufacturer, but under specific conditions;
nevertheless, we can estimate τs from a simple
experimental setup in order to assert this
value. Table 1 shows the basic characteristics
given by the manufacturer of the thermistor
used.
Fundamentals of the interface circuit
Wheatstone bridges with resistive sensors
(quarter-bridge, half-bridge, and full-bridge)
can be directly connected to a µC by using
time-based measurement circuits that yield
a digital output that is proportional to the
change of x.
estimate the changes on $x$, the direct interface circuit
performs four discharging times measurements
($t_{d1}$, $t_{d2}$, $t_{d3}$ and $t_{d4}$) and applies a
time-based
equation accordingly with
the bridge
94
Revista Mexicana de Ingeniería Biomédica · volumen 37 · número 2 · May-Ago, 2016
topology [10].
and pins P2-P5, respectively, which results
Table 1: Basics characteristic of the thermistor
Table 1: Basics NTCLE305E4202SB.
characteristic of the thermistor in a RC circuit with a time constant τ =
NTCLE305E4202SB.
Parameter
Value
Unit
Reqi C. During the discharging time, when the
voltage across C reaches VTL (low threshold
Resistance value
2060
Ω
Value
Unit
◦ Parameter
voltage of the Schmitt Trigger (ST) input)
at 25 C (R0 )
Resistance value at 25 °C (R0)
2060
Ω
on pin P1, the timing process stops. The
Tolerance
on R25− value
±1.92
%
Tolerance on
$\pm$1.92
\%
count of the embedded timer is the digital
B
3511
K
R25- value
◦
Operating
Range
-40
to
+125
C
equivalent to the discharging time td . Figure
B
3511
K
Operating
Range
-40
to
+125
°C
Response time
2 shows the voltage waveform across C during
Response
timeair)
(in
stirred
≈3
s
the measurement, which is accomplished in
(in stirred air)
≈3
s
(in
oil)
≈
0.7
eight steps. Table 2 summarizes the µC
(in oil)
≈ 0.7
Weight
≈≈0.05
gg
Weight
0.05
pins configuration during the measurement
sequence.
A circuit
quarter-bridge
is considered
VDD
$RC$
with a timetopology
constant $\tau
= R_{eqi}C$.
when
R2 = R3time,
= Rwhen
(1 −
1 =
0 and
4 = R0across
DuringRthe
discharging
theRvoltage
x),
such
a
NTC
thermistor
(Figure
1).
In
$C$ reaches $V_{TL}$ (low threshold voltage of the
P5
this
case,
the respective
Schmitt
Trigger
(ST) input)equivalent
on pin P1,resistances
the timing
Req3
O3
P4
process
stops.
TheP2-P5
count ofand
the embedded
timer isthe
the
seen
from
pins
node A (when
R2
R1
digital equivalent
to theR
discharging
timeµC
$t_d$pin
. Figure
internal
resistance
are
ini of each
2
shows
the
voltage
waveform
across
C
during
the
O
2
R
considered)
are:
eq2
P3
I1
µC
P2
Req1
R3
R4
O1
Roff
Rp
A
C
P1
measurement, which is accomplished in eight steps.
Table 2 summarizes
$\mu C$ pins configuration
R0 (3 −the
3x)
Req1
=
+ Roff + Rin2
(3a)
during
the measurement
sequence.
4−x
A quarter-bridge topology is considered when
R0 (4 − 2x)
$R_1 R
= R_
$R_
= R_
- x)$, such
=R_3 = R_0$ and +
R4off
+0(1
Rin3
(3b)a
eq22 =
4 − x 1). In this case, the respective
NTC thermistor (Figure
R0 (3 −seen
x) from pins P2-P5 and node
equivalent resistances
Req3 =
+ R + Rin4
(3c)
A (when the internal
resistance off
$R_{ini}$ of each $\mu
4−x
C$ pin
considered)
R are =
R + Rare:
(3d)
eq4
off
thr
𝑡
[10
bri
𝑥
Re
𝑥
in5
Figure
1: Direct
sensor-to-$\mu
interface
circuit
Figure
1: Direct
sensor-to-µC C$
interface
circuit
for for
!! (!!!!)
𝑅!"!
+
𝑅!"" + 𝑅!"!
In = such
conditions,
resistive
bridge sensors.
!!!
resistive bridge sensors.
(3a)
the respective
discharging time through each equivalent
Figure
1
shows
a
direct
interface
circuit
for
is:
The measurement of each discharging time, $t_d$, resistance
!! (!!!!)
𝑅
=
+ 𝑅!"" + 𝑅!"!
(3b)
resistive
previously
!"#
involves
two bridge
stages: sensors,
(a) charging
and (b)analyzed
discharging
!!!
VDD
in [10,
17]. In thisFirst,
kind$C$
of is
interface,
tdi = Reqi C ln
(4)
and time
measurement.
charged the
through
VTL
resistive bridge is considered a network
$R_p$ (at least $5R_pC$) towards $V_{DD}$. Then $C$ 𝑅!"! = !!(!!!) + 𝑅!"" + 𝑅!"!
(3c)
with one input terminal and three output
The !!!
time-based equation, originally
is discharged
towards
$V_{
SS}$ (ground reference)
terminals. To estimate the changes on x,
to
𝑅!"! = 𝑅!""in+ [10]
𝑅!"! and improved in [17],(3d)
through
equivalent
$R_{eqi}$, four
between proposed
the each
direct
interfaceresistance,
circuit performs
estimate x in a quarter-bridge topology is:
nodedischarging
A and pins times
P2-P5,measurements
respectively, which
in a
(td1 , tresults
d2 , td3
Timer starts
and td4 ) and applies a time-based equation
Timer stops
V
accordingly with the bridge topology [10].
The measurement of each discharging
V
time, td , involves two stages: (a) charging
V
7
1
2
3
4
5
6
8
and (b) discharging and time measurement.
t
t
t
t
First, C is charged through Rp (at least
1,3,5,7 Charging stage
2,4,6,8 Discharging and time measurement stage
5Rp C) towards VDD . Then C is discharged
Figure
during aa full
full
Figure2:2: Voltage
Voltagewaveform
waveformacross
across$C$
C during
towards VSS (ground reference) through each
measurement sequence.
equivalent resistance, Reqi , between node A
measurement sequence.
DD
dep
$\m
equ
tho
res
reje
sta
eff
ma
dis
are
per
the
TL
SS
d1
d2
d3
d4
Table 2: Configuration of the $\mu C$ pins during the
measurement sequence.
Step
P1
P2
P3
P4
P5
De
the
mic
run
em
tim
vol
Sifuentes et al. Respiratory Rate Detection by a Time-Based Measurement System
Table 2: Configuration of the µC pins during the
measurement sequence.
Step
P1
P2
P3
P4
P5
1,3
5,7
2
4
6
8
Output “1”
Input
Input
Input
Input
Capture
Capture
Capture
Capture
Output “0”
Input
Input
Input
Input
Output “0”
Input
Input
Input
Input
Output “0”
Input
Input
Input
Input
Output “0”
x∗ =
2(td1 − td3 )
td2 + td3 − td1 − td4
(5)
Replacing (3) in (4), and subsequently in
(5), yields:
x∗ =
R0
x
R0 + ∆Rin35 + ∆Rin42
2∆Rin24
+
R0 + ∆Rin35 + ∆Rin42
(6)
Gain and offset errors are small because
they depend on the differences between Rini
of the µC. For instance, if the internal
resistances are equals, the errors will be
zero. On the other hand, those errors can
be corrected by calibration. The resistance
Rp in Figure 1 is included to improve the
rejection of power supply interferences in the
charging stage [18, 19]. Roff was included
to reduce the effects of Rini [17], also this
resistance limits the maximal current sunk
by each pin during the discharging and time
measurement. In [18, 19], there are some
design guidelines to improve the performance
of the direct interface circuits and therefore
the measurement.
DESIGN AND IMPLEMENTATION
Design of the measurement system
Figure 3 shows the proposed circuit for
detecting the breathing. It was implemented
by the microcontroller MSP430F123 (Texas
Instruments) running at 8 MHz (quartz
oscillator clock). So, the embedded 16-bit
timer/counter counts the discharge time
by incrementing its value each 125 ns.
The supply voltage of the µC was
VDD = 3.0 V, provided by a dedicated voltage
regulator (LF30CV) to reduce power supply
95
interference, which may result in trigger noise
[19]. The function of P1-P5 (Figure 1) was
implemented by P1.2, P3.7, P3.6, P3.3, and
P3.2, respectively. A quarter-bridge topology
was implemented by R1 = R2 = R3 = R0 =
2.2 kΩ resistances (with 1 % of tolerance and
50 ppm). The resistance of the NTC at 25
◦
C was close to 2.06 kΩ (see Table 1). The
thermistor was placed on R4 = R0 (1 − x).
To reduce the effects of the internal trigger
noise, the µC was set in LPM2 mode. This
option disables the CPU but remains in
active mode timers and interrupts, while
the discharging times are being measured
as suggested in [19]. The pin P1.2 (external
interrupt with ST buffer, capture mode) was
configured to interrupt the µC on falling edge
every time the discharging C voltage reaches
the VTL value. The µC program was written in
C language; however, to increase precision in
time measurements, the sequences shown in
Table 2 were written in assembler language.
C was selected to obtain a suitable time
constant, τ = Reqi C, for the discharging
and time measurement stage. A large τ value
implies a slow slew rate of the exponential
voltage waveform at the trigger point, which
makes the triggering process more susceptible
to noise, increasing the count dispersion and
the standard deviation of the measurement.
In contrast, a too small value of τ yields
few counts, giving a large quantization
error. Thus, the optimal time constant value
was experimentally determined, and it was
between 2 and 3 ms [10, 19]. Therefore, we
selected C = 1 µF, with ±5 % of tolerance
and 100 ppm/◦ C of temperature coefficient.
We chose Rp = 100 Ω that results in charging
times (5Rp C) of 500 µs.
The discharging times td1 , td2 , td3 and
td4 were measured, and x was estimated
by (5). Then, this value was sent to
a PC via RS-232 by a control program
in LabVIEWTM . The serial communication
interface was implemented by a MAX3223
supplied by a separated voltage regulator
(and was set in shutdown mode during
sensor.
Nasal
thermistor
Piezoelectric
sensor
Nasal
thermistor
Thermistor
Piezoelectric
sensor
Thermistor
a)
Figure
3. Time-based
measurement
system
for
Figure
3: Time-based
measurement
system for
detecting
detectingrate.
respiratory rate.
respiratory
the measuring
process) to prevent induced
Measurement
protocol
transients in the power supply that could
The process
to validate process
the proposed
affect
the discharging
[19]. method was
done over 23 volunteers (8 women and 15 men), all
with distinct physical characteristics: (mean $\pm$
SD: age = (27 $\pm$ 8) years; weight = (77 $\pm$
Measurement
protocol
15) kg; height
(1.72 $\pm$ 0.09)
m. We measured
thermal fluctuations by placing the thermistor near to
process
to validate
theaproposed
theThe
nostrils
of each
subject. As
reference method
signal, a
was
done
over
23
volunteers
(8
women
piezoelectric sensor LDT1-028K from Measurement
and 15 [20],
men),
with todistinct
Specialties
was all
attached
the chestphysical
of each
characteristics:
(mean
age = (27
±
volunteer
in order to
detect±theSD:
movements
of the
8) years;
= Figure
(77 ±
15) kg;
height
thorax
on eachweight
breathing.
4 depicts
the location
of(1.72
each sensor
on the subject
(a) and
the position
of the
± 0.09)
m. We
measured
thermal
thermistor
near
the
nostrils
(b).
The
signal
of
the
fluctuations by placing the thermistor near
piezoelectric
sensor was
processed
by a As
charge
to the nostrils
of each
subject.
a
amplifier
with
a
sensitivity
of
-212
mV/pC
and
filtered
reference signal, a piezoelectric sensor LDT1by028K
a first-order
low-pass filter with
a corner [20],
frequency
from Measurement
Specialties
was
ofattached
1 Hz.
to the chest of each volunteer in
The tomeasurements
were obtained
two
order
detect the movements
of thebythorax
procedures. In the first procedure called ``Controlled
on each breathing. Figure 4 depicts the
Breathing”, every subject was asked to breathe
location of each sensor on the subject (a)
following a baseline of an oscilloscope that showed a
and the position of the thermistor near the
sinusoidal signal with 1 V peak-to-peak and 0.25 Hz.
nostrils (b). The signal of the piezoelectric
In the second procedure called ``Free Breathing”, the
sensor
was processed
bysubjects
a charge
tests
were performed
while the
wereamplifier
breathing
sensitivity
filtered
at with
their aown
rhythm. of
On-212
eachmV/pC
subject, and
the test
was
by
a
first-order
low-pass
filter
with
a
repeated three times with a measurement time corner
of 30 s
frequency
1 Hz.obtained from nasal thermistor
each
test. Theofsignal
was compared with that obtained from a piezoelectric
b)
(a)Location of the sensors on (b)
Figure 4. (a)
the body
Figure
4:
(a)
Location
of
the
sensors
on
the body
during the measurement
protocol and (b) (b)
position
of during th
(a)
measurement
protocol
(b) position of the thermistor nea
the thermistor
near
the and
nostrils.
Figure 4: (a)
Location
of the sensors on the body during the
the nostrils.
measurement protocol and (b) position of the thermistor near
The
measurements were obtained by two
the nostrils.
procedures.RESULTS
In the first
called
ANDprocedure
DISCUSSION
RESULTS
AND
DISCUSSION
“Controlled Breathing”, every subject was
asked
to breathe
following a the
baseline
of anresponse i
In the
low-pass
In proposed
the proposedsystem,
system, the low-pass
response is
oscilloscope that showed a sinusoidal signal
limited
by the
time
response of the
Figure
5
limited
by the
time
response
thethermistor.
thermistor.
Figure
with depicts
1 V peak-to-peak
and 0.25ofHz.
In
the as a
the fractionalresistance
resistance
the
thermistor
depicts
the
fractional
of
the
thermistor
as
second
procedure
calledstep
“Free
response
to a thermal
input,Breathing”,
which was between
response
to a thermal
stepwhile
input,
was betwee
the tests
thewhich
subjects
23 °C were
and 9 performed
°C. We obtained
$\tau_s$
= 0.8 s, which is
23were
°Csuitable
and 9 °C.
We
obtained
$\tau_s$
0.8errors
s, which
i
breathing
at
their
own
rhythm.
On=each
for detecting
the RR.
The nonlinear
of
suitable
RR.
The
nonlinear
the for
thermistor
not
considered
because
were o
subject,
thedetecting
test were
was the
repeated
three
timeswe errors
only
interested
on
the
detection
of
an
AC
magnitude
the
thermistor
were not
because
with
a measurement
timeconsidered
of 30 s each
test. we wer
(temperature
fluctuations).
The interested
signal obtained
nasal of
thermistor
only
on
thefrom
detection
an AC magnitud
Figure 6 shows the signals obtained with the
was compared
with that obtained from a
(temperature
proposed fluctuations).
system and with the piezoelectric sensor.
piezoelectric
sensor.
Figure 6 shows the signals obtained with the
Both signals match in the number of breaths and also
proposed
system
and 0.25
withHzthe
piezoelectric
sensor
coincide
with the
of the
{\it Controlled
Breathing}
measured
during
the
30
s.
Both RESULTS
signals match
in the number of breaths and also
AND DISCUSSION
coincide0.8 with the 0.25 Hz of the {\it Controlled
Breathing}
measured
during
the 30 s.response
In the proposed
system,
the low-pass
0.6
is limited by the time response of the
0.4
thermistor.
Figure 5 depicts the fractional
0.8
resistance
of
the thermistor as a response to
0.2
0.6
a thermal step input, which was between 23
◦
1
1.5 τ =
2 0.8 2.5
3
4
C and 900 ◦ C. 0.5
We obtained
s, which
is3.5
s t/s
0.4
suitable
for5:detecting
thetime
RR.response
The nonlinear
Figure
Experimental
of the thermistor
for awere
thermal
between 23 °C and
errors
of the thermistor
notstep
considered
0.2 NTCLE305E4202SB
9
°C.
The
sensor
is
able
to
respond
in
0.8 s,the
enough for
because we were only interested on
detecting
the
breathing-related
thermal
fluctuations.
0
detection
of an AC
magnitude
0
0.5
1
1.5
2 (temperature
2.5
3
3.5
t/s
fluctuations).
Figure 5: Experimental time response of the thermisto
NTCLE305E4202SB for a thermal step between 23 °C an
9 °C. The sensor is able to respond in 0.8 s, enough fo
detecting the breathing-related thermal fluctuations.
x
_{d3}$
ed by
by a
serial
by a
ulator
uring
ower
9].
sensor.
Revista Mexicana de Ingeniería Biomédica · volumen 37 · número 2 · May-Ago, 2016
x
ption
mers
being
ernal
was
edge
s the
in C
time
were
ed to
$, for
large
the
point,
ptible
the
ast, a
ing a
time
nd it
, we
rance
chose
imes
96
0.6
-0.25
0.2
-0.2
-0.25
0.4
-0.3 -0.3
0
0
0.2
1
0.5
1.5
1
1.52
t/s
22.5
t/s
2.53
3 3.5
3.5 4
4
Figure Figure
5: Experimental
time time
response
of the
Experimental
response
of thermistor
the thermistor
Figure5: 5.
Experimental
time response
of the
NTCLE305E4202SB
for a thermal
step between
23 °C
NTCLE305E4202SB
for a thermal
step between
23and
°C and
for
aenough
thermal
9thermistor
°C.sensor
The sensor
able
to respond
ins,0.8
s, enough
for
9 °C. The
isNTCLE305E4202SB
ableis to
respond
in 0.8
for step
◦
detecting
the
thermal
fluctuations.
between
23 breathing-related
C and 9 ◦thermal
C. The
sensor
is able to respond
detecting
the breathing-related
fluctuations.
in 0.8 s, enough for detecting the breathing-related
thermal fluctuations.
-0.15
x
-0.2
20 20
2525
3030
1.5 1.5
f/Hzf/Hz
2 2
2.52.5
33
20
10
0
0.5 0.5
1
1
15
t/s
20
25
x
10
30
-0.18
-0.2
0
0
Amplitude/V/Hz
Voltage/V
15 15
t/s t/s
-0.16
5
0.5
-0.5
5
10
15
20
25
30
t/s
Figure 6. Controlled Breathing waveform obtained
Figure 6: Controlled Breathing waveform obtained from a
fromthermistor
a nasal thermistor
directlyto connected
a µC
nasal
directly connected
a $\mu C$ to
(top)
and
(top)a and
from a piezoelectric
film sensor
attached
from
piezoelectric
film sensor attached
to the
chest oftoa
volunteer
(bottom).
the chest
of a volunteer (bottom).
Figure7 and
6 shows
thefrequency
signals spectrum
obtained
Figures
8 show the
of
with
the
proposed
system
and
with
the
the breathing waveforms of two subjects who were
asked
to breathe at
their own
rhythm.
The signals
piezoelectric
sensor.
Both
signals
matchwere
in
obtained
with theofdirect
interface
can be
the number
breaths
andcircuit.
also As
coincide
seen,
remarkable differences.
withboth
the figures
0.25 Hzpresented
of the Controlled
Breathing
For
example,during
Figure the
7 shows
measured
30 s.a clear peak at 0.3 Hz
(18 breaths
per 7minute)
Figurethe
8 shows
a clear
Figures
and while
8 show
frequency
peak
at
0.16
Hz
(10
breath
per
minute).
These
spectrum of the breathing waveforms ofresults
two
demonstrate that the proposed system is able to detect
subjects who were asked to breathe at their
RR breath-by-breath, and it is also able to detect
own rhythm. The signals were obtained with
different breathing rates. Moreover, the frequency
the direct interface circuit. As can be seen,
spectrum of both signals shows a negligible
both figures presented remarkable differences.
contribution of noise.
For
example,
shows amethod
clear peak
The
sensitivityFigure
of the 7proposed
relies at
on
0.3
Hz
(18
breaths
per
minute)
while
the sensor sensitivity. Since the thermistorFigure
is not
8 showsbya any
clearconstant
peak at
0.16 Hz
(10 breath
supplied
voltage
or current,
selfper
minute).
These
results
demonstrate
heating problems are avoided and the sensitivity that
does
thedepends
proposed
system
is ablesource,
to detect
RR
not
on any
polarization
as usually
breath-by-breath,
and
it
is
also
able
happens in conventional signal conditioning systems.to
detect different breathing rates. Moreover,
the-0.2frequency spectrum of both signals shows
a negligible
contribution of noise.
-0.25
20
10 10
-0.14
1
-0.3
0
5
Figure
7: Free
Breathing
waveformobtained
obtainedfrom
froma anasal
nasal
Figure
7: Free
Breathing
waveform
Figure
7. Free
Breathing
waveform obtained
from a
thermistor
directly
connected
a $\muC$C$(top)
(top)and
andthe
the
thermistor
directly
connected
to toa $\mu
nasal
thermistor
directly
connected
to
a0.3
µC
(top) and
frequency
spectrum
showing
a clear
peak
0.3Hz.
Hz.
frequency
spectrum
showing
a clear
peak
at at
the frequency spectrum showing a clear peak at 0.3
Hz.
-0.3
-1
0
10
0
-0.25
-0.35
0
Amplitude/V/Hz
0
0.5
0
Amplitude/V/Hz
20
0
0
5
5
10
15
t/s
20
25
30
0.5
1
1.5
f/Hz
2
2.5
3
2
0
Figure 8. Free Breathing waveform obtained from a
Figure 8: Free Breathing waveform obtained from a nasal
nasal thermistor
connected
a µC
(top)
thermistor
directly directly
connected
to a $\mutoC$
(top)
andand
the
the frequency
spectrum
a clear
peak
frequency
spectrum
showingshowing
a clear peak
at 0.16
Hz.at 0.16
Hz.
Figure 9 shows a Bland-Altman plot that compares
the RR
time
interval of the
signals,method
detected
The
sensitivity
of breathing
the proposed
from
23 volunteers
withsensitivity.
the proposed Since
method the
and
reliestheon
the sensor
with
the
piezoelectric
sensor.
The
mean
bias
was
17
thermistor is not supplied by any constant
ms and the dispersion (with a 95 \% confidence
voltage or current, self-heating problems are
interval) was about 303 ms, which is practically
avoided and the sensitivity does not depends
negligible. Figure 10 shows the scatter plot and the
on any coefficient
polarization
as usually
correlation
for the source,
RR time interval,
where
happens
in conventional
signal
conditioning
both
were estimated
from signals
detected
with the
systems.method and with the piezoelectric sensor.
proposed
The Figure
correlation
the data on theplot
23
9 coefficient
shows a for
Bland-Altman
volunteers
was 0.95, which
is a statistically
significant
that compares
the RR
time interval
of
correlation.
the breathing signals, detected from the 23
volunteers with the proposed method and
0.8
with
the piezoelectric sensor. The mean bias
0.6
was0.417 ms and the dispersion (with 0.320
a 95 %
0.2
confidence interval) was about 303 ms, which
0.017
0
is practically
negligible. Figure 10 shows the
-0.2
- 0.286
scatter
plot and the correlation coefficient
-0.4
for-0.6the RR time interval, where both were
-0.8
estimated
from3 signals 4 detected5 with the
2
6
Average RR time interval from the signal detected with the piezoelectric sensor
and the NTC (in seconds)
5
10
15
t/s
20
25
30
Figure 9: Bland-Altman plot of each RR time interval
RR time interval of the signal detected with the NTC (s)
0.4
x
x
x
x
-0.2
RRPiezo-RRNTC (s)
h the
ensor.
d also
rolled
0.6
0.8
x
nse is
gure 5
or as a
etween
hich is
ors of
were
nitude
0.8
z
ing the
or near
the sensor sensitivity. Since the thermistor is not
shows
obtainedsensor.
with the
the sensor sensitivity. Since the thermistor is not
proposed Figure
system 6and
withthe
thesignals
piezoelectric
supplied
by any constant voltage or current, selfproposed system and with the piezoelectric sensor.
supplied by any constant voltage or current, selfBoth signals match in the number of breaths and also
heating problems are avoided and the sensitivity does
Both signals match in the number of breaths and also
heating problems are avoided and the sensitivity does
coincide with the 0.25 Hz of the {\it Controlled
not depends on any polarization source, as usually
coincide with the 0.25 Hz of the {\it Controlled
not depends on any polarization source, as usually
Breathing}
measured
during the
30 Detection
s.
Sifuentes et al.
Respiratory
Ratethe
System
happens
in conventional signal conditioning systems.97
Breathing}
measured
during
30 s. by a Time-Based Measurement
happens in conventional signal conditioning systems.
Fig
est
the
the
A
im
the
qu
$\m
(du
det
of
pro
res
of
tem
bre
con
Th
by
cap
cur
avo
can
the
tem
Bla
30
3
a nasal
and the
mpares
etected
od and
was 17
idence
ctically
nd the
where
ith the
sensor.
the 23
nificant
20
17
286
6
c sensor
interval
stor and
RR time interval of the signal detected with the NTC (s)
RRPiezo-RRNTC (s)
both were estimated from signals detected with the
$\mu C$. The temperature fluctuations near the nostrils
proposed method and with the piezoelectric sensor.
(due to breathing of the subjects) have been clearly
The correlation coefficient for the data on the 23
detected. The proposed system did not require the use
volunteers was 0.95, which is a statistically significant
of any analog processing circuits, nor ADC. The
98
correlation.
Revista Mexicana de Ingeniería
Biomédica
· volumen
· número
2 · May-Ago,
2016 in
proposed
circuit
could37detect
fractional
changes
resistance of $x >$ 0.01, which resulted in a resolution
of 0.2fractional
°C, enoughchanges
to followin theresistance
breathing-related
0.8
detect
of
0.6
temperature fluctuations. This achieved well-shaped
x > 0.01, which resulted in a resolution of
0.4
breathing
waveforms, with a negligible noise
0.320
◦
0.2contribution,
C, enoughfrom
to follow
the
breathing-related
0.2
which it
is easy
to estimate the RR.
0.017
0
temperature
fluctuations.
This
achieved
The system was
able to detect the
respiration
breath-0.2
- 0.286
well-shaped
breathing
waveforms,
with
ain a
by-breath,
just
by
measuring
the
discharging
time
-0.4
capacitor.
It
was
not
necessary
to
supply
any
voltage
negligible noise contribution, from which it is or
-0.6
current
to the the
thermistor,
so system
the self-heating
-0.8
easy
to estimate
RR. The
was ablewas
2
3
4
5
6
avoided. The methodology proposed in this research
to detect the respiration breath-by-breath,
Average RR time interval from the signal detected with the piezoelectric sensor
can be considered in other medical applications, where
and the NTC (in seconds)
just
measuring the
discharging
timethein abody
theby temperature
measurement
(e.g.,
capacitor.
It
was
not
necessary
to
supply
temperature) could be obtained by a resistiveany
sensor.
Figure9: 9.
Bland-Altman
ploteach
of RR
eachtime
RRinterval
time
Figure
Bland-Altman
plot of
voltage
or
current
to
the
thermistor,
so
the
Bland-Altman
and
Scatter
plots
were
used
to
compare
interval from
detected
from obtained
the signal
with the
detected
the signal
withobtained
the thermistor
and
de RR time was
interval
betweenThe
the signal
detected with
self-heating
avoided.
methodology
with
the piezoelectric
of the 23 volunteers.
thermistor
and withsensor
the piezoelectric
sensor of the 23
the
thermistor
and
that
detected
with
the
piezoelectric
proposed in this research can be considered
volunteers.
sensor. The calculated mean bias was less than 17 ms,
5.5
in other medical applications, where the
and the dispersion was lower than 303 ms, and
temperature
measurement (e.g., the body
correlation coefficient was 0.95, which is statistically
temperature)
could
be obtained by a resistive
significant.
sensor. Bland-Altman and Scatter plots were
ACKNOWLEDGMENTS
used to compare
de RR time interval between
the signal detected with the thermistor and
Thisdetected
work haswith
been the
funded
by PRODEPsensor.
(Programa
that
piezoelectric
para el Desarrollo Profesional Docente) and UACJ
The
calculated mean bias was less than 17
(Universidad Autónoma de Ciudad Juárez) México,
ms,project
and the
dispersion was lower than 303 ms,
UACJ-PTC-327.
and correlation coefficient was 0.95, which is
statistically significant.
r = 0.95
5
4.5
4
3.5
3
2.5
2
2
2.5
3
3.5
4
4.5
5
5.5
RR time interval of the signal detected with the piezoelectric sensor (s)
Figure
Correlation
analysis
of theofRR
Figure10:10.
Correlation
analysis
thetime
RRinterval
time
estimated from the signal detected with the thermistor and
interval
estimated
from thefrom
signal
with the
the
RR time
interval estimated
thedetected
signal detected
with
thermistor
andsensor.
the RR time interval estimated from
the
piezoelectric
the signal detected with the piezoelectric sensor.
CONCLUSIONS
proposed method and with the piezoelectric
A
simple,
low-cost
and compact
system
has data
been
sensor.
The
correlation
coefficient
for the
implemented
for
detecting
the
breathing
using
a
nasal
on the 23 volunteers was 0.95, which is a
thermistor.
Thesignificant
sensor, mounted
in a Wheatstone
statistically
correlation.
quarter-bridge topology, was directly connected to a
$\mu C$. The temperature fluctuations near the nostrils
(due to breathing
of the subjects) have been clearly
CONCLUSIONS
detected. The proposed system did not require the use
of any analog processing circuits, nor ADC. The
A simple,
low-cost
compact
system
hasin
proposed
circuit
could and
detect
fractional
changes
been implemented
forwhich
detecting
the
resistance
of $x >$ 0.01,
resulted
in breathing
a resolution
using
a
nasal
thermistor.
The
sensor,
of 0.2 °C, enough to follow the breathing-related
temperature
fluctuations.
This achieved
well-shaped
mounted in
a Wheatstone
quarter-bridge
breathing
a negligible
noise
topology, waveforms,
was directly with
connected
to a µC. The
contribution,
from
which
it
is
easy
to
estimate
the
RR.
temperature fluctuations near the nostrils
The system was able to detect the respiration breath(due to breathing of the subjects) have been
by-breath, just by measuring the discharging time in a
clearly detected.
The proposed
didor
capacitor.
It was not necessary
to supply system
any voltage
not
require
the
use
of
any
analog
processing
current to the thermistor, so the self-heating was
circuits,The
normethodology
ADC. The proposed
circuit
could
avoided.
proposed in
this research
can be considered in other medical applications, where
the temperature measurement (e.g., the body
temperature) could be obtained by a resistive sensor.
Bland-Altman and Scatter plots were used to compare
ACKNOWLEDGMENTS
This work has been funded by PRODEP
(Programa para el Desarrollo Profesional
Docente) and UACJ (Universidad Autónoma
de Ciudad Juárez) México, project UACJPTC-327.
REFERENCES
1. Schäfer A., et al., “Estimation of
breathing rate from respiratory sinus
arrhythmia: comparison of various
methods,” Ann. of Biomed. Eng., vol.
36, no 3, pp. 476-485, Jan. 2008.
2. Karlen W., et al., “Multiparameter
respiratory rate estimation from the
photoplethysmogram,” IEEE Trans. on
Biomed. Eng., vol. 60, no 7, pp. 19461953, Feb. 2013.
3. Chan A. M., et al., “Ambulatory
respiratory rate detection using ECG
Sifuentes et al. Respiratory Rate Detection by a Time-Based Measurement System
and triaxial accelerometer,” in 35th
Conf. of the IEEE Eng. in Med. and
Biol. Soc., Osaka, 2013, pp. 4058-4061.
99
12. Sifuentes E., et al., “Wireless magnetic
sensor node for vehicle detection with
optical wake-up,” IEEE Sensors J., vol.
11, no. 8, pp. 1669-1676, Jan. 2011.
4. Carskadon M. A., et al., “Respiration
during sleep in children,” Western
Journal of Medicine, vol. 128, pp. 477481, Jun. 1978.
13. Brown B. H., et al., “Medical Physics
and Biomedical Engineering,” Institute
of Physics, Madison, 2001, pp. 548-577.
5. American Thoracic Socienty “Standards
and indications for cardiopulmonary
sleep studies in children,” Am. J Respir.
Crit. Care Med., vol. 153, no. 2, pp. 866878, Feb. 1996.
14. Toba E., et al., “Non-Invasive
Measurement System for Human
Respiratory Condition and Body
Temperature,” in IEEE Int. Conf. on
MFI, Las Vegas NV, 1994, pp. 770-783.
6. Dinç A. E., et al, “Reliability of Sleep
Strip as a screening test in obstructive
sleep apnea patients,” Eur. Arch. of
Otorhinolaryngol., vol. 271, no. 10, pp.
1-6, Oct. 2014.
15. Storck K., et al., “Heat Transfer
Evaluation of the Nasal Thermistor
Technique,” IEEE Trans. On Biomed.
Eng., vol. 43, no. 12, pp. 1187-1191, Dec.
1996.
7. Tan H. L., et al., “Overnight
Polysomnography versus Respiratory
Polygraphy in the Diagnosis of Pediatric
Obstructive Sleep Apnea,” Sleep, Vol.
37, no. 2, pp. 255-260, Feb. 2014.
8. Pallàs-Areny R., Webster J. G.,
“Sensors and Signal Conditioning 2nd
Edition”, John Wiley & Sons, New
York, 2001, pp. 94-109.
9. Cuadras A., Casas O., “Determination
of heart rate using a high-resolution
temperature measurement,” IEEE
Sensors J., Vol. 6, no. 3, pp. 836-843,
Jun. 2006 .
10. Sifuentes E., et al., “Direct interface
circuit to linearise resistive sensor
bridges,” Sensors and Actuators A, vol.
147, no. 1, pp. 210-215, Sep. 2008.
11. Jordana J., Pallàs-Areny R., “A
simple, efficient interface circuit for
piezoresistive pressure sensors,” Sensors
and Actuators A, vol 127, no. 1, pp. 6973, Feb. 2006.
16. Farre R., et al., “Accuracy of
thermistors and thermocouples as
flow-measuring devices for detecting
hypopnoeas,” Eur. Respir. J., vol. 11,
no.1, pp. 179-182, Jan. 1998.
17. Sifuentes E., et al., “Improved direct
interface circuit for resistive full-and
half-bridge sensors,” in IEEE, 4th Int.
Conf. on Electrical and Electronics
Engineering, ICEEE 2007, Mexico,
2007, pp. 197-200.
18. Reverter F., “The art of directly
interfacing sensors to microcontrollers,”
Low Power Electronics and Applications
J., vol 2, no. 4, pp 265-281, 2012.
19. Reverter F., “Direct interface circuits
for sensors”, in: S. Nihtianov and A.
Luque, “Smart Sensors and MEMS:
Intelligent Devices and Microsystems
for Industrial Applications”, Woodhead
Publishing, Chap 2, pp 27-62, 2014.
20. LDT1-028K Shielded Piezo Sensors,
Measurement Specialties, Hampton,
VA, 2009, pp. 1-3.
100
Revista Mexicana de Ingeniería Biomédica · volumen 37 · número 2 · May-Ago, 2016
ib