I
n
t
e
r
n
at
ion
al
Jou
r
n
al
of
E
lec
t
r
ical
an
d
Com
p
u
t
e
r
E
n
gin
e
e
r
in
g
(
I
JE
CE
)
Vol.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
,
pp.
337
~
34
8
I
S
S
N:
2088
-
8708
,
DO
I
:
10
.
11591/i
jec
e
.
v
15
i
1
.
pp
3
37
-
34
8
337
Jou
r
n
al
h
omepage
:
ht
tp:
//
ij
e
c
e
.
iaes
c
or
e
.
c
om
M
e
asu
r
in
g a
n
xi
e
t
y l
e
ve
l
on
p
h
ob
ia
u
si
n
g
e
le
c
t
r
od
e
r
m
al
a
c
t
iv
ity,
e
le
c
t
r
o
c
ar
d
io
gr
am
a
n
d
r
e
s
p
ir
at
or
y si
gn
al
s
Kh
u
s
n
u
l
Ain
1
,
2
,
Os
m
ali
n
a
Nur
Rahm
a
1
,
2
,
E
n
d
ah
P
u
r
want
i
1
,
Ric
h
a
Var
yan
1
,
S
ayyid
u
l
I
s
t
igh
f
ar
I
t
t
aq
il
a
h
1
,
3
,
Dann
y
S
an
j
aya
Ar
f
e
n
s
ia
4
,
T
iara
D
i
ah
S
os
ial
i
t
a
5
,
F
it
r
iya
t
u
l
Q
u
lu
b
1
,
Rif
ai
Chai
1,
6
1
B
io
me
di
c
a
l
E
ngi
ne
e
r
in
g, F
a
c
ul
ty
of
S
c
ie
nc
e
a
nd
T
e
c
hnol
ogy,
U
ni
ve
r
s
it
a
s
A
ir
la
ngga
, S
ur
a
ba
ya
, I
ndone
s
i
a
2
B
io
me
di
c
a
l
E
ngi
ne
e
r
in
g I
nnova
ti
on R
e
s
e
a
r
c
h G
r
oup, F
a
c
ul
ty
of
S
c
ie
nc
e
a
nd
T
e
c
hnol
ogy, Unive
r
s
it
a
s
A
ir
la
ngga
, S
ur
a
ba
y
a
, I
ndone
s
ia
3
B
io
me
di
c
a
l
E
ngi
ne
e
r
in
g M
a
s
te
r
P
r
ogr
a
m S
tu
dy, F
a
c
ul
ty
of
S
c
i
e
nc
e
a
nd
T
e
c
hnol
ogy, Unive
r
s
it
a
s
A
ir
la
ngga
, S
ur
a
ba
ya
, I
ndone
s
ia
4
B
e
r
bi
na
r
I
ns
ig
ht
f
ul
I
ndone
s
ia
, S
ur
a
ba
ya
, I
ndone
s
ia
5
F
a
c
ul
ty
of
P
s
yc
hol
ogy, Unive
r
s
it
a
s
A
ir
la
ngga
, S
ur
a
ba
y
a
, I
ndone
s
ia
6
D
e
pa
r
tm
e
nt
of
E
ngi
ne
e
r
in
g T
e
c
hnol
ogi
e
s
, S
c
hool
of
S
c
ie
nc
e
,
C
omput
in
g a
nd E
ngi
ne
e
r
in
g T
e
c
hnol
ogi
e
s
, S
w
in
bur
ne
U
ni
ve
r
s
i
ty
of
T
e
c
hnol
ogy, M
e
lb
our
ne
, V
ic
to
r
ia
, A
us
tr
a
li
a
Ar
t
icle
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
ived
M
a
y
21,
2024
R
e
vis
e
d
Aug
28,
2024
Ac
c
e
pted
Oc
t
1,
2024
Peo
p
l
e
w
i
t
h
s
p
i
d
er
p
h
o
b
i
a
ex
p
eri
e
n
ce
ex
ce
s
s
i
v
e
an
x
i
et
y
react
i
o
n
s
w
h
en
ex
p
o
s
e
d
t
o
s
p
i
d
er
s
t
h
a
t
w
i
l
l
i
n
t
erfere
w
i
t
h
d
ai
l
y
l
i
fe.
D
i
ag
n
o
s
i
n
g
an
d
meas
u
r
i
n
g
an
x
i
et
y
l
e
v
el
s
i
n
p
at
i
en
t
s
w
i
t
h
s
p
i
d
er
p
h
o
b
i
a
i
s
a
co
m
p
l
e
x
ch
al
l
en
g
e.
Co
n
v
en
t
i
o
n
a
l
d
i
a
g
n
o
s
i
s
req
u
i
re
s
p
s
y
c
h
o
l
o
g
i
cal
ev
a
l
u
a
t
i
o
n
s
an
d
cl
i
n
i
ca
l
i
n
t
er
v
i
e
w
s
t
h
at
t
ak
e
t
i
me
an
d
o
ft
e
n
res
u
l
t
i
n
a
h
i
g
h
d
eg
ree
o
f
s
u
b
j
ec
t
i
v
i
t
y
.
T
h
erefo
re,
t
h
ere
i
s
a
n
e
ed
fo
r
a
mo
re
o
b
j
ect
i
v
e
a
n
d
eff
i
ci
e
n
t
ap
p
r
o
ach
t
o
meas
u
r
i
n
g
an
x
i
et
y
l
ev
e
l
s
i
n
p
a
t
i
e
n
t
s
.
T
h
i
s
s
t
u
d
y
p
erf
o
rms
an
x
i
et
y
l
ev
e
l
cl
a
s
s
i
fi
ca
t
i
o
n
b
as
e
d
o
n
el
ec
t
ro
d
erma
l
act
i
v
i
t
y
,
el
ect
r
o
card
i
o
g
ram
(E
CG
)
an
d
re
s
p
i
rat
o
ry
s
i
g
n
al
s
u
s
i
n
g
t
h
e
d
at
a
s
et
o
f
A
rach
n
o
p
h
o
b
i
a
s
u
b
j
ec
t
s
.
E
ac
h
raw
d
a
t
a
i
s
p
rep
r
o
ces
s
e
d
u
s
i
n
g
2
4
t
y
p
es
o
f
feat
u
re
s
.
Feat
u
re
p
erf
o
rman
ce
i
s
p
ro
ce
s
s
e
d
u
s
i
n
g
t
h
e
recu
rs
i
v
e
fea
t
u
re
el
i
m
i
n
a
t
i
o
n
met
h
o
d
.
D
a
t
a
p
r
o
ces
s
i
n
g
w
a
s
p
erf
o
rmed
i
n
3
a
n
x
i
et
y
l
e
v
el
s
(h
i
g
h
,
med
i
u
m,
l
o
w
)
an
d
t
w
o
an
x
i
et
y
l
ev
el
s
(
h
i
g
h
,
l
o
w
)
w
i
t
h
t
h
e
s
u
p
p
o
rt
v
ect
o
r
mach
i
n
e
me
t
h
o
d
a
n
d
h
o
l
d
-
o
u
t
v
a
l
i
d
at
i
o
n
met
h
o
d
(
7
:
3
).
T
h
e
p
erfo
rma
n
ce
o
f
t
h
e
mo
d
el
i
s
ev
al
u
a
t
ed
b
y
s
h
o
w
i
n
g
t
h
e
accu
racy
,
p
reci
s
i
o
n
,
recal
l
an
d
F
1
s
c
o
re
v
al
u
es
.
T
h
e
p
o
l
y
n
o
mi
a
l
k
ern
e
l
c
an
p
erfo
rm
o
p
t
i
mal
cl
as
s
i
f
i
cat
i
o
n
an
d
o
b
t
ai
n
1
0
0
%
accu
rac
y
i
n
2
cl
a
s
s
e
s
an
d
t
h
ree
cl
a
s
s
e
s
w
i
t
h
1
0
0
%
p
reci
s
i
o
n
,
reca
l
l
,
a
n
d
F
1
s
co
re
v
al
u
es
.
T
h
i
s
re
s
u
l
t
s
h
o
w
s
ex
ce
l
l
e
n
t
p
o
t
en
t
i
a
l
i
n
mea
s
u
r
i
n
g
an
x
i
et
y
l
e
v
el
s
t
h
a
t
co
rre
l
at
e
w
i
t
h
men
t
a
l
h
eal
t
h
i
s
s
u
e
s
.
K
e
y
w
o
r
d
s
:
Anxie
ty
E
lec
tr
oc
a
r
diogr
a
m
E
lec
tr
ode
r
mal
a
c
ti
vit
y
P
hobia
R
e
s
pir
a
tor
y
s
ignals
S
uppor
t
ve
c
tor
mac
hine
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
Os
malina
Nur
R
a
hma
B
iom
e
dica
l
E
nginee
r
ing
S
tudy
P
r
og
r
a
m,
F
a
c
ult
y
o
f
S
c
ienc
e
a
nd
T
e
c
hnology,
Unive
r
s
it
a
s
Air
langga
J
l.
Dr
.
I
r
S
oe
ka
r
no
,
C
C
a
mpus
,
S
ur
a
ba
ya
60115,
I
n
done
s
ia
E
mail:
os
malina.
n
.
r
a
hma@
f
s
t.
una
ir
.
a
c
.
id
1.
I
NT
RODU
C
T
I
ON
P
e
ople
with
phobia
e
xpe
r
ienc
e
a
n
e
xa
gge
r
a
ted
a
n
xiety
r
e
a
c
ti
on
a
nd
thi
s
of
ten
int
e
r
f
e
r
e
s
with
their
da
il
y
li
ve
s
i
f
not
t
r
e
a
ted
we
ll
.
F
e
e
li
ngs
o
f
f
e
a
r
o
r
a
nxiety
a
bout
s
omething
that
doe
s
not
c
a
us
e
a
c
tual
ha
r
m
a
r
e
of
ten
r
e
f
e
r
r
e
d
to
a
s
phobias
[
1]
.
Appr
oxim
a
tely
7.
4%
of
the
human
population
ha
s
e
xpe
r
ienc
e
d
a
s
pe
c
if
ic
phobia
a
t
lea
s
t
onc
e
in
their
li
f
e
ti
me
[
2]
.
S
pe
c
i
f
ic
phobias
a
r
e
pe
r
s
is
tent
a
nd
e
x
c
e
s
s
ive
phobias
of
a
s
pe
c
if
ic
objec
t
or
s
it
ua
ti
on,
s
uc
h
a
s
c
laus
tr
ophobia,
whic
h
is
the
f
e
a
r
of
c
los
e
d
or
locke
d
plac
e
s
,
c
e
r
tain
objec
ts
,
s
uc
h
a
s
ne
e
dles
a
nd
knives
,
a
nd
z
oophobia.
Z
oophobia
is
ge
ne
r
a
ll
y
de
f
ined
a
s
the
f
e
a
r
o
f
c
e
r
tain
types
of
a
nim
a
ls
.
S
ome
phobias
include
d
in
thi
s
g
r
oup
a
r
e
a
r
a
c
hnophobia
(
f
e
a
r
o
f
s
pider
s
)
,
s
c
oli
ode
ntos
a
ur
ophobia
(
f
e
a
r
o
f
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
:
337
-
34
8
338
li
z
a
r
ds
)
,
r
a
nidaphobia
(
f
e
a
r
of
f
r
ogs
)
,
ophidi
ophob
ia
(
f
e
a
r
of
s
na
ke
s
)
,
ka
ts
a
r
idaphobia
(
f
e
a
r
of
c
oc
kr
oa
c
he
s
)
,
mus
ophobia
(
f
e
a
r
of
r
a
ts
)
,
c
ynophobia
(
f
e
a
r
o
f
do
gs
)
a
nd
many
other
s
.
S
pe
c
if
ic
phobias
a
r
e
one
of
the
mos
t
c
omm
on
ps
yc
hologi
c
a
l
dis
or
de
r
s
,
a
c
c
ounti
ng
f
o
r
a
ppr
oxim
a
tely
7
%
–
11%
of
the
ge
ne
r
a
l
populat
ion
[
3
]
.
All
youngs
ter
s
up
to
the
a
ge
of
14
or
15
may
de
ve
lop
s
c
hool
phobia,
e
s
pe
c
ially
if
they
ha
ve
a
ba
d
e
x
pe
r
ienc
e
a
t
s
c
hool
[
4]
.
I
n
a
ddit
ion
,
ther
e
is
a
s
ur
ve
y
that
in
one
ye
a
r
,
the
r
e
we
r
e
9%
of
r
e
por
ted
c
a
s
e
s
of
s
pe
c
if
ic
phobia,
with
the
p
r
e
va
lenc
e
of
the
pos
s
ibi
li
ty
o
f
in
divi
dua
ls
e
xpe
r
ienc
ing
s
pe
c
if
ic
phobia
a
r
ound
10
%
to
13
%
[
5]
.
I
n
s
pe
c
if
ic
phobias
,
wome
n
a
r
e
twice
a
s
li
ke
ly
[
6]
.
Among
the
phobias
a
r
e
f
e
a
r
o
f
he
ight
s
[
7]
,
e
nc
los
e
d
s
pa
c
e
s
[
8]
,
da
r
kne
s
s
[
9]
,
a
nd
ins
e
c
ts
[
10]
.
T
he
mos
t
c
omm
on
c
a
us
e
of
s
pe
c
if
ic
phobia
is
s
pider
ph
obia.
P
e
ople
with
s
pider
phobia
e
xpe
r
ienc
e
a
n
e
xa
gge
r
a
ted
a
nxiety
r
e
a
c
ti
on
whe
n
they
a
r
e
e
xpos
e
d
to
s
pider
s
,
e
ve
n
if
the
s
pider
s
a
r
e
ha
r
ml
e
s
s
.
T
his
of
ten
int
e
r
f
e
r
e
s
with
their
da
il
y
li
ve
s
,
s
uc
h
a
s
ke
e
ping
a
di
s
tanc
e
f
r
om
plac
e
s
whe
r
e
s
pider
s
may
a
ppe
a
r
or
ha
ving
dif
f
iculty
s
lee
ping.
T
he
ne
ga
ti
ve
im
pa
c
t
of
s
pider
phobia
on
qua
li
ty
o
f
l
if
e
high
li
ghts
the
im
por
tanc
e
o
f
pr
ope
r
diagnos
is
a
nd
tr
e
a
tm
e
nt.
How
e
ve
r
,
diagnos
ing
a
nd
mea
s
ur
ing
a
nxiety
leve
ls
in
pa
ti
e
nts
with
s
pider
p
hobia
is
a
c
ompl
e
x
c
ha
ll
e
nge
.
C
onve
nti
ona
l
diagnos
is
r
e
qu
ir
e
s
ps
yc
hologi
c
a
l
e
va
luations
a
nd
c
li
nica
l
int
e
r
vi
e
ws
that
take
ti
me
a
nd
of
ten
r
e
s
ult
in
a
high
de
gr
e
e
of
s
ubj
e
c
ti
vit
y.
T
he
r
e
f
or
e
,
ther
e
is
a
ne
e
d
f
o
r
a
mor
e
obje
c
ti
ve
a
nd
e
f
f
icie
nt
a
ppr
oa
c
h
to
mea
s
ur
ing
a
nxiety
leve
ls
in
p
a
ti
e
nts
.
S
ome
of
the
phys
iol
ogica
l
mea
s
ur
e
ment
pur
pos
e
s
that
a
r
e
wide
ly
a
ppli
e
d
in
the
medic
a
l
f
ield
s
uc
h
a
s
r
e
duc
ing
s
tr
e
s
s
or
menta
l
wor
kload
we
r
e
c
a
r
d
iovas
c
ular
,
e
ye
moveme
nt,
e
lec
tr
oe
nc
e
pha
logr
a
m
(
E
E
G)
,
r
e
s
pir
a
ti
on,
e
lec
tr
omyogr
a
m
(
E
M
G)
,
a
nd
s
kin
c
a
tegor
ies
[
11]
;
ove
r
c
omi
ng
he
a
da
c
he
s
,
whe
r
e
E
M
G
biof
e
e
dba
c
k
ther
a
py
is
s
uc
c
e
s
s
f
ul
in
r
e
duc
ing
the
l
e
ve
l
of
a
c
ute
he
a
da
c
he
s
[
12]
;
e
mot
ion
mea
s
ur
e
me
nt,
whe
r
e
biof
e
e
dba
c
k
s
e
ns
or
s
c
a
n
be
us
e
d
in
e
mot
ion
m
e
a
s
ur
e
ment
a
nd
he
lp
c
ontr
ol
the
s
pe
e
d
a
nd
int
e
ns
it
y
of
e
mot
ions
f
e
lt
.
T
he
li
ter
a
tu
r
e
of
f
e
r
s
a
we
a
lt
h
o
f
in
f
or
mation
r
e
ga
r
ding
the
e
f
f
e
c
ts
of
e
mot
ion
r
e
gulati
on
(
E
R
)
ther
a
pies
on
menta
l
he
a
lt
h
a
nd
we
ll
ne
s
s
[
13]
.
I
n
th
e
us
e
of
bio
f
e
e
dba
c
k,
s
pe
c
ial
s
e
ns
or
s
a
r
e
li
nke
d
to
de
vice
s
that
dis
play
inf
or
mation
a
bout
the
body
’
s
phys
iol
ogica
l
f
unc
ti
ons
in
r
e
a
l
-
ti
me.
I
n
a
ddit
ion,
p
e
ople
a
r
e
us
ing
digi
tal
tec
hnologi
e
s
mor
e
a
nd
mor
e
to
c
ontr
ol
a
nd
pos
it
ively
a
f
f
e
c
t
their
a
f
f
e
c
ti
ve
s
tate
s
,
whic
h
include
their
s
tr
e
s
s
leve
ls
,
e
mot
ions
,
a
nd
mood
[
14]
.
A
pe
r
s
on
’
s
a
nxiety
leve
l
c
a
n
be
a
na
lyze
d
f
r
om
phys
iol
ogica
l
s
ignals
,
a
s
the
human
a
utonom
ic
ne
r
vous
s
ys
tem
is
c
a
pa
bl
e
of
pr
oduc
ing
r
e
s
pons
e
s
to
r
e
gulate
bodil
y
f
unc
ti
ons
,
s
uc
h
a
s
c
a
r
diac
a
c
ti
vit
y
[
15]
.
C
r
it
ica
l
phys
iol
ogica
l
r
e
s
pons
e
s
r
e
late
d
to
a
nxiety
c
a
n
be
obtaine
d
f
r
om
e
lec
tr
oc
a
r
diogr
a
m
(
E
C
G)
,
e
lec
tr
ode
r
mal
a
c
ti
vit
y
(
E
DA
)
,
a
nd
r
e
s
pir
a
ti
on
(
R
S
P
)
s
ignals
[
16]
.
T
he
us
e
of
thes
e
s
ignals
in
a
na
lyzing
a
nxiety
leve
ls
in
pa
ti
e
nts
wit
h
s
pider
phobia
ha
s
e
xc
e
ll
e
nt
potential
.
T
he
r
e
is
a
ne
e
d
f
o
r
ne
w
wa
ys
s
uc
h
a
s
mac
hine
lea
r
ning
whic
h
ha
s
be
e
n
pr
ove
n
to
be
us
e
d
a
s
a
n
e
f
f
e
c
ti
ve
c
las
s
if
ica
ti
on
t
ool,
s
uc
h
a
s
us
ing
the
method
s
uppor
t
ve
c
to
r
mac
hine
(
S
VM
)
,
na
ive
B
a
ye
s
,
a
nd
de
c
is
ion
tr
e
e
s
.
I
n
a
pr
e
vious
s
tudy,
He
a
ley
c
onc
luded
that
us
ing
a
li
ne
a
r
dis
c
r
im
inator
in
de
tec
ti
ng
s
tr
e
s
s
leve
ls
ba
s
e
d
on
E
M
G,
E
C
G,
E
DA
,
a
nd
R
S
P
s
ignals
,
a
nd
br
e
a
thi
ng
in
24
dr
iver
s
in
B
os
ton
obtaine
d
97.
4%
a
c
c
ur
a
c
y
[
17]
.
Ke
s
ha
n
a
nd
C
he
n
us
ing
the
s
a
me
da
ta
but
di
f
f
e
r
e
nt
c
las
s
if
ier
s
a
nd
windowing
dur
a
ti
ons
(
5
mi
n
utes
a
nd
10
s
e
c
onds
)
,
s
howe
d
that
us
ing
the
S
VM
metho
d
us
ing
E
C
G
s
ignals
,
E
DA
,
a
nd
R
S
P
s
ignals
obt
a
ined
a
n
a
c
c
ur
a
c
y
of
89%
in
the
de
tec
ti
on
of
two
leve
ls
o
f
s
tr
e
s
s
,
a
nd
in
the
de
c
is
ion
tr
e
e
method
to
c
las
s
if
y
thr
e
e
leve
ls
of
s
tr
e
s
s
obtaine
d
a
n
a
c
c
ur
a
c
y
of
a
bout
70%
[
18]
,
[
19
]
.
I
hmi
g
e
t
al
.
[
20]
us
ing
a
r
a
c
hnophobia
t
r
e
a
tm
e
nt
da
ta
with
s
ix
types
of
e
xtr
a
c
ti
on
f
e
a
tur
e
s
a
nd
a
10
-
f
old
c
r
os
s
-
va
li
da
ti
on
va
li
da
ti
on
method
in
the
ba
g
ge
d
tr
e
e
s
c
las
s
if
ica
ti
on
method,
obtaine
d
a
n
a
c
c
ur
a
c
y
of
89.
8%
in
two
-
leve
l
a
nxiety
c
las
s
e
s
a
nd
74.
4%
in
thr
e
e
-
leve
l
a
nxiety
c
las
s
e
s
.
B
a
s
e
d
on
the
a
bov
e
r
e
s
e
a
r
c
h,
the
a
c
c
ur
a
c
y
va
lue
f
or
the
c
las
s
if
ica
ti
on
of
phobic
pa
ti
e
nts
is
s
ti
ll
r
e
latively
low
,
e
s
pe
c
ially
in
the
thr
e
e
-
leve
l
c
las
s
if
ica
ti
on
(
low,
medium,
high)
.
S
o
,
a
c
las
s
if
ica
ti
on
method
is
ne
e
de
d
that
c
a
n
p
r
ovide
a
highe
r
a
c
c
ur
a
c
y
va
lue.
I
n
thi
s
a
nxiety
leve
l
c
las
s
if
ica
ti
on,
r
e
s
e
a
r
c
he
r
s
u
s
e
d
the
S
VM
model
on
da
ta
f
or
s
pider
phobia
s
uf
f
e
r
e
r
s
.
T
he
S
VM
method
is
a
powe
r
f
ul
too
l
in
da
ta
c
las
s
s
e
pa
r
a
ti
on
a
nd
ha
s
be
e
n
wide
ly
us
e
d
in
c
las
s
if
ica
ti
on
pr
oblems
.
I
t
is
e
xplaine
d
that
the
S
VM
method
is
s
uit
a
ble
f
o
r
the
da
ta
c
las
s
if
ica
ti
on
pr
oc
e
s
s
be
c
a
us
e
it
ha
s
a
high
-
dim
e
ns
ional
f
e
a
tur
e
s
pa
c
e
[
2
1]
.
I
n
a
ddit
ion
,
S
VM
is
an
e
f
f
icie
nt
c
las
s
if
ier
with
s
e
v
er
al
be
ne
f
it
s
,
including
a
good
ge
ne
r
a
li
z
a
ti
on
o
f
ne
w
o
bjec
ts
a
nd
a
r
e
pr
e
s
e
ntation
that
r
e
li
e
s
on
a
s
mall
n
umber
of
pa
r
a
mete
r
s
[
22]
.
L
a
be
li
ng
method
a
c
c
or
ding
to
th
e
s
our
c
e
da
tas
e
t
to
e
li
mi
na
te
s
ubjec
ti
vit
y,
window
ing
s
ize
(
10
s
)
to
e
xtr
a
c
t
mor
e
da
ta
us
ing
f
e
a
tur
e
s
s
uit
a
ble
f
or
biof
e
e
dba
c
k
s
ignal
d
is
tr
ibut
ion
to
im
p
r
ove
the
a
c
c
ur
a
c
y
of
the
a
lgor
it
hm
model.
I
n
the
c
ontext
of
thi
s
r
e
s
e
a
r
c
h,
S
VM
c
a
n
be
us
e
d
to
de
ve
lop
a
c
las
s
if
ica
ti
on
model
uti
li
z
ing
E
DA
,
E
C
G,
a
nd
r
e
s
pir
a
tor
y
da
ta,
whic
h
is
pr
oc
e
s
s
e
d
us
ing
24
e
xtr
a
c
ted
f
e
a
tur
e
s
a
nd
va
li
da
ted
by
the
hold
-
out
va
li
da
ti
on
method
.
T
his
model
is
e
xpe
c
ted
to
p
r
ovide
a
n
objec
ti
ve
a
nd
e
f
f
icie
nt
c
las
s
if
ica
ti
on
s
ys
tem
f
or
e
a
r
ly
diagnos
is
,
tr
e
a
tm
e
nt,
a
nd
moni
to
r
i
ng
of
pa
ti
e
nts
.
W
he
n
s
uc
h
f
e
a
r
a
nd
a
nxiety
a
r
is
e
,
it
be
c
omes
a
pr
oblem
that
ne
e
ds
to
be
a
ddr
e
s
s
e
d,
a
s
it
c
a
n
be
phys
ica
ll
y,
c
ognit
ively,
a
nd
mor
a
ll
y
dis
r
upt
ive.
T
he
r
e
f
or
e
,
phobias
ne
e
d
to
be
a
ddr
e
s
s
e
d
thr
ough
a
p
pr
opr
iate
tr
e
a
tm
e
nt.
P
hobias
c
a
n
be
e
a
s
e
d
or
e
ve
n
e
li
mi
na
ted
by
va
r
ious
methods
,
including
dr
ug
ther
a
py
a
nd
ps
yc
hologi
c
a
l
ther
a
py
[
23]
.
One
of
the
e
f
f
e
c
ti
ve
t
he
r
a
pies
us
e
d
to
ove
r
c
ome
phobias
is
c
ognit
ive
b
e
ha
viour
ther
a
py
(
C
B
T
)
,
whic
h
c
ons
is
ts
of
e
xpos
ur
e
the
r
a
py
a
nd
c
ognit
ive
r
e
s
tr
uc
tur
ing
ne
e
de
d
f
or
pe
ople
w
ho
ha
ve
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
M
e
as
ur
ing
anx
iety
lev
e
l
on
phobia
us
ing
e
lec
tr
od
e
r
mal
ac
ti
v
it
y
,
e
lec
tr
oc
ar
diogr
am
and…
(
K
hus
nul
A
in
)
339
s
pe
c
if
ic
phobias
.
Ac
c
or
ding
to
M
a
ns
e
ll
,
C
B
T
is
a
the
r
a
py
that
s
hows
mor
e
e
f
f
e
c
ti
ve
r
e
s
ult
s
f
or
a
nxiety
dis
or
de
r
s
a
nd
phobias
[
24]
,
[
25]
.
I
n
a
ddit
ion
,
a
ps
yc
hologi
c
a
l
ther
a
py
that
c
a
n
be
us
e
d
to
r
e
duc
e
s
pe
c
if
ic
phobias
or
tr
e
a
tm
e
nt
of
a
nxiety
dis
or
de
r
s
is
vi
r
tu
a
l
r
e
a
li
ty
e
xpos
ur
e
the
r
a
py
(
VR
E
T
)
[
26]
,
[
27]
.
E
xpos
ur
e
ther
a
py
c
a
n
be
c
onduc
ted
in
vivo,
whe
r
e
pa
ti
e
nts
a
r
e
e
xpos
e
d
to
phobic
s
ti
mul
i
in
r
e
a
l
li
f
e
,
or
in
s
e
ns
u
,
whe
r
e
pa
ti
e
nts
a
r
e
e
xpos
e
d
to
phobic
objec
ts
in
thei
r
im
a
gination
[
27
]
.
T
he
r
e
f
or
e
,
the
main
ob
jec
ti
ve
of
thi
s
s
tudy
is
to
de
ve
lop
a
c
las
s
if
ica
ti
on
model
that
c
a
n
identif
y
a
nxiety
leve
ls
in
pa
ti
e
nts
with
s
pider
phobia
ba
s
e
d
on
E
DA
a
nd
E
C
G
s
ignal
da
ta
us
ing
the
S
VM
method.
As
s
uc
h,
thi
s
r
e
s
e
a
r
c
h
ha
s
the
potential
to
pr
ovide
ne
w
ins
ight
s
int
o
the
unde
r
s
tanding
o
f
a
nxiety
leve
ls
in
pa
ti
e
n
ts
with
phob
ias
.
T
his
r
e
s
e
a
r
c
h
is
e
xpe
c
ted
to
he
l
p
menta
l
he
a
lt
h
pr
of
e
s
s
ionals
in
making
de
c
is
ions
to
de
s
ign
a
ppr
opr
iate
tr
e
a
tm
e
nt,
be
ing
a
ble
to
be
tt
e
r
mo
nit
or
the
pa
ti
e
nt
’
s
c
ondit
ion
dur
ing
ther
a
py
wi
th
the
us
e
of
t
he
s
e
s
e
ns
or
s
a
nd
da
ta
pr
oc
e
s
s
ing
methods
.
2.
M
AT
E
R
I
AL
S
AN
D
M
E
T
HO
D
S
2
.
1.
Dat
a
c
oll
e
c
t
ion
Da
ta
of
s
pider
phobia
pa
ti
e
nts
who
we
r
e
tr
e
a
ted
we
r
e
obtaine
d
thr
ough
the
ope
n
-
a
c
c
e
s
s
we
bs
it
e
of
P
hys
ioNe
t
(
htt
ps
:
//
phy
s
ionet.
or
g/conte
nt/
e
c
g
-
s
pider
-
c
li
p/1.
0.
0/
)
[
20]
,
[
28
]
,
[
29
]
.
T
he
da
tas
e
t
c
ontains
s
e
ve
r
a
l
r
a
w
da
ta
with
biom
a
r
ke
r
s
,
s
uc
h
a
s
E
C
G,
E
DA
,
a
nd
R
S
P
.
T
h
is
da
ta
wa
s
c
oll
e
c
ted
us
ing
a
B
it
a
li
no
(
r
)
e
volut
ion
B
luetooth
low
e
ne
r
gy
(
B
L
E
)
de
vice
(
f
ir
mwa
r
e
ve
r
s
ion
5.
1)
.
T
his
a
nxiety
leve
l
c
las
s
if
ica
ti
on
s
tudy
u
s
e
s
E
C
G
a
nd
E
DA
s
ignal
pa
r
a
mete
r
s
,
a
s
we
ll
a
s
B
R
s
ignals
,
a
s
va
li
da
ti
on.
T
he
da
tas
e
t
c
ontains
phys
iol
ogica
l
E
DA
,
he
a
r
t
r
a
t
e
va
r
iabili
ty
(
HR
V
)
,
a
nd
br
e
a
th
r
a
te
(
BR
)
da
ta
of
57
s
ubjec
ts
a
ge
d
18
-
40
ye
a
r
s
.
Da
ta
we
r
e
c
oll
e
c
ted
a
t
S
a
a
r
land
Unive
r
s
it
y
,
Ge
r
many
,
f
r
om
J
uly
2017
to
J
uly
2018.
E
a
c
h
s
ubjec
t
ga
ve
wr
it
ten
inf
or
med
c
ons
e
nt.
B
e
f
or
e
the
da
ta
c
oll
e
c
ti
on
pr
oc
e
s
s
,
e
a
c
h
s
ubj
e
c
t
wa
s
e
xplaine
d
a
bout
the
pr
oc
e
dur
e
s
a
nd
a
ppe
a
ls
to
be
done
f
or
e
a
c
h
s
e
s
s
ion.
All
s
ubjec
ts
we
r
e
int
e
r
view
e
d
a
nd
given
s
ome
be
ha
vior
a
l
tes
ts
s
o
that
they
c
ould
be
gr
oupe
d
a
c
c
or
ding
to
their
phobia
leve
l.
P
a
r
ti
c
ipa
nts
we
r
e
c
ons
ider
e
d
a
s
indi
viduals
with
int
e
r
media
te
pho
bia
if
they
ha
d
a
t
lea
s
t
14
point
s
on
the
Ge
r
man
S
pider
Anxie
ty
S
c
r
e
e
ning
tes
t
[
30]
,
whic
h
is
a
c
omm
only
us
e
d
thr
e
s
hold
point
[
31
]
,
[
32]
,
a
nd
ha
d
a
t
lea
s
t
5
0
point
s
on
the
f
e
a
r
of
s
pider
s
que
s
ti
onna
ir
e
tes
t.
I
n
a
ddit
ion,
ha
ving
a
t
lea
s
t
4
point
s
on
the
int
e
r
view
f
or
menta
l
dis
or
de
r
s
on
the
AD
I
S
s
e
c
ti
on
‘
s
pe
c
if
ic
phobias
’
[
33]
.
S
ubjec
t
c
r
i
ter
ia
we
r
e
a
ls
o
ba
s
e
d
on
the
pr
e
s
e
nc
e
of
other
menta
l
dis
or
de
r
s
be
s
ides
a
r
a
c
hnophobia
(
pa
ti
e
nt
he
a
lt
h
que
s
ti
onna
ir
e
a
nd
be
c
k
de
pr
e
s
s
ion
in
ve
ntor
y
)
a
nd
the
pr
e
s
e
nc
e
of
c
onge
nit
a
l
c
a
r
d
iovas
c
ular
dis
e
a
s
e
.
T
he
be
ha
viour
a
l
tes
t
s
e
s
s
ion
wa
s
c
onduc
ted
us
ing
the
pr
inciples
o
f
the
be
ha
viour
a
l
a
pp
r
oa
c
h
tes
t
method
a
da
pted
f
r
om
a
pr
e
vious
s
im
il
a
r
s
tudy
by
He
nne
mann
a
nd
M
icha
e
l
[
34]
c
ons
is
ti
ng
of
a
pr
oc
e
dur
e
in
whic
h
pa
r
ti
c
ipants
we
r
e
a
s
ke
d
to
s
tand
in
f
r
ont
of
a
c
los
e
d
r
oom
c
ontaining
a
hous
e
s
pider
(
T
e
ge
na
r
ia
a
tr
ica
)
mea
s
ur
ing
a
ppr
oxim
a
tely
5
c
m
(
including
legs
)
.
T
he
s
pider
s
we
r
e
plac
e
d
in
a
t
ight
plas
ti
c
c
ontaine
r
on
a
table
a
t
the
e
nd
o
f
the
r
oom.
Ne
xt,
pa
r
t
icipa
nts
we
r
e
a
s
ke
d
to
e
nter
the
r
oom,
a
ppr
oa
c
h
the
c
ontaine
r
,
ope
n
the
li
d,
ins
e
r
t
their
ha
nds
,
a
nd
t
r
y
to
pick
up
a
nd
hold
the
s
pider
f
o
r
a
t
lea
s
t
20
s
e
c
onds
.
W
he
n
pa
r
ti
c
ipants
tr
ied
to
pick
up
or
touch
the
s
pider
or
whe
n
they
de
c
ided
t
o
s
top
the
a
ppr
oa
c
h,
the
r
e
maining
dis
tanc
e
wa
s
r
e
c
or
de
d.
I
n
de
tail,
13
s
teps
we
r
e
c
ode
d,
including
:
−
P
a
r
ti
c
ipant
wa
s
una
ble
to
e
nte
r
the
tes
t
c
ha
mber
wi
ll
ge
t
0
point
s
.
−
P
a
r
ti
c
ipant
s
topped
5
m
f
r
om
the
s
pider
c
ontaine
r
,
e
a
r
ning
1
point
.
−
P
a
r
ti
c
ipants
who
s
topped
4
m
f
r
om
the
c
ontaine
r
w
il
l
r
e
c
e
ive
2
point
s
.
−
P
a
r
ti
c
ipants
who
s
topped
3
m
f
r
om
the
c
ontaine
r
w
il
l
r
e
c
e
ive
3
point
s
.
−
P
a
r
ti
c
ipants
who
s
topped
2
m
f
r
om
the
c
ontaine
r
w
il
l
r
e
c
e
ive
4
point
s
.
−
P
a
r
ti
c
ipant
s
tops
1
m
f
r
o
m
the
c
ontaine
r
,
e
a
r
ns
5
p
oint
s
.
−
P
a
r
ti
c
ipant
s
tops
c
los
e
to
the
table
with
the
c
ontain
e
r
,
e
a
r
ns
6
point
s
.
−
P
a
r
ti
c
ipant
who
is
a
ble
to
touch
the
c
ontaine
r
e
a
r
ns
7
point
s
.
−
P
a
r
ti
c
ipant
who
is
a
ble
to
ope
n
the
li
d
will
r
e
c
e
ive
8
point
s
.
−
P
a
r
ti
c
ipant
who
is
a
ble
to
put
their
ha
nd
int
o
the
c
o
ntaine
r
will
r
e
c
e
ive
a
point
9
.
−
P
a
r
ti
c
ipant
who
is
a
ble
to
touch
the
s
pider
wi
th
on
e
f
inger
wi
ll
r
e
c
e
ive
10
point
s
.
−
P
a
r
ti
c
ipant
who
is
a
ble
to
hold
the
s
pider
f
or
les
s
than
20
s
e
c
onds
will
r
e
c
e
ive
a
point
11
.
−
P
a
r
ti
c
ipants
a
ble
to
hold
the
s
pider
f
or
a
t
lea
s
t
20
s
e
c
onds
will
ge
t
point
12.
T
his
tes
t
wa
s
c
onduc
ted
be
f
or
e
a
nd
a
f
ter
the
s
ubjec
ts
we
r
e
given
the
tr
e
a
tm
e
nt
,
a
nd
the
di
f
f
e
r
e
nc
e
be
twe
e
n
be
f
or
e
a
nd
a
f
ter
the
t
r
e
a
tm
e
nt
wa
s
us
e
d
a
s
the
pr
im
a
r
y
outcome
mea
s
ur
e
.
T
his
a
s
s
e
s
s
ment
l
a
s
ted
a
n
a
ve
r
a
ge
of
45
mi
nutes
,
a
nd
pa
r
ti
c
ipants
who
ob
t
a
ined
a
tot
a
l
o
f
14
-
50
point
s
pa
s
s
e
d
the
s
c
r
e
e
nin
g
s
tage
.
P
a
r
ti
c
ipants
who
s
c
or
e
d
les
s
than
14
or
mor
e
than
50
we
r
e
a
s
ke
d
to
a
tt
e
nd
the
a
s
s
e
s
s
ment
a
t
the
De
pa
r
tm
e
nt
of
c
li
nica
l
ps
yc
hology
a
nd
ps
yc
hother
a
py
f
or
a
c
on
s
ult
a
ti
on.
P
a
r
ti
c
ipants
who
pa
s
s
e
d
the
s
c
r
e
e
ning
s
tage
we
r
e
tr
a
ined
a
t
home
f
or
one
we
e
k
a
nd
r
e
tu
r
ne
d
f
o
r
bio
-
s
ignal
r
e
c
or
dings
dur
ing
the
s
ti
mul
us
.
E
DA
,
E
C
G,
a
nd
R
S
P
s
ignals
we
r
e
r
e
c
or
de
d
us
ing
a
B
I
T
a
li
no
bio
-
s
ignal
mea
s
ur
e
ment
de
vice
with
a
s
a
mpl
ing
f
r
e
que
nc
y
s
e
t
to
100
Hz
pe
r
c
ha
nne
l
a
nd
10
-
bit
r
e
s
olut
ion,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
:
337
-
34
8
340
whic
h
is
s
uf
f
icie
nt
f
o
r
E
C
G
r
hythm
moni
tor
ing
.
T
hr
e
e
e
lec
tr
ode
s
we
r
e
plac
e
d
a
c
c
or
ding
to
the
s
tanda
r
d
lea
d
I
I
c
onf
igu
r
a
ti
on.
F
or
E
DA
mea
s
ur
e
ments
,
two
e
lec
tr
ode
s
we
r
e
plac
e
d
on
the
p
r
oxim
a
l
pa
r
t
of
the
pa
l
m
of
the
pa
r
ti
c
ipant
’
s
non
-
domi
na
nt
ha
nd.
T
he
e
lec
tr
ode
s
us
e
d
we
r
e
ge
l
-
b
a
s
e
d
di
s
pos
a
ble
Ag/Ag
C
l
e
lec
tr
o
de
s
.
T
he
B
I
T
a
li
no
R
S
P
s
e
ns
or
wa
s
a
n
a
djus
table
c
he
s
t
be
lt
with
a
n
e
las
ti
c
a
ted
c
lamp
a
nd
a
n
int
e
gr
a
ted
piez
oe
lec
tr
ic
s
e
ns
or
.
HR
V
biof
e
e
dba
c
k
wa
s
obtaine
d
us
ing
a
R
hythm
+
HR
moni
tor
ing
a
r
mband
with
a
s
a
mpl
ing
f
r
e
que
nc
y
of
1
Hz
.
T
his
a
r
mband
wa
s
plac
e
d
be
low
the
e
lb
ow
of
the
pa
r
ti
c
ipant
’
s
non
-
domi
na
nt
a
r
m.
All
s
e
ns
or
da
ta
wa
s
tr
a
ns
mi
tt
e
d
wir
e
les
s
ly
to
a
pe
r
s
ona
l
c
omput
e
r
(
P
C
)
v
ia
a
s
mar
tphone
(
Ne
xus
5)
us
ing
B
luetooth
low
e
ne
r
gy
(
B
L
E
)
a
nd
wir
e
les
s
loca
l
a
r
e
a
ne
twor
k
(
W
L
AN
)
int
e
r
f
a
c
e
s
.
At
the
da
ta
c
oll
e
c
ti
on
s
tage
,
pa
r
ti
c
ipants
we
r
e
r
e
i
ntr
oduc
e
d
to
the
s
ti
mul
a
ti
on
method.
T
his
a
im
s
to
give
pa
r
ti
c
ipants
a
n
idea
o
f
whe
n
they
will
be
e
x
pos
e
d
to
the
f
e
a
r
e
d
objec
t
a
nd
he
lp
pa
r
ti
c
ipants
t
o
de
c
ide
a
ga
in
a
bout
the
s
ti
mul
a
ti
on
to
be
r
e
c
e
ived.
E
a
c
h
da
ta
c
oll
e
c
ti
on
wa
s
take
n
with
a
n
a
ve
r
a
ge
du
r
a
ti
on
of
35
mi
nutes
dur
ing
the
pe
r
iod
f
r
om
2
p.
m
.
to
6
p.
m
.
to
c
ontr
ol
c
or
ti
s
ol
leve
ls
that
c
a
n
a
f
f
e
c
t
the
tappi
ng
va
lue
[
34]
,
whe
r
e
the
s
ubjec
t
will
wa
tch
a
s
pider
video
a
s
a
s
ti
mul
us
f
or
two
s
e
s
s
ions
dis
playe
d
on
a
P
C
.
E
a
c
h
s
e
s
s
ion
s
tar
ted
with
a
que
s
ti
on
a
bout
wha
t
would
a
ppe
a
r
in
the
f
oll
owing
video
c
li
p.
T
he
r
e
we
r
e
16
dif
f
e
r
e
nt
video
c
li
ps
of
1
m
inut
e
e
a
c
h,
whic
h
we
r
e
take
n
f
r
om
T
V
doc
umenta
r
ies
a
nd
s
howe
d
de
tails
a
bout
s
pider
s
.
T
he
s
e
16
c
li
ps
we
r
e
divi
de
d
int
o
two
s
e
s
s
ions
.
I
n
s
e
s
s
ion
1,
e
ight
videos
we
r
e
s
hown
with
low
in
t
e
ns
it
y
of
s
pider
objec
t
a
ppe
a
r
a
nc
e
a
nd
s
low
moveme
nt.
T
he
da
tas
e
t
include
d
inf
o
r
mation
to
labe
l
the
da
t
a
ba
s
e
d
on
the
leve
l
of
a
nxiety
a
nd
the
tr
e
a
tm
e
nt
given
to
the
s
ubjec
t.
HR
V/E
DA
labe
li
ng
is
done
ba
s
e
d
on
c
li
ps
[
20]
.
I
n
thi
s
s
tudy,
c
las
s
if
ica
ti
on
will
be
c
a
r
r
ied
out
in
2
c
las
s
e
s
(
high
a
nd
low
)
a
nd
thr
e
e
c
l
a
s
s
e
s
(
high,
medium
,
a
nd
low)
.
2
.
2
.
F
e
at
u
r
e
e
xt
r
ac
t
ion
Da
ta
pr
e
pr
oc
e
s
s
ing
c
ons
i
s
ts
of
s
e
ve
r
a
l
s
teps
,
inc
ludi
ng
c
lea
ning,
f
il
ter
ing,
nor
maliza
ti
on,
f
e
a
tur
e
e
xtr
a
c
ti
on,
windowing,
int
e
g
r
a
ti
on,
a
nd
labe
li
ng.
C
lea
ning
a
nd
f
il
ter
ing
we
r
e
a
ppli
e
d
to
the
r
a
w
da
ta.
T
he
pr
oc
e
s
s
ing
a
nd
f
e
a
tur
e
e
xtr
a
c
ti
on
s
tage
s
we
r
e
pe
r
f
or
med
with
P
ython.
A
tot
a
l
o
f
24
f
e
a
tur
e
s
we
r
e
e
xtr
a
c
ted
to
a
na
lyze
the
s
ignal
in
the
ti
me
domain
f
o
r
e
a
c
h
bio
f
e
e
dba
c
k
,
a
s
s
hown
in
T
a
ble
1
.
Ana
lys
e
s
in
the
f
r
e
que
nc
y
domain
we
r
e
not
take
n
int
o
a
c
c
ount
be
c
a
us
e
the
y
us
e
d
s
hor
t
windowing
that
c
ould
not
dis
play
a
c
c
ur
a
te
s
pe
c
tr
a
l
a
na
lys
e
s
.
T
a
ble
1
.
F
e
a
tur
e
e
xt
r
a
c
ti
on
in
the
ti
me
domain
E
C
G
E
D
A
R
S
P
1.
H
e
a
r
t
be
a
t
nor
ma
li
z
e
d me
a
n
1.
E
da
nor
ma
li
z
e
d m
ean
1.
B
r
e
a
th
r
a
te
nor
ma
li
z
e
d m
ean
2.
S
ta
nda
r
d
de
vi
a
ti
on
2.
S
ta
nda
r
d de
vi
a
ti
on
2.
S
ta
nda
r
d de
vi
a
ti
on
3.
M
e
a
n of
t
he
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
nor
ma
li
z
e
d f
ir
s
t
di
f
f
e
r
e
nc
e
s
(
N
F
D
)
3.
M
e
a
n of
t
he
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
N
F
D
3.
M
e
a
n of
t
he
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
N
F
D
4.
M
e
a
n of
th
e
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
nor
ma
li
z
e
d s
e
c
ond dif
f
e
r
e
nc
e
s
(
N
S
D
)
4.
M
e
a
n of
t
he
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
nor
ma
li
z
e
d s
e
c
ond dif
f
e
r
e
nc
e
s
(
N
S
D
)
4.
M
e
a
n of
t
he
a
bs
ol
ut
e
v
a
lu
e
s
of
t
he
nor
ma
li
z
e
d s
e
c
ond dif
f
e
r
e
nc
e
s
(
N
S
D
)
5.
H
e
a
r
t
r
a
te
va
r
ia
bi
li
ty
(
H
R
V
)
5.
M
e
a
n
m
a
g
ni
t
ud
e
of
or
i
e
nt
a
ti
o
n
r
e
s
po
n
s
e
(
mm
O
R
)
5.
B
r
e
a
th
in
g
r
a
te
va
r
ia
bi
li
ty
(
B
R
V
)
6.
A
ve
r
a
ge
of
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
(
a
vN
N
)
6.
M
e
a
n
dur
a
ti
on of
or
ie
nt
a
ti
on r
e
s
pons
e
(
mdOR
)
6.
A
ve
r
a
ge
of
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
(
a
vN
N
)
7.
S
ta
nda
r
d de
vi
a
ti
on
of
nor
ma
l
-
to
-
no
r
ma
l
in
te
r
va
ls
(
s
dN
N
)
7.
S
ta
nda
r
d
de
vi
a
ti
on of
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
(
s
dN
N
)
8.
R
oot
me
a
n s
qua
r
e
of
s
u
c
c
e
s
s
iv
e
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
l
di
f
f
e
r
e
nc
e
(
R
ms
s
d)
9.
S
uc
c
e
s
s
iv
e
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
th
a
t
di
f
f
e
r
by mor
e
t
ha
n 50 ms
(
N
N
50)
10.
P
r
opor
ti
on of
N
N
50 divi
de
d by the
t
ot
a
l
numbe
r
of
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
(
pN
N
50)
11.
P
r
opor
ti
on of
N
N
20 divi
de
d by the
t
ot
a
l
numbe
r
of
nor
ma
l
-
to
-
nor
ma
l
in
te
r
va
ls
(
pN
N
20)
I
n
E
C
G
s
ignals
,
the
f
il
ter
ing
p
r
oc
e
s
s
will
be
c
a
r
r
i
e
d
out
with
the
f
r
e
que
nc
y
domain.
T
he
r
e
f
or
e
,
it
is
ne
c
e
s
s
a
r
y
to
c
onve
r
t
da
ta
f
r
om
the
ti
me
domain
to
the
f
r
e
que
nc
y
domain
us
ing
the
dis
c
r
e
te
F
our
ier
tr
a
ns
f
or
m
.
I
n
thi
s
da
ta
p
r
oc
e
s
s
ing,
the
f
a
s
t
F
our
ier
t
r
a
ns
f
or
m
(
F
F
T
)
a
lgor
it
hm
is
us
e
d
to
c
a
lcula
te
the
dis
c
r
e
te
F
our
ier
tr
a
ns
f
or
m
quickly,
whe
r
e
the
a
dva
ntage
of
us
ing
th
e
F
F
T
is
that
it
c
a
n
s
tor
e
s
ignal
in
f
or
mation
that
a
ll
ows
f
or
making
a
mor
e
s
tr
a
ight
f
o
r
wa
r
d
inver
s
e
tr
a
ns
f
or
mation.
F
il
te
r
ing
is
pe
r
f
o
r
med
be
c
a
us
e
E
C
G
s
ignals
of
ten
c
ontain
nois
e
.
T
his
a
lgor
it
hm
a
ppli
e
s
a
lowpa
s
s
f
il
ter
to
r
e
move
low
-
f
r
e
que
nc
y
nois
e
.
T
he
n
,
the
s
ignal
is
f
il
ter
e
d
us
ing
a
ba
ndpa
s
s
f
il
ter
to
highl
ight
the
QR
S
c
ompl
e
x.
T
he
f
il
ter
ing
us
e
s
a
ba
ndpa
s
s
f
il
ter
a
t
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
M
e
as
ur
ing
anx
iety
lev
e
l
on
phobia
us
ing
e
lec
tr
od
e
r
mal
ac
ti
v
it
y
,
e
lec
tr
oc
ar
diogr
am
and…
(
K
hus
nul
A
in
)
341
f
r
e
que
nc
y
of
5
-
12Hz
.
T
he
r
e
s
ult
s
of
thi
s
F
F
T
pr
oc
e
s
s
ing
will
be
inver
ted
to
obtain
the
in
f
or
mation
n
e
e
de
d
f
or
the
QR
S
c
ompl
e
x
de
tec
ti
on
p
r
oc
e
s
s
.
QR
S
c
ompl
e
x
de
tec
ti
on
is
then
pe
r
f
or
med
us
ing
the
P
a
n
-
T
omki
ns
a
lgor
it
hm
,
whe
r
e
thi
s
method
de
tec
ts
the
QR
S
s
ignal
by
pe
r
f
or
mi
ng
ba
ndpa
s
s
f
il
ter
ing
f
i
r
s
t.
I
n
th
is
a
lgor
it
hm
,
s
e
ve
r
a
l
s
tage
s
a
r
e
pe
r
f
or
med,
including
[
35]
:
−
Dif
f
e
r
e
nti
a
ti
on
whe
r
e
the
s
ignal
is
s
im
pli
f
ied
to
obtain
inf
or
mation
on
the
width
o
f
the
QR
S
c
om
plex.
T
his
method
us
e
s
f
ive
-
point
dif
f
e
r
e
nc
ing
to
ge
t
th
e
s
lope
va
lue
of
the
QR
S
c
ompl
e
x
wa
ve
f
r
om
the
E
C
G
s
ignal.
−
S
qua
r
ing,
whe
r
e
the
s
ignal
is
s
qua
r
e
d
to
incr
e
a
s
e
the
a
mpl
it
ude
o
f
the
QR
S
wa
ve
to
obtain
only
po
s
it
ive
s
ignal
output
a
nd
da
mpen
the
pa
r
ts
that
a
r
e
not
pa
r
t
of
the
QR
S
c
ompl
e
x
.
−
M
oving
window
int
e
gr
a
ti
on
(
M
I
W
)
,
whe
r
e
the
a
lgor
it
hm
c
a
lcula
tes
the
tot
a
l
e
ne
r
gy
in
a
ti
me
r
a
n
ge
to
obtain
other
inf
o
r
mation
f
r
om
the
wa
ve
f
or
m
be
s
ides
the
s
lope
[
20
]
.
T
he
s
ignal
f
r
om
the
s
qua
r
ing
pr
oc
e
s
s
is
then
c
ombi
ne
d
in
the
M
I
W
pr
oc
e
s
s
,
whic
h
a
i
ms
to
s
im
pli
f
y
the
c
a
lcula
ti
on
of
the
QR
S
c
ompl
e
x
wi
dth.
−
T
hr
e
s
holdi
ng
s
e
pa
r
a
tes
the
s
ignal
r
e
late
d
to
the
QR
S
c
ompl
e
x
f
r
om
nois
e
a
nd
other
pa
r
ts
.
I
n
thi
s
pr
oc
e
s
s
,
the
s
ignal
is
c
las
s
if
ied
a
c
c
or
ding
to
it
s
a
mpl
it
ude
v
a
lue.
I
f
the
s
ignal
va
lue
is
0
,
then
it
is
c
las
s
if
ied
a
s
low,
a
nd
if
not,
it
is
c
las
s
if
ied
a
s
high
[
35]
.
T
he
thr
e
s
hold
va
lue
is
obtaine
d,
whic
h
is
us
e
d
a
s
the
va
lue
of
the
QR
S
width.
F
r
om
thi
s
s
tage
,
the
R
R
int
e
r
va
l
a
nd
HR
pa
r
a
mete
r
s
c
a
n
be
obtaine
d
by
pe
r
f
or
mi
ng
pe
a
k
de
tec
ti
on.
−
P
e
a
k
de
tec
ti
on
,
whe
r
e
s
ignal
pe
a
ks
a
r
e
identif
ied
to
mar
k
the
on
-
s
e
t
a
nd
of
f
-
s
e
t
of
the
QR
S
c
ompl
e
x.
T
he
a
ve
r
a
ge
nor
malize
d
he
a
r
t
r
a
te
(
HR
)
va
lue
is
obtaine
d
by
c
a
lcula
ti
ng
the
a
ve
r
a
ge
HR
va
lue
dur
in
g
the
r
e
s
ti
ng
pha
s
e
of
the
s
ti
mul
a
ti
on
s
e
s
s
ion.
I
n
the
R
S
P
s
ignal,
obtaine
d
f
r
om
the
s
e
ns
or
-
s
hif
t
va
lue
in
pe
r
c
e
ntage
,
the
B
R
c
a
lcula
ti
on
is
done
by
c
ounti
ng
the
number
of
ti
mes
the
c
he
s
t
e
xpa
nds
.
I
n
pr
oc
e
s
s
ing
thi
s
s
ignal,
it
ne
e
ds
to
be
c
onve
r
t
e
d
f
r
om
pe
r
c
e
ntage
f
or
m
to
vol
tage
f
o
r
m.
F
il
te
r
ing
is
pe
r
f
or
med
us
ing
a
butt
e
r
ba
ndpa
s
s
f
il
ter
with
a
f
r
e
q
ue
nc
y
of
0.
1
-
24
Hz
,
whic
h
is
e
quivale
nt
to
6
-
24
b
r
e
a
ths
p
e
r
mi
nute
to
r
e
move
o
f
f
s
e
ts
a
nd
nois
e
.
I
n
a
ddit
io
n,
pe
a
ks
we
r
e
identif
ied
us
ing
the
‘
ndpe
a
ks
’
a
lgor
it
hm
f
unc
ti
on.
On
the
E
DA
s
ignal,
f
i
lt
e
r
ing
is
pe
r
f
or
med
us
ing
a
s
e
c
ond
-
or
de
r
B
utt
e
r
wor
th
low
-
pa
s
s
f
il
ter
with
a
c
ut
-
of
f
f
r
e
que
nc
y
of
1
.
5
Hz
f
or
f
e
a
tur
e
e
xtr
a
c
ti
on.
T
he
n,
a
high
-
pa
s
s
f
il
ter
with
a
c
ut
-
of
f
f
r
e
que
nc
y
o
f
0.
05
Hz
wa
s
a
ppli
e
d
to
ge
ne
r
a
te
a
pha
s
ic
s
ignal,
the
f
luc
tuations
in
s
kin
c
onduc
tanc
e
that
oc
c
ur
in
r
e
s
po
ns
e
to
a
s
ti
mul
us
.
T
he
n
the
on
-
s
e
t,
o
f
f
-
s
e
t,
a
nd
pe
a
k
a
r
e
de
tec
ted
with
a
thr
e
s
hold
o
f
0.
03
S
ieme
ns
whe
r
e
the
number
of
r
e
s
pons
e
s
is
the
numbe
r
of
pe
a
ks
de
tec
ted,
th
e
mea
n
magnitude
o
f
r
e
s
pons
e
(
mm
OR
)
is
the
d
if
f
e
r
e
nc
e
be
twe
e
n
the
pe
a
k
magnitude
a
nd
it
s
on
-
s
e
t,
a
nd
the
mea
n
dur
a
ti
on
of
r
e
s
pons
e
(
mdOR
)
is
the
ti
me
d
if
f
e
r
e
nc
e
be
twe
e
n
on
-
s
e
t
a
nd
of
f
-
s
e
t.
3.
RE
S
UL
T
S
I
n
the
da
ta
c
oll
e
c
ti
on
pr
oc
e
s
s
,
57
r
a
w
E
C
G,
E
DA
,
a
nd
R
S
P
da
ta
we
r
e
obtaine
d
f
r
om
60
s
ubjec
ts
a
ge
d
be
twe
e
n
18
-
40
ye
a
r
s
.
T
he
da
ta
obtaine
d
is
in
the
f
or
m
o
f
numer
ica
l
da
ta,
whic
h
is
then
us
e
d
to
f
a
c
il
it
a
te
pr
oc
e
s
s
ing.
F
igur
e
1
(
a
)
is
a
n
im
a
ge
of
r
a
w
bios
ign
a
l
da
ta
on
one
o
f
the
s
ubjec
ts
take
n
e
ve
r
y
1/60
s
e
c
onds
.
I
n
E
C
G
da
ta,
the
va
lue
of
volt
a
ge
or
e
lec
tr
ic
poten
ti
a
l
in
the
he
a
r
t
with
mi
ll
ivol
t
unit
s
ha
s
a
va
lue
r
a
nge
of
-
1.
5
to
1.
5
mv;
in
F
igu
r
e
1(
b
)
E
DA
da
ta
c
a
n
be
s
e
e
n
the
va
lue
of
s
kin
c
onduc
tanc
e
with
mi
c
r
os
iem
e
ns
(
µ
)
unit
s
with
a
va
lue
r
a
nge
of
-
12.
6
to
41
µ
,
a
nd
i
n
F
igu
r
e
1
(
c
)
R
S
P
da
ta,
the
va
lue
o
f
pr
e
s
s
ur
e
c
h
a
nge
s
or
vibr
a
ti
ons
that
oc
c
ur
du
r
ing
b
r
e
a
thi
ng
a
nd
c
onve
r
t
them
int
o
e
lec
tr
ica
l
s
ignals
that
c
a
n
be
r
e
c
or
de
d
with
a
va
lue
r
a
nge
of
-
50%
to
50
%
.
Da
ta
f
r
om
the
E
C
G
s
ignal
is
s
ubjec
ted
to
a
n
F
F
T
tr
a
ns
f
or
mation
pr
oc
e
s
s
to
c
onve
r
t
the
s
ignal
in
th
e
ti
me
domain
to
the
f
r
e
que
nc
y
domain.
T
he
n
,
f
il
t
e
r
ing
is
pe
r
f
or
med
us
ing
a
ba
ndpa
s
s
f
il
ter
with
a
c
ut
-
of
f
va
lue
of
5
to
12
Hz
.
T
he
f
il
ter
ing
p
r
oc
e
s
s
us
e
s
the
‘
f
ir
win
’
f
unc
ti
on
a
nd
then
a
ppli
e
s
the
f
il
ter
to
the
r
a
w
E
C
G
da
ta
us
ing
the
‘
lf
i
ter
’
f
unc
ti
on
.
Af
ter
f
il
te
r
ing,
t
he
s
ignal
is
inve
r
s
e
d
int
o
the
t
im
e
domain;
thi
s
is
done
be
c
a
us
e
the
P
a
n
-
T
ompki
ns
a
lgor
it
hm
f
oc
us
e
s
on
a
na
lyzing
ti
me
-
domain
s
ignals
to
de
tec
t
QR
S
c
omp
lexe
s
in
E
C
G
s
ignals
.
Ne
xt,
we
de
tec
t
the
QR
S
c
ompl
e
x
us
ing
the
P
a
n
-
T
omki
ns
a
lgor
i
thm
.
I
n
thi
s
a
lgo
r
i
thm
,
the
dif
f
e
r
e
nti
a
ti
on
a
s
s
hown
in
F
igur
e
2
a
nd
s
qua
r
in
g
s
tage
s
a
r
e
pe
r
f
or
med
on
the
f
il
te
r
e
d
s
ignal,
w
he
r
e
the
a
mpl
it
ude
of
the
QR
S
s
ignal
pha
s
e
is
incr
e
a
s
e
d
a
nd
da
mpens
the
pa
r
ts
that
a
r
e
no
t
include
d
in
t
he
QR
S
c
ompl
e
x
a
s
s
hown
in
F
igur
e
3
(
a
)
.
M
oving
window
int
e
gr
a
ti
on
to
c
a
lcula
te
the
tot
a
l
e
ne
r
gy
withi
n
10
s
e
c
onds
a
s
s
hown
in
F
igur
e
3
(
b
)
.
T
hr
e
s
holdi
ng
is
pe
r
f
or
med
to
de
ter
mi
ne
the
s
ignal
a
s
s
oc
iate
d
with
the
QR
S
c
ompl
e
x
a
nd
s
e
pa
r
a
te
other
s
ignals
that
e
xc
e
e
d
the
thr
e
s
hold
va
lue
,
including
nois
e
,
whe
r
e
a
thr
e
s
holdi
ng
va
lue
o
f
0.
01
is
ob
t
a
ined
a
s
s
hown
in
F
igur
e
4
(
a
)
.
F
r
om
the
de
ter
mi
na
ti
on
o
f
t
he
thr
e
s
hold,
pe
a
k
de
tec
ti
on
c
a
n
be
pe
r
f
or
med
on
t
he
s
ignal
whe
r
e
the
pe
a
k
is
de
tec
ted
to
mar
k
the
on
-
s
e
t
a
nd
of
f
-
s
e
t
a
s
s
hown
in
F
igur
e
4
(
b
)
.
T
he
n,
the
R
S
P
da
ta
is
int
e
gr
a
ted
with
E
C
G
pr
oc
e
s
s
ing
da
ta
va
lues
,
a
nd
f
e
a
tur
e
e
xtr
a
c
ti
on
is
pe
r
f
or
med.
T
he
s
ize
of
E
C
G
da
ta
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
:
337
-
34
8
342
e
xtr
a
c
ti
on
f
e
a
tur
e
s
is
5652
×
11
f
e
a
tur
e
s
f
o
r
two
c
l
a
s
s
e
s
a
nd
7461
×
11
f
e
a
tur
e
s
f
or
th
r
e
e
c
las
s
e
s
.
F
e
a
tur
e
s
that
a
r
e
us
e
d
include
Nme
a
n,
s
td,
NFD
,
NSD,
HR
V,
a
v
NN
,
a
dNN
,
r
M
S
DD
,
NN
50,
pNN
50
,
a
nd
pNN
20
a
s
s
hown
in
T
a
ble
1.
T
he
us
e
of
thes
e
f
e
a
tur
e
s
is
ba
s
e
d
on
th
e
pur
pos
e
of
pr
e
pr
oc
e
s
s
ing,
whic
h
is
to
obtain
HR
V
va
lues
f
r
om
E
C
G
s
ignals
.
(
a
)
(
b)
(
c
)
F
igur
e
1.
B
ios
ignal
plot
s
(
a
)
E
C
G
s
ignal,
(
b
)
E
DA
s
ignal,
a
nd
(
c
)
R
S
P
s
ignal
F
igur
e
2
.
E
C
G
s
ignal
a
f
ter
d
if
f
e
r
e
nti
a
ti
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
M
e
as
ur
ing
anx
iety
lev
e
l
on
phobia
us
ing
e
lec
tr
od
e
r
mal
ac
ti
v
it
y
,
e
lec
tr
oc
ar
diogr
am
and…
(
K
hus
nul
A
in
)
343
(
a
)
(
b)
F
igur
e
3.
E
C
G
s
ignal
a
f
ter
(
a
)
s
qua
r
ing
a
nd
(
b
)
mo
ving
window
int
e
gr
a
ti
on
(
a
)
(
b)
F
igur
e
4.
E
C
G
s
ignal
a
f
ter
(
a
)
thr
e
s
holdi
ng
is
pe
r
f
o
r
med
a
nd
(
b
)
E
C
G
pe
a
k
de
tec
ti
on
T
he
E
DA
da
ta
is
tr
a
ns
f
or
med
f
r
om
the
ti
me
dom
a
in
to
the
f
r
e
que
nc
y
domain
us
ing
the
f
a
s
t
F
our
i
e
r
tr
a
ns
f
or
m,
whic
h
is
a
n
e
f
f
icie
nt
method
f
or
s
olvi
ng
the
dis
c
r
e
te
F
our
ie
r
t
r
a
ns
f
or
m
that
is
wide
ly
us
e
d
f
or
s
ignal
a
na
lys
is
pur
po
s
e
s
s
uc
h
a
s
f
il
ter
ing
a
nd
s
p
e
c
tr
um
a
na
lys
is
.
F
il
ter
ing
wa
s
pe
r
f
or
med
us
ing
a
lowpa
s
s
f
il
ter
with
a
c
ut
-
of
f
va
lue
of
10
Hz
a
nd
7
th
or
de
r
t
o
r
e
move
nois
e
.
T
he
E
DA
f
i
lt
e
r
ing
da
ta
wa
s
c
onve
r
ted
int
o
pha
s
ic
s
ignal
s
to
obtain
S
C
R
da
ta
a
nd
s
kin
c
onduc
tanc
e
da
ta
inf
or
mation
whe
n
ther
e
wa
s
a
s
im
ulatio
n.
F
r
om
the
S
C
R
s
ignal,
f
e
a
tur
e
e
xtr
a
c
ti
on
is
pe
r
f
or
med.
T
he
s
ize
of
E
DA
da
ta
e
xtr
a
c
ti
on
f
e
a
tur
e
s
is
the
s
a
me
a
s
E
C
G
da
ta,
whic
h
us
e
s
s
ix
types
o
f
f
e
a
tur
e
s
,
including
Nme
a
n,
s
td,
NFD,
a
nd
NSD
.
I
n
thi
s
p
r
oc
e
s
s
,
by
c
a
lcula
ti
ng
the
va
lue
of
o
f
f
-
s
e
t
a
nd
on
-
s
e
t
pe
a
ks
,
the
mdOR
a
nd
mm
OR
va
lues
a
r
e
obtaine
d.
T
he
mdOR
va
lue
will
de
s
c
r
ibe
the
dur
a
ti
on
of
the
s
ubjec
t
’
s
r
e
s
pons
e
t
o
the
s
ti
mul
us
.
At
the
s
a
me
ti
me
,
m
mOR
r
e
pr
e
s
e
nts
the
magnitude
va
lue,
whic
h
de
s
c
r
ibes
how
much
r
e
s
pons
e
the
s
ubjec
t
gives
to
the
s
ti
mul
us
pr
ov
ided.
S
VM
is
a
lea
r
ning
a
lgor
it
hm
that
is
ve
r
y
us
e
f
ul
i
n
da
ta
c
las
s
if
ica
ti
on
a
nd
s
e
pa
r
a
ti
on.
One
of
the
k
e
y
c
omponents
of
S
VM
is
the
ke
r
ne
l,
whic
h
t
r
a
ns
f
or
ms
the
da
ta
int
o
higher
dim
e
ns
ions
s
o
that
it
c
a
n
be
s
e
pa
r
a
ted
li
ne
a
r
ly
or
non
-
li
ne
a
r
ly,
de
pe
nding
on
t
he
type
of
ke
r
ne
l
us
e
d.
T
h
is
c
las
s
if
ica
ti
on
is
divi
de
d
int
o
two
types
,
na
mely
two
c
las
s
e
s
(
high
a
nd
low
)
a
nd
thr
e
e
c
las
s
e
s
(
low,
medium,
a
nd
high)
.
I
t
us
e
s
va
r
i
a
ti
ons
of
s
e
ve
r
a
l
ke
r
ne
ls
,
na
mely
R
B
F
,
li
ne
a
r
,
polynom
ial,
a
nd
s
igm
oid.
T
he
2
-
c
las
s
c
las
s
if
ica
ti
on
,
T
a
ble
2
s
hows
the
opti
mal
c
las
s
if
ica
ti
on
va
lue
us
ing
the
polynom
ial
ke
r
ne
l.
T
his
ke
r
ne
l
pr
ovides
opti
mal
r
e
s
ult
s
wit
h
100%
a
c
c
ur
a
c
y
on
both
da
ta
s
e
ts
.
T
he
dis
tr
ibut
ion
of
a
c
tual
da
ta
in
e
a
c
h
c
las
s
on
the
c
onf
us
ion
matr
i
x
of
the
polynom
ial
ke
r
ne
l
is
in
a
c
c
or
da
nc
e
with
the
pr
e
dic
ti
on.
T
he
r
e
f
or
e
,
thi
s
ke
r
ne
l
ha
s
a
n
a
c
c
ur
a
c
y
va
lue
o
f
100%
.
I
n
the
a
na
lys
is
of
the
3
-
c
las
s
c
las
s
if
ica
ti
on
us
ing
dif
f
e
r
e
nt
types
of
ke
r
ne
ls
,
ther
e
we
r
e
va
r
iations
in
th
e
r
e
s
ult
s
that
s
howe
d
dif
f
e
r
e
nc
e
s
in
the
a
bil
it
y
of
e
a
c
h
ke
r
ne
l
to
c
las
s
if
y
the
da
ta
e
f
f
e
c
ti
ve
ly
a
s
s
hown
in
T
a
ble
3.
T
he
polynom
ial
ke
r
ne
l
wa
s
f
ound
to
be
th
e
opti
mal
ke
r
ne
l
in
thi
s
c
las
s
if
ica
ti
on.
T
his
ke
r
ne
l
a
c
hieve
d
100%
a
c
c
ur
a
c
y
on
both
da
ta
s
e
ts
,
with
pr
e
c
is
ion,
r
e
c
a
ll
,
a
nd
F
1
-
s
c
or
e
r
e
a
c
hing
100%
.
T
he
s
e
r
e
s
ult
s
s
how
that
the
c
las
s
if
ica
ti
on
pr
oc
e
s
s
c
a
n
be
pe
r
f
or
med
we
ll
by
the
model
us
ing
the
polynom
ial
ke
r
ne
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
:
337
-
34
8
344
T
a
ble
2.
T
he
2
-
c
las
s
c
las
s
if
ica
ti
on
r
e
s
ult
K
e
r
ne
l
A
c
c
ur
a
c
y
P
r
e
c
is
io
n
R
e
c
a
ll
F
1 S
c
or
e
T
r
a
in
T
e
s
t
P
ol
ynomi
a
l
100.0%
100.0%
100.0%
100.0%
100.0%
L
in
e
a
r
71.9%
86.4%
87.0%
85.0%
85.0%
RBF
36.5%
66.0%
66.0%
65.0%
66.0%
S
ig
moi
d
58.7%
57.2%
60.0%
59.0%
57.0%
T
a
ble
3.
T
he
3
-
c
las
s
c
las
s
if
ica
ti
on
r
e
s
ult
K
e
r
ne
l
A
c
c
ur
a
c
y
P
r
e
c
is
io
n
R
e
c
a
ll
F
1 S
c
or
e
T
r
a
in
T
e
s
t
P
ol
ynomi
a
l
100
.0
%
100
.0
%
100
.0
%
100
.0
%
100
.0
%
L
in
e
a
r
58
.
7%
85
.
9%
84
.0
%
82
.0
%
83
.0
%
RBF
48
.
5%
67
.
7%
59
.0
%
60
.0
%
59
.0
%
S
ig
moi
d
22
.
3%
27
.
4%
31
.0
%
31
.0
%
30
.0
%
T
o
de
ter
mi
ne
the
type
of
f
e
a
tur
e
e
xt
r
a
c
ti
on
that
i
s
a
ppr
opr
iate
f
or
the
s
ignal
us
e
d,
the
mos
t
opti
mal
f
e
a
tur
e
s
e
lec
ti
on
is
de
ter
mi
ne
d
a
f
ter
c
las
s
if
ica
ti
on.
Due
to
the
s
hor
tcomings
of
S
VM
,
whic
h
c
a
nnot
pr
oduc
e
a
c
c
ur
a
te
pr
e
dictions
whe
n
it
ha
s
many
ir
r
e
leva
nt
f
e
a
tur
e
s
,
not
a
ll
f
e
a
tur
e
s
a
r
e
us
e
d
in
the
modeling
pr
oc
e
s
s
.
T
his
c
a
n
be
ove
r
c
ome
by
the
f
e
a
tu
r
e
s
e
lec
ti
on
method.
T
his
f
e
a
tu
r
e
s
e
lec
ti
on
us
e
s
a
r
a
ndom
f
or
e
s
t
model
with
the
r
e
c
ur
s
ive
f
e
a
tur
e
e
li
m
ination
(
R
F
E
)
a
lg
or
it
hm
—
f
e
a
tur
e
s
e
lec
ti
on
modul
e
.
T
he
n,
a
pply
R
F
E
wi
th
r
a
ndom
f
or
e
s
t
to
s
or
t
the
f
e
a
tur
e
s
f
r
om
the
mos
t
im
por
tant.
T
his
a
s
s
e
s
s
ment
pr
oc
e
s
s
is
r
e
pe
a
ted
unti
l
the
o
r
d
e
r
dis
playe
d
doe
s
not
c
ha
nge
.
I
n
thi
s
s
top
c
r
it
e
r
i
on
c
ondit
ion,
the
pe
r
f
or
manc
e
or
de
r
of
e
a
c
h
f
e
a
tur
e
will
be
s
hown
whe
r
e
the
top
f
e
a
tur
e
is
c
ons
ider
e
d
the
be
s
t
f
e
a
tur
e
.
T
he
higher
the
c
oe
f
f
icie
nt
va
lue
,
the
be
t
t
e
r
the
r
a
nking
a
nd
the
mor
e
li
ke
ly
to
be
s
e
lec
ted.
T
his
is
done
to
e
xplor
e
a
nd
wa
nt
to
know
whic
h
f
e
a
tur
e
s
a
r
e
the
mos
t
pr
omi
ne
nt
a
nd
domi
na
nt.
T
his
r
a
nke
d
f
e
a
tur
e
method
is
highl
y
de
pe
nde
nt
on
the
model
a
lgor
it
h
m
us
e
d.
I
f
the
model
us
e
d
is
not
a
c
c
ur
a
te,
the
R
F
E
p
r
oc
e
s
s
c
a
n
a
ls
o
r
e
s
ult
in
les
s
-
than
-
opti
mal
f
e
a
tur
e
s
e
lec
t
ion.
F
r
om
the
f
e
a
tur
e
s
e
lec
ti
on
p
r
oc
e
s
s
,
the
thr
e
e
opti
mal
f
e
a
tur
e
c
ombi
na
ti
ons
obtaine
d
in
thi
s
s
tudy
a
r
e
Nme
a
n,
s
td,
NSD
,
a
nd
two
e
xt
r
a
c
ti
on
f
e
a
tu
r
e
s
i
n
E
DA
,
including
mm
OR
a
nd
mdOR
a
s
s
hown
in
F
igur
e
5.
T
he
Nme
a
n
e
xtr
a
c
ti
on
f
e
a
tur
e
r
e
pr
e
s
e
nts
the
no
r
malize
d
a
ve
r
a
ge
va
lue
of
the
da
ta.
B
y
us
ing
th
is
f
e
a
tur
e
,
we
c
a
n
ge
t
in
f
or
mation
a
bout
the
tr
e
nd
or
tende
nc
y
of
va
lues
in
the
da
tas
e
t.
T
he
us
e
of
mea
n
nor
maliza
ti
on
is
us
e
d
to
c
ompar
e
da
ta
withi
n
a
u
nif
or
m
r
a
nge
,
thus
s
im
pli
f
ying
the
a
na
lys
is
a
nd
modelli
ng
pr
oc
e
s
s
.
T
he
s
tanda
r
d
de
viation
(
s
td)
f
e
a
tu
r
e
mea
s
ur
e
s
the
s
pr
e
a
d
or
va
r
iation
of
a
da
tas
e
t.
B
y
kno
wing
the
s
tanda
r
d
de
viation,
it
c
a
n
a
s
s
e
s
s
how
f
a
r
the
da
ta
is
s
pr
e
a
d
f
r
om
the
a
ve
r
a
ge
va
lue
.
NSD
is
the
no
r
malize
d
s
tanda
r
d
de
viation
of
a
da
tas
e
t.
B
y
us
ing
NSD
,
we
c
a
n
obtain
inf
o
r
mation
a
bout
the
va
r
iabil
it
y
o
f
th
e
da
ta
in
a
unif
or
m
r
a
nge
,
thus
a
ll
owing
be
tt
e
r
c
ompar
is
on
be
twe
e
n
da
ta.
F
igur
e
5.
F
e
a
tur
e
e
xtr
a
c
ti
on
r
a
nke
d
4.
DI
S
CU
S
S
I
ON
T
o
de
ve
lop
a
c
las
s
if
ica
ti
on
model
that
c
a
n
ident
if
y
a
nxiety
leve
ls
in
pa
ti
e
nts
with
s
pider
phobia
ba
s
e
d
on
E
DA
a
nd
E
C
G
s
ignal
da
ta
us
ing
the
S
V
M
method
we
ne
e
d
to
e
va
luate
the
opti
mum
ke
r
ne
l
a
s
we
ll
a
s
the
be
s
t
f
e
a
tur
e
.
T
hus
,
it
c
ould
pr
ovide
ne
w
in
s
ight
s
int
o
the
unde
r
s
tanding
o
f
a
nxiety
leve
ls
in
pa
ti
e
nts
with
phobias
.
I
f
we
look
f
ur
ther
in
to
the
va
r
iatio
n
of
the
ke
r
ne
l
us
e
d
be
f
or
e
,
both
c
las
s
if
ica
ti
ons
(
two
-
c
las
s
a
nd
thr
e
e
-
c
las
s
)
s
howe
d
that
polynom
ials
is
the
be
s
t
ke
r
ne
ls
c
ompar
e
d
to
li
ne
a
r
,
R
B
F
a
nd
s
igm
oid.
T
he
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
M
e
as
ur
ing
anx
iety
lev
e
l
on
phobia
us
ing
e
lec
tr
od
e
r
mal
ac
ti
v
it
y
,
e
lec
tr
oc
ar
diogr
am
and…
(
K
hus
nul
A
in
)
345
polynom
ial
ke
r
ne
l
ha
s
a
de
gr
e
e
o
f
us
e
that
c
a
n
be
a
djus
ted
to
the
c
ompl
e
xit
y
of
the
da
ta
be
ing
pr
oc
e
s
s
e
d
s
o
that
it
c
a
n
incr
e
a
s
e
the
pos
s
ibi
li
ty
of
da
ta
be
ing
c
las
s
if
ied
li
ne
a
r
ly
a
nd
quickly
in
high
s
pa
ti
a
l
di
mens
ions
[
36]
.
T
his
c
a
n
be
p
r
ove
n
in
the
dis
tr
ibut
ion
of
da
ta
in
the
c
onf
us
ion
matr
ix,
whic
h
indi
c
a
tes
that
the
c
las
s
if
ier
is
f
oll
owing
the
pr
e
diction
a
nd
the
a
c
tual
da
ta.
Ho
we
ve
r
,
thi
s
pe
r
f
e
c
t
r
e
s
ult
ne
e
ds
f
ur
ther
va
li
da
ti
on
to
pr
ove
the
a
bs
e
nc
e
of
ove
r
f
it
ti
ng.
T
he
li
ne
a
r
,
R
B
F
a
nd
s
igm
oid
ke
r
ne
ls
we
r
e
not
e
f
f
e
c
ti
ve
in
c
las
s
if
ying
the
da
ta,
s
howing
their
li
mi
tations
in
ha
ndli
ng
c
ompl
e
x
p
a
tt
e
r
ns
.
T
his
s
hows
that
thi
s
ke
r
ne
l
doe
s
not
f
it
the
da
ta.
T
he
r
e
f
or
e
,
ke
r
ne
l
s
e
lec
ti
on
s
hould
be
ba
s
e
d
on
the
c
ha
r
a
c
ter
is
ti
c
s
,
s
ha
pe
or
pa
tt
e
r
n
o
f
da
ta
dis
tr
ibut
ion
.
B
e
s
ide
s
the
ke
r
ne
l,
f
e
a
tur
e
s
a
ls
o
c
ontr
ibut
e
to
the
p
e
r
f
or
manc
e
.
T
he
thr
e
e
op
ti
mum
f
e
a
tur
e
s
ba
s
e
d
on
the
F
igur
e
5
a
r
e
no
r
malize
d
mea
n,
s
tanda
r
d
de
vi
a
ti
on
a
nd
no
r
malize
d
s
e
c
ond
dif
f
e
r
e
nc
e
s
ba
s
e
d
o
n
E
C
G,
E
DA
a
nd
R
S
P
s
ignals
.
S
ome
p
r
e
vious
s
tudi
e
s
ha
ve
mentioned
e
mot
ions
a
f
f
e
c
t
he
a
r
t
r
a
te.
M
or
e
ove
r
,
br
e
a
thi
ng
c
a
n
a
ls
o
be
us
e
d
a
s
a
n
indi
c
a
ti
on
of
e
mot
ional
c
ha
nge
s
[
16]
,
[
37]
.
B
r
e
a
thi
ng
r
a
te
(
B
R
)
incr
e
a
s
e
s
with
incr
e
a
s
ing
leve
ls
o
f
s
tr
e
s
s
or
a
nxiety
whic
h
c
a
n
lea
d
to
hype
r
ve
nti
lation
[
38
]
.
E
DA
c
a
n
a
ls
o
be
us
e
d
a
s
a
biom
a
r
ke
r
of
indi
vidual
c
ha
r
a
c
ter
is
ti
c
s
in
e
mot
ional
r
e
s
pons
e
s
a
nd
a
s
a
potential
method
of
tr
e
a
ti
ng
ps
yc
hos
omatic
c
ondit
ions
thr
ough
biof
e
e
dba
c
k
tr
a
ini
ng
[
39]
.
W
he
n
ther
e
is
a
n
e
ve
nt
that
tr
igger
s
a
phobia,
e
mot
ional
c
ha
nge
s
will
a
utom
a
ti
c
a
ll
y
oc
c
ur
.
T
he
r
e
f
or
e
,
bo
th
E
DA
a
nd
E
C
G
a
r
e
phys
iol
ogica
l
s
ignals
that
c
a
n
be
us
e
d
to
de
tec
t
s
tr
e
s
s
a
nd
a
nxiety.
S
ome
s
tudi
e
s
us
e
d
E
C
G,
E
DA
a
nd
r
e
s
pir
a
tor
y
s
ignals
(
R
S
P
)
to
de
tec
t
a
nxiety
[
40]
.
He
a
r
t
r
a
te
va
r
iabili
ty
(
HR
V)
mea
s
ur
e
d
thr
ough
E
C
G
ha
s
be
e
n
us
e
d
to
a
s
s
e
s
s
a
nxiety
dis
or
de
r
s
.
I
n
pr
e
vious
r
e
late
d
r
e
s
e
a
r
c
h,
c
las
s
if
ica
ti
on
us
ing
t
he
S
VM
a
lgor
it
hm
by
Ha
ndouz
i
e
t
al.
[
41
]
,
whic
h
c
las
s
if
ies
s
pe
c
if
ic
phobia
/
s
oc
ial
phobia
in
VR
E
T
t
r
e
a
tm
e
nt
int
o
two
a
nxiety
c
las
s
e
s
with
the
holdout
va
li
da
ti
on
method
,
whe
r
e
ther
e
a
r
e
200
tr
a
ini
ng
d
a
ta
a
nd
80
tes
t
da
ta
obtaine
d
a
n
a
c
c
ur
a
c
y
va
lue
of
76%
.
I
n
a
ddit
ion,
in
the
r
e
s
e
a
r
c
h
of
I
h
mi
g
e
t
a
l.
[
20
]
c
onduc
ted
us
ing
the
s
a
me
da
tas
e
t,
a
n
a
c
c
ur
a
c
y
va
lue
o
f
74
.
4%
wa
s
obtaine
d
us
ing
the
ba
gge
d
t
r
e
e
s
method.
T
his
s
tudy
us
e
s
the
s
a
me
da
tas
e
t
with
a
va
r
ied
windowi
ng
va
lue
of
5
a
nd
10
s
e
c
a
nd
obtains
a
higher
a
c
c
ur
a
c
y
va
lue
a
t
10
-
s
e
c
windowing.
W
he
r
e
thi
s
c
las
s
if
ica
ti
on
dis
ti
nguis
he
s
a
nxiety
leve
ls
in
3
c
las
s
e
s
us
ing
the
10
-
vold
-
c
r
os
s
-
va
li
da
ti
on
method
in
thi
s
s
tudy,
whic
h
us
e
s
the
S
VM
a
lgor
it
hm
(
polynom
ial
ke
r
ne
l)
a
nd
wind
owing
of
10
s
e
c
onds
,
with
the
hold
-
out
va
li
da
ti
on
method
is
c
ons
ider
e
d
s
upe
r
ior
be
c
a
u
s
e
it
c
a
n
dis
ti
nguis
h
a
nxiety
leve
ls
in
3
c
las
s
e
s
(
‘
high,
’
‘
medium
,
’
a
n
d
‘
low
’
)
with
a
n
a
c
c
ur
a
c
y
va
lue
of
100%
,
with
pr
e
c
is
ion,
r
e
c
a
ll
a
nd
F
1
s
c
or
e
va
lues
of
100
%
.
T
his
a
c
c
ur
a
c
y
va
lue
is
the
s
a
me
a
s
the
a
c
c
ur
a
c
y
va
lue
in
the
2
-
c
las
s
c
las
s
if
ica
ti
on.
How
e
ve
r
,
the
3
-
c
las
s
c
las
s
if
ica
ti
on
is
s
ti
ll
s
upe
r
ior
c
ons
ider
ing
the
a
ddit
ional
c
las
s
s
o
that
it
c
a
n
dis
play
e
a
c
h
a
nxiety
c
las
s
mor
e
pr
e
c
is
e
ly.
F
r
om
thi
s
r
e
s
e
a
r
c
h,
it
is
known
that
the
polynom
ial
ke
r
ne
l
is
the
mos
t
opti
mal
ke
r
ne
l
f
or
the
c
las
s
if
ica
ti
on
p
r
oc
e
s
s
;
thi
s
is
known
f
r
om
T
a
ble
1
a
nd
T
a
ble
2
that
f
o
r
e
a
c
h
c
las
s
if
ica
ti
on
c
a
tegor
y,
the
polynom
ial
ke
r
ne
l
ha
s
the
highes
t
a
c
c
ur
a
c
y
va
lue
c
ompar
e
d
to
other
ke
r
ne
ls
.
5.
CONC
L
USI
ON
T
his
s
tudy
de
ve
loped
a
c
las
s
if
ica
ti
on
of
a
nxiety
leve
ls
with
E
C
G
a
nd
E
DA
bios
ignals
that
c
a
n
be
done
us
ing
the
s
uppor
t
ve
c
tor
mac
hine
metho
d
invol
ving
s
e
ve
r
a
l
opti
mal
e
xtr
a
c
ti
on
f
e
a
tu
r
e
s
s
uc
h
a
s
nor
malize
d
mea
n,
s
tanda
r
d
de
viation,
n
o
r
malize
d
s
e
c
ond
dif
f
e
r
e
nc
e
s
(
NFD)
,
a
nd
a
ddi
ti
ona
l
f
e
a
tur
e
s
o
n
E
DA
,
na
mely
mm
OR
a
nd
mdOR
,
with
a
windowing
o
f
1
0
s
e
c
onds
us
ing
the
hold
-
out
va
li
da
ti
o
n
va
li
da
ti
on
method.
I
n
the
S
VM
method,
the
s
e
lec
ti
on
of
ke
r
ne
l
typ
e
is
ve
r
y
inf
luential
on
a
c
c
ur
a
c
y
r
e
s
ult
s
.
T
he
ke
r
ne
l
that
pr
ovides
opti
mal
r
e
s
ult
s
f
o
r
a
nxiety
leve
l
c
las
s
if
ica
ti
on
is
the
polynom
ial
ke
r
ne
l.
T
his
ke
r
ne
l
is
f
lexible
be
c
a
us
e
it
ha
s
a
polynom
ial
de
gr
e
e
that
c
a
n
be
a
djus
ted
to
the
da
ta
us
e
d
s
o
that
it
c
a
n
c
las
s
if
y
da
ta
with
dif
f
e
r
e
nt
c
ompl
e
xit
ies
be
tt
e
r
than
other
ke
r
ne
ls
.
T
he
s
uppor
t
ve
c
tor
mac
hine
method
in
the
a
nxiety
leve
l
c
las
s
if
ica
ti
on
pr
oc
e
s
s
ha
s
good
pe
r
f
or
manc
e
,
e
s
pe
c
ially
in
3
c
las
s
e
s
that
pr
ovide
opti
mal
r
e
s
ult
s
with
a
mor
e
s
pe
c
if
ic
c
las
s
if
ica
ti
on.
F
r
om
the
r
e
s
e
a
r
c
h,
both
in
the
c
las
s
if
ica
ti
on
of
2
c
las
s
e
s
a
nd
thr
e
e
c
la
s
s
e
s
,
the
a
c
c
ur
a
c
y
va
lue
on
tes
t
da
ta
a
nd
t
r
a
ini
ng
da
ta
is
10
0%
,
with
pr
e
c
is
ion,
r
e
c
a
ll
,
a
nd
F
1
s
c
or
e
va
lues
of
100%
.
I
n
thi
s
s
tudy,
the
a
lgor
it
hm
c
a
n
pr
ovide
in
f
or
matio
n
on
the
leve
l
of
a
nxiety
in
s
ubjec
ts
who
ha
ve
a
s
pe
c
if
ic
phobia
(
a
r
a
c
hnophobia)
s
o
that
it
c
a
n
be
a
ppli
e
d
a
s
a
n
a
ddit
ional
ther
a
py
in
the
VR
E
T
/AR
E
T
t
r
e
a
tm
e
nt
method
with
the
a
im
of
pa
ti
e
nts
ge
tt
ing
mor
e
e
f
f
ici
e
nt,
opti
mal
a
nd
c
omf
o
r
table
tr
e
a
tm
e
nt
.
AC
KNOWL
E
DGE
M
E
NT
S
T
he
a
uthor
s
thank
the
f
a
c
ult
y
of
s
c
ienc
e
a
nd
t
e
c
hnology,
Unive
r
s
it
a
s
Air
langga
,
f
or
f
unding
thi
s
r
e
s
e
a
r
c
h
unde
r
the
Ai
r
langga
R
e
s
e
a
r
c
h
F
und
(
I
nte
r
na
ti
ona
l
R
e
s
e
a
r
c
h
Ne
twor
k)
with
g
r
a
nt
n
umber
1669/UN3.
L
P
P
M
/P
T
.
01
.
03/2023.
RE
F
E
RE
NC
E
S
[
1]
V
.
S
mi
th
,
J
.
R
e
ddy,
K
.
F
os
te
r
,
E
.
T
.
A
s
bur
y,
a
nd
J
.
B
r
ooks
,
“
P
ubl
ic
pe
r
c
e
pt
io
ns
,
knowle
dge
a
nd
s
ti
gma
to
w
a
r
ds
pe
opl
e
w
it
h
s
c
hi
z
ophr
e
ni
a
,”
J
ou
r
nal
of
P
ubl
ic
M
e
nt
al
H
e
al
th
, vol
. 10, no. 1, pp. 45
–
56, 2011, doi:
10.1108/174657211
11134547.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
15
,
No.
1
,
F
e
br
ua
r
y
20
25
:
337
-
34
8
346
[
2]
K
.
J
.
W
a
r
de
na
a
r
e
t
al
.
,
“
T
he
c
r
os
s
-
na
ti
ona
l
e
pi
de
mi
ol
ogy
of
s
p
e
c
if
ic
phobia
in
th
e
W
or
ld
M
e
nt
a
l
H
e
a
lt
h
S
ur
ve
ys
,”
P
s
y
c
hol
og
ic
al
M
e
di
c
in
e
, vol
. 47, no. 10, pp. 1744
–
1760, 2017, doi:
10.1017/S
0033291717000174.
[
3]
A
me
r
ic
a
n
P
s
yc
hi
a
tr
ic
A
s
s
oc
ia
ti
on,
D
ia
gnos
ti
c
and
S
ta
ti
s
ti
c
al
m
anual
of
m
e
nt
al
di
s
or
de
r
s
,
fo
ur
th
e
di
ti
on,
te
x
t
r
e
v
is
io
n
(
D
SM
-
IV
-
T
R
)
, vol
. 1, VA
:
A
me
r
ic
a
n P
s
yc
hi
a
tr
ic
A
s
s
oc
ia
ti
on, 2000.
[
4]
M
. R
e
tt
ig
a
nd J
.
C
r
a
w
f
or
d, “
G
e
tt
in
g pa
s
t
th
e
f
e
a
r
of
goi
ng t
o s
c
hool
,”
T
he
E
duc
at
io
n D
ig
e
s
t
, vol
. 9, no. 65, pp. 54
–
59, 2000.
[
5]
A
me
r
ic
a
n
P
s
yc
hi
a
tr
ic
A
s
s
oc
ia
ti
on,
D
ia
gnos
ti
c
and
s
ta
ti
s
ti
c
al
m
anual
of
m
e
nt
al
di
s
o
r
de
r
s
,
5
th
e
d.
A
me
r
ic
a
n
P
s
y
c
hi
a
tr
ic
A
s
s
oc
ia
ti
on P
ubl
is
hi
ng, 2022.
[
6]
J
.
S
.
N
e
vi
d,
S
.
A
.
R
a
th
u
s
,
a
nd
B
.
G
r
e
e
ne
,
“
A
bnor
ma
l
ps
yc
hol
o
gy :
in
a
c
ha
ngi
ng
w
or
ld
,”
W
or
ld
Spor
t
s
A
c
ti
v
e
w
e
ar
,
vol
.
8,
no
.
2,
pp. 55
–
56, 2018.
[
7]
C
.
M
.
C
oe
lh
o
a
nd
G
.
W
a
ll
is
,
“
D
e
c
ons
tr
uc
ti
ng
a
c
r
ophobia
:
ph
ys
io
lo
gi
c
a
l
a
nd
ps
yc
hol
ogi
c
a
l
pr
e
c
ur
s
or
s
to
de
ve
lo
pi
ng
a
f
e
a
r
of
he
ig
ht
s
,”
D
e
pr
e
s
s
io
n and A
nx
ie
ty
, vol
. 27, no. 9, pp. 864
–
870, 2010, doi:
10.1002/da.20698.
[
8]
V
.
R
a
ha
ni
,
A
.
V
a
r
d,
a
nd M
.
N
a
ja
f
i,
“
C
la
us
tr
ophobia
g
a
me
:
de
s
ig
n
a
nd
de
ve
lo
pme
nt
of
a
ne
w
vi
r
tu
a
l
r
e
a
li
ty
ga
me
f
or
tr
e
a
tm
e
nt
o
f
c
la
us
tr
ophobia
,”
J
our
nal
of
M
e
di
c
al
Si
gnal
s
and
S
e
ns
or
s
, vol
. 8
, no. 4, p. 231, 2018, doi
:
10.4103/j
ms
s
.J
M
S
S
_27_18.
[
9]
E
.
P
a
ul
us
,
F
.
P
.
Y
us
uf
,
M
.
S
ur
ya
ni
,
a
nd
I
.
S
ur
ya
na
,
“
D
e
ve
lo
pme
nt
a
nd
e
va
lu
a
ti
on
on
ni
ght
f
or
e
s
t
vi
r
tu
a
l
r
e
a
li
ty
a
s
in
nova
ti
ve
nyc
to
phobia
t
r
e
a
tm
e
nt
,”
J
our
nal
of
P
hy
s
ic
s
:
C
onf
e
r
e
nc
e
Se
r
ie
s
,
vol
. 1235, no. 1, 2019, doi
:
10.1088/1742
-
6596/1235/
1/
012003
.
[
10]
A
.
de
J
ongh,
“
T
r
e
a
tm
e
nt
of
a
w
oma
n
w
it
h
e
me
to
phobia
:
a
tr
a
uma
f
oc
us
e
d
a
ppr
oa
c
h,”
M
e
nt
al
I
ll
ne
s
s
,
vol
.
4,
no.
1,
pp.
10
–
14,
J
a
n. 2012, doi:
10.4081/m
i.
2012.e
3.
[
11]
D
.
T
a
o,
H
.
T
a
n, H
.
W
a
ng,
X
.
Z
ha
ng,
X
.
Q
u,
a
nd
T
.
Z
ha
ng,
“
A
s
ys
te
ma
ti
c
r
e
vi
e
w
of
phys
io
lo
gi
c
a
l
me
a
s
ur
e
s
of
me
nt
a
l
w
or
kl
oa
d,”
I
nt
e
r
nat
io
nal
J
our
nal
of
E
nv
ir
onm
e
nt
al
R
e
s
e
ar
c
h
and
P
ubl
ic
H
e
al
th
,
vol
.
16,
no.
15,
pp.
1
–
23,
J
ul
.
2
019,
doi
:
10.3390/i
je
r
ph16152716.
[
12]
A
.
M
A
R
T
I
N
O
C
I
N
N
E
R
A
e
t
al
.
,
“
H
e
a
da
c
he
s
tr
e
a
tm
e
nt
w
it
h
E
M
G
bi
of
e
e
dba
c
k:
a
f
oc
us
e
d
s
ys
t
e
ma
ti
c
r
e
vi
e
w
a
nd
me
ta
-
a
na
ly
s
is
,”
E
ur
ope
an
J
our
nal
of
P
hy
s
ic
al
and
R
e
habi
li
ta
ti
on
M
e
di
c
in
e
,
vol
.
59,
no.
6,
pp.
697
–
705,
J
a
n.
2024,
doi
:
10.23736
/S
19
73
-
9087.23.07745
-
6.
[
13]
F
.
J
a
dha
kha
n,
H
.
B
l
a
ke
,
D
.
H
e
tt
,
a
nd
S
. M
a
r
w
a
ha
,
“
E
f
f
ic
a
c
y
of
di
gi
ta
l
te
c
hnol
ogi
e
s
a
im
e
d
a
t
e
nh
a
nc
in
g
e
mot
io
n
r
e
gul
a
ti
on
s
ki
l
ls
:
L
it
e
r
a
tu
r
e
r
e
vi
e
w
,”
F
r
ont
ie
r
s
i
n P
s
y
c
hi
at
r
y
, vol
. 13, pp. 1
–
15, S
e
p. 2022, doi:
10.3389/f
ps
yt
.2022.809332.
[
14]
D
.
V
il
la
ni
,
C
.
C
a
r
is
s
ol
i,
S
.
T
r
ib
e
r
ti
,
A
.
M
a
r
c
he
tt
i,
G
.
G
il
li
,
a
nd
G
.
R
iv
a
,
“
V
id
e
oga
me
s
f
or
e
mot
io
n
r
e
gul
a
ti
on:
a
s
ys
te
ma
ti
c
r
e
vi
e
w
,”
G
am
e
s
f
o
r
H
e
al
th
J
our
nal
, vol
. 7, no. 2, pp. 85
–
99, A
pr
. 2018, doi:
10.1089/g4h.2017.0
108.
[
15]
H
.
Y
a
r
ib
e
ygi
,
Y
.
P
a
na
hi
,
H
.
S
a
hr
a
e
i,
T
.
P
.
J
ohns
to
n,
a
nd
A
.
S
a
he
bka
r
,
“
T
he
im
pa
c
t
of
s
tr
e
s
s
on
body
f
unc
ti
on:
a
r
e
vi
e
w
,”
E
X
C
L
I
J
our
nal
, vol
. 16, pp. 1057
–
1072, 2017, doi:
10.17179/exc
li
2017
-
480.
[
16]
G
.
G
ia
nna
ka
ki
s
,
D
.
G
r
ig
or
ia
di
s
,
K
.
G
ia
nna
ka
ki
,
O
.
S
im
a
nt
ir
a
k
i,
A
.
R
oni
ot
is
,
a
nd
M
.
T
s
ik
na
ki
s
,
“
R
e
vi
e
w
on
ps
yc
hol
ogi
c
a
l
s
t
r
e
s
s
de
te
c
ti
on
us
in
g
bi
os
ig
na
ls
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
A
ff
e
c
ti
v
e
C
om
put
in
g
,
vol
.
13,
no.
1,
pp.
440
–
460,
20
22,
doi
:
10.1109/T
A
F
F
C
.2019.2927337.
[
17]
J
.
A
.
H
e
a
le
y a
nd
R
.
W
.
P
ic
a
r
d,
“
D
e
te
c
ti
ng
s
tr
e
s
s
dur
in
g
r
e
a
l
-
w
or
ld
dr
iv
in
g
ta
s
ks
us
in
g
phys
io
lo
gi
c
a
l
s
e
ns
or
s
,”
I
E
E
E
T
r
ans
ac
t
io
ns
on I
nt
e
ll
ig
e
nt
T
r
ans
por
ta
ti
on Sy
s
te
m
s
, vol
. 6, no. 2, pp. 156
–
16
6, J
un. 2005, doi:
10.1109/T
I
T
S
.2005.848368.
[
18]
N
.
K
e
s
ha
n,
P
.
V
P
a
r
im
i,
a
nd
I
.
B
i
c
hi
nda
r
it
z
,
“
M
a
c
hi
ne
le
a
r
ni
ng
f
or
s
tr
e
s
s
de
t
e
c
ti
on
f
r
om
E
C
G
s
ig
na
ls
in
a
ut
omobi
le
dr
iv
e
r
s
,”
P
r
oc
e
e
di
ngs
-
2015
I
E
E
E
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
B
ig
D
at
a,
I
E
E
E
B
ig
D
at
a
2015
,
pp.
2661
–
2669,
2
015,
doi
:
10.1109/B
ig
D
a
ta
.2015.7364066.
[
19]
L
.
la
n
C
he
n,
Y
.
Z
ha
o,
P
.
f
e
i
Y
e
,
J
.
Z
ha
ng,
a
nd
J
.
z
hong
Z
ou,
“
D
e
te
c
ti
ng
dr
iv
in
g
s
tr
e
s
s
in
phys
io
lo
gi
c
a
l
s
ig
na
ls
ba
s
e
d
on
mul
ti
moda
l
f
e
a
tu
r
e
a
na
ly
s
is
a
nd
ke
r
ne
l
c
l
a
s
s
if
ie
r
s
,
”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h
A
ppl
ic
at
io
ns
,
vol
.
85,
pp.
279
–
291,
20
17,
doi
:
10.1016/j
.e
s
w
a
.2017.01.040.
[
20]
F
.
R
.
I
hmi
g,
H
.
A
nt
oni
o
G
oge
a
s
c
oe
c
he
a
,
F
.
N
e
ur
ohr
-
P
a
r
a
ke
ni
ngs
,
S
.
K
.
S
c
hä
f
e
r
,
J
.
L
a
s
s
-
H
e
nne
ma
nn,
a
nd
T
.
M
i
c
ha
e
l,
“
O
n
-
li
ne
a
nxi
e
ty
le
ve
l
de
te
c
ti
on
f
r
om
bi
os
ig
na
ls
:
ma
c
hi
ne
le
a
r
ni
ng
ba
s
e
d
on
a
r
a
ndomi
z
e
d
c
ont
r
ol
le
d
t
r
ia
l
w
it
h
s
pi
de
r
-
f
e
a
r
f
ul
in
di
vi
du
a
ls
,”
P
L
oS O
N
E
, vol
. 15, no. 6, 2020, doi
:
10.1371/j
ou
r
na
l.
pone
.0231517.
[
21]
B
.
G
ha
dda
r
a
nd
J
.
N
a
oum
-
S
a
w
a
ya
,
“
H
ig
h
di
me
ns
io
na
l
da
ta
c
l
a
s
s
if
ic
a
ti
on
a
nd
f
e
a
tu
r
e
s
e
le
c
ti
on
us
in
g
s
uppor
t
ve
c
to
r
ma
c
hi
ne
s
,
”
E
ur
ope
an J
our
nal
of
O
pe
r
at
io
nal
R
e
s
e
ar
c
h
, vol
. 265, no. 3, pp.
993
–
1004, M
a
r
. 2018, doi:
10.1016/j
.e
jo
r
.2017.08.040.
[
22]
S
.
M
a
ld
ona
do,
R
.
W
e
be
r
,
a
nd
F
.
F
a
mi
li
,
“
F
e
a
tu
r
e
s
e
le
c
ti
on
f
or
hi
gh
-
di
me
ns
io
na
l
c
la
s
s
-
im
ba
la
nc
e
d
d
a
ta
s
e
t
s
us
in
g
s
uppor
t
ve
c
to
r
ma
c
hi
ne
s
,”
I
nf
or
m
at
io
n Sc
ie
n
c
e
s
, vol
. 286, pp. 228
–
246, D
e
c
. 2
014, doi:
10.1016/j
.i
ns
.2014.07.015.
[
23]
S
.
G
.
H
of
ma
nn
a
nd
J
.
A
.
J
.
S
mi
ts
,
“
C
ogni
ti
ve
-
be
ha
vi
or
a
l
th
e
r
a
py
f
or
a
dul
t
a
nxi
e
ty
di
s
or
de
r
s
:
A
me
ta
-
a
n
a
ly
s
is
of
r
a
ndomi
z
e
d
pl
a
c
e
bo
-
c
ont
r
ol
le
d t
r
ia
ls
,”
J
our
nal
of
C
li
ni
c
al
P
s
y
c
hi
at
r
y
, vol
.
69, no. 4, pp. 621
–
632, 2008, doi:
10.4088/j
c
p.v69n0415.
[
24]
W
.
M
a
ns
e
ll
,
C
opi
ng
w
it
h
fe
ar
s
and
phobias
:
a
C
B
T
gui
de
to
unde
r
s
ta
ndi
ng
and
fa
c
in
g
y
our
anx
ie
ti
e
s
.
N
e
w
Y
or
k:
O
ne
w
or
ld
P
ubl
ic
a
ti
ons
, 2007.
[
25]
S
.
V
a
r
a
dhi
la
P
e
r
is
ti
a
nt
o
a
nd
K
.
A
s
tu
ti
,
“
D
e
c
r
e
a
s
in
g
s
ympt
oms
of
s
pe
c
if
ic
phobi
a
s
w
it
h
c
ogni
ti
ve
be
h
a
vi
or
th
e
r
a
py,”
M
al
ay
s
ia
n
M
e
nt
al
H
e
al
th
J
our
nal
, vol
. 1, no. 1, pp. 12
–
14, 2022, doi:
10.26480/m
mhj
.01.2022.12.14.
[
26]
K
.
B
.
W
ol
it
z
ky
-
T
a
yl
or
,
J
.
D
.
H
or
ow
it
z
,
M
.
B
.
P
ow
e
r
s
,
a
nd
M
.
J
.
T
e
lc
h,
“
P
s
yc
hol
ogi
c
a
l
a
ppr
oa
c
he
s
in
th
e
tr
e
a
tm
e
nt
of
s
p
e
c
if
ic
phobia
s
:
A
me
ta
-
a
na
ly
s
i
s
,”
C
li
ni
c
al
P
s
y
c
hol
og
y
R
e
v
ie
w
, vol
. 28
, no. 6, pp. 1021
–
1037, 2008, doi:
10.1016/j
.c
pr
.2008.02.007.
[
27]
D
.
B
oe
ld
t,
E
.
M
c
M
a
hon,
M
.
M
c
F
a
ul
,
a
nd
W
.
G
r
e
e
nl
e
a
f
,
“
U
s
in
g
vi
r
tu
a
l
r
e
a
li
ty
e
xpos
ur
e
th
e
r
a
py
to
e
nha
nc
e
tr
e
a
tm
e
nt
of
a
nxi
e
ty
di
s
or
de
r
s
:
id
e
nt
if
yi
ng
a
r
e
a
s
of
c
li
ni
c
a
l
a
dopt
io
n
a
nd
pot
e
nt
ia
l
obs
ta
c
le
s
,”
F
r
ont
ie
r
s
in
P
s
y
c
hi
at
r
y
,
vol
.
10,
pp.
1
–
6,
O
c
t.
2019,
doi
:
10.3389/f
ps
yt
.2019.00773.
[
28]
F
.
R
.
I
hm
ig
,
A
.
G
og
e
a
s
c
o
e
c
h
e
a
,
S
.
S
c
h
ä
f
e
r
,
J
.
L
a
s
s
-
H
e
nn
e
m
a
nn
,
a
n
d
T
.
M
i
c
h
a
e
l,
“
E
l
e
c
tr
oc
a
r
di
ogr
a
m,
s
ki
n
c
on
du
c
t
a
nc
e
a
nd
r
e
s
p
ir
a
ti
on
f
r
om
s
p
id
e
r
-
f
e
a
r
f
ul
i
n
di
vi
du
a
l
s
w
a
tc
hi
ng
s
pi
d
e
r
vi
d
e
o
c
l
ip
s
(
v
e
r
s
i
on
1
.0
.0
)
,
”
P
h
y
s
io
N
e
t
,
20
20
,
do
i:
1
0.
13
02
6/
s
q6
q
-
z
g0
4.
[
29]
A
.
L
.
G
ol
dbe
r
ge
r
e
t
al
.
,
“
P
hys
io
B
a
nk,
P
hys
io
T
ool
ki
t,
a
nd
P
hy
s
io
N
e
t,
”
C
ir
c
ul
at
io
n
,
vol
.
101,
no.
23,
pp.
215
-
220,
J
un.
2000,
doi
:
10.1161/01.C
I
R
.101.23.e
215.
[
30]
M
.
R
in
c
k
e
t
al
.
,
“
R
e
li
a
bi
li
ty
a
nd
va
li
di
ty
of
G
e
r
ma
n
ve
r
s
io
ns
of
th
r
e
e
in
s
tr
ume
nt
s
me
a
s
ur
in
g
f
e
a
r
of
s
pi
de
r
s
,”
D
ia
gnos
ti
c
a
,
vol
. 48, no. 3, 2002, doi
:
10.1026//
0012
-
1924.48.3.141.
[
31]
E
. S
. B
e
c
ke
r
a
nd M
. R
in
c
k, “
S
e
ns
it
iv
it
y a
nd r
e
s
pons
e
bi
a
s
i
n f
e
a
r
of
s
pi
de
r
s
,”
C
ogni
ti
on and E
m
ot
io
n
,
vol
. 18, no.
7, pp. 961
–
976,
2004, doi:
10.1080/026999303
41000329.
[
32]
M
.
R
in
c
k
a
nd
E
.
S
.
B
e
c
ke
r
,
“
S
pi
de
r
f
e
a
r
f
ul
in
di
vi
dua
ls
a
tt
e
nd
to
th
r
e
a
t,
th
e
n
qui
c
kl
y
a
voi
d
it
:
E
vi
de
nc
e
f
r
om
e
ye
move
me
n
ts
,”
J
our
nal
of
A
bnor
m
al
P
s
y
c
hol
ogy
, vol
. 115, no. 2, pp. 231
–
238,
2006, doi:
10.1037/0021
-
843X.115.2.231.
[
33]
E
.
E
r
df
e
ld
e
r
,
F
.
F
A
ul
,
A
.
B
uc
hne
r
,
a
nd
A
.
G
.
L
a
ng,
“
S
ta
ti
s
ti
c
a
l
pow
e
r
a
na
ly
s
e
s
us
in
g
G
*P
ow
e
r
3.1:
te
s
ts
f
or
c
or
r
e
la
ti
on
a
nd
r
e
gr
e
s
s
io
n a
na
ly
s
e
s
,”
B
e
hav
io
r
R
e
s
e
a
r
c
h M
e
th
ods
, vol
. 41, no.
4, pp. 1149
–
1160, 2009, doi:
10.3758/B
R
M
.41.4.1149.
[
34]
J
.
L
a
s
s
-
H
e
nn
e
ma
nn
a
nd
T
.
M
i
c
ha
e
l,
“
E
ndoge
nou
s
c
or
ti
s
ol
le
ve
ls
in
f
lu
e
nc
e
e
xpo
s
ur
e
th
e
r
a
py
in
s
pi
de
r
phobia
,”
B
e
hav
i
our
R
e
s
e
ar
c
h and T
he
r
apy
, vol
. 60, pp. 39
–
45, S
e
p. 2014, doi:
10.1
016/
j.
br
a
t.
2014.06.009.
[
35]
Z
.
F
.
M
ohd
A
pa
ndi
,
R
.
I
ke
ur
a
,
S
.
H
a
ya
ka
w
a
,
a
nd
S
.
T
s
ut
s
umi
,
“
Q
R
S
de
te
c
ti
on
in
e
le
c
tr
oc
a
r
di
ogr
a
m
s
ig
na
l
of
e
xe
r
c
i
s
e
phys
ic
a
l
a
c
ti
vi
ty
,”
J
our
nal
of
P
hy
s
ic
s
:
C
onf
e
r
e
n
c
e
Se
r
ie
s
, vol
. 2319, no.
1, pp. 1
–
9, A
ug. 2022, doi:
10.1088/1742
-
6596/2319/
1/
012021.
Evaluation Warning : The document was created with Spire.PDF for Python.