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ith
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s
tr
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d
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with
f
ac
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ac
k
in
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in
l
o
w
lig
h
t
[
1
]
–
[
5
]
.
E
lectr
o
en
ce
p
h
al
o
g
r
a
p
h
y
(
E
E
G)
an
d
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d
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(
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with
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at
im
p
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o
v
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m
en
tal
h
ea
lth
o
u
tco
m
es
[
6
]
–
[
8
]
.
Stre
s
s
im
p
ac
ts
b
o
th
t
h
e
b
r
ain
an
d
ca
r
d
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lar
s
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s
tem
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tially
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s
in
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ar
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m
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R
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cu
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ac
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d
ca
r
d
io
v
ascu
lar
h
ea
lth
[
9
]
–
[
1
2
]
.
R
esear
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ev
alu
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m
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lik
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s
u
p
p
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r
t
v
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to
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(
SVM)
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r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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C
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,
Vo
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15
,
No
.
2
,
Ap
r
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20
25
:
1
6
4
7
-
1
6
5
5
1648
d
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L
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m
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[
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.
An
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ac
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[
1
4
]
.
Key
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in
clu
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co
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p
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tatio
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al
co
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n
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n
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ata.
T
h
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Ar
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tu
r
e
s
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tio
n
m
eth
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d
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d
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r
id
p
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ee
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co
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al
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g
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ter
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m
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STM
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Sectio
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1
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ev
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m
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tal
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n
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s
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ti
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2
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s
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3
p
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e
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in
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co
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p
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em
with
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t
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2.
M
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M
E
T
H
O
D
T
h
e
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
s
y
s
tem
en
h
an
ce
s
ac
c
u
r
ac
y
b
y
tack
lin
g
ch
allen
g
es
s
u
c
h
as
n
o
is
e,
co
m
p
lex
ity
,
an
d
d
ata
in
teg
r
at
io
n
.
Ad
v
an
ce
d
p
r
e
p
r
o
ce
s
s
in
g
m
eth
o
d
s
,
in
clu
d
i
n
g
n
o
is
e
r
e
m
o
v
al
a
n
d
a
r
tifa
ct
r
ejec
tio
n
,
im
p
r
o
v
e
s
ig
n
al
clar
ity
.
R
o
b
u
s
t
f
ea
tu
r
e
s
elec
tio
n
co
m
b
in
ed
with
d
ee
p
lear
n
in
g
m
o
d
els
en
ab
les
th
e
d
etec
tio
n
o
f
i
n
tr
icate
p
atter
n
s
f
o
r
m
o
r
e
r
eliab
le
s
tr
ess
d
etec
tio
n
.
2
.
1
.
Da
t
a
s
et
T
h
e
p
r
o
p
o
s
ed
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
s
y
s
tem
in
teg
r
ates
E
E
G
s
ig
n
als
f
r
o
m
th
e
o
n
l
in
e
DE
AP
d
ataset
an
d
E
C
G
s
ig
n
als
f
r
o
m
th
e
o
n
lin
e
W
E
SAD
d
ataset.
T
h
is
r
esear
ch
co
n
d
u
cts
ex
p
er
i
m
en
ts
ac
r
o
s
s
th
r
ee
d
is
tin
ct
co
n
f
ig
u
r
atio
n
s
:
E
C
G
s
tr
ess
d
etec
tio
n
,
E
E
G
s
tr
ess
d
etec
tio
n
,
an
d
m
u
ltimo
d
a
l
s
tr
ess
d
etec
tio
n
.
E
E
G
an
d
E
C
G
d
ata
s
am
p
les a
r
e
co
n
s
id
er
ed
u
n
d
er
n
o
r
m
al
an
d
s
tr
ess
co
n
d
itio
n
s
,
as lis
ted
in
T
ab
le
1
.
T
ab
le
1
.
E
E
G
a
n
d
E
C
G
s
tr
ess
d
etec
tio
n
s
am
p
le
C
l
a
s
s
EEG
sam
p
l
e
s
(
D
EA
P
d
a
t
a
s
e
t
)
EC
G
sa
mp
l
e
s
(
W
ESA
D
d
a
t
a
se
t
)
N
o
r
mal
1
0
4
2
8
3
S
t
r
e
ss
1
4
0
1
6
5
To
t
a
l
2
4
4
4
4
8
Mu
ltimo
d
al
s
tr
ess
d
etec
tio
n
u
tili
ze
s
p
air
ed
E
E
G
an
d
E
C
G
d
ata
to
ex
am
in
e
s
tr
ess
ef
f
ec
ts
o
n
b
r
ain
an
d
h
ea
r
t
ac
tiv
ity
.
T
h
e
d
ataset
c
o
m
p
r
is
es
2
4
4
s
am
p
les,
with
1
0
4
f
r
o
m
n
o
r
m
al
co
n
d
itio
n
s
a
n
d
1
4
0
f
r
o
m
s
tr
ess
co
n
d
itio
n
s
,
f
ac
ilit
atin
g
an
al
y
s
is
o
f
b
o
th
s
y
s
tem
s
.
T
h
is
ap
p
r
o
a
ch
p
r
o
v
id
es
a
co
m
p
r
e
h
en
s
iv
e
ev
alu
atio
n
o
f
s
tr
ess
in
d
icato
r
s
,
en
h
a
n
cin
g
d
etec
tio
n
ac
cu
r
ac
y
an
d
r
eliab
ilit
y
.
2
.
2
.
P
r
o
po
s
ed
m
et
ho
do
lo
g
y
T
h
e
p
r
o
p
o
s
ed
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
m
eth
o
d
o
lo
g
y
in
v
o
l
v
es
f
iv
e
k
ey
s
tag
es:
en
h
an
cin
g
E
E
G/E
C
G
s
ig
n
als
v
ia
wav
elet
p
ac
k
et
tr
an
s
f
o
r
m
,
ex
tr
ac
tin
g
m
u
ltip
le
f
ea
tu
r
es,
p
e
r
f
o
r
m
in
g
f
ea
tu
r
e
-
lev
el
f
u
s
io
n
,
s
elec
tin
g
r
elev
an
t
f
ea
tu
r
es
wit
h
th
e
A
o
A
-
AHP
alg
o
r
ith
m
,
an
d
u
tili
zin
g
PDC
NN
-
L
STM
f
o
r
f
in
al
d
etec
tio
n
,
as
s
h
o
wn
in
Fig
u
r
e
1
.
E
ac
h
s
tag
e
ad
d
r
ess
es
ch
allen
g
es
lik
e
n
o
is
e,
co
m
p
lex
ity
,
an
d
h
ig
h
-
d
i
m
en
s
io
n
al
d
ata.
T
h
e
wav
elet
p
ac
k
et
tr
an
s
f
o
r
m
im
p
r
o
v
es
s
ig
n
al
clar
ity
b
y
r
em
o
v
in
g
n
o
is
e
an
d
r
etai
n
in
g
es
s
en
tial
in
f
o
r
m
atio
n
,
wh
ile
th
e
Ao
A
-
AHP
alg
o
r
ith
m
o
p
tim
izes f
ea
tu
r
e
s
elec
tio
n
t
o
en
h
a
n
ce
m
o
d
el
ef
f
icien
cy
an
d
ac
cu
r
ac
y
.
2
.
2
.
1
.
E
nh
a
ncing
E
E
G
/E
CG
s
ig
na
ls
v
ia
wa
v
elet
pa
ck
e
t
t
r
a
ns
f
o
rm
R
aw
E
E
G
an
d
E
C
G
s
ig
n
als
g
o
th
r
o
u
g
h
p
r
ep
r
o
ce
s
s
in
g
to
r
e
m
o
v
e
n
o
is
e
an
d
ar
tifa
cts
u
s
in
g
tech
n
iq
u
es
lik
e
f
ilter
in
g
,
ar
tifa
ct
r
ejec
ti
o
n
.
A
f
ter
p
r
ep
r
o
ce
s
s
in
g
,
th
e
s
ig
n
als
ar
e
en
h
a
n
ce
d
b
y
t
h
e
wav
elet
p
ac
k
et
d
ec
o
m
p
o
s
itio
n
m
eth
o
d
as
it
d
i
v
id
es
th
em
in
to
f
r
e
q
u
en
c
y
b
a
n
d
s
an
d
r
ec
o
n
s
tr
u
cts
th
em
to
im
p
r
o
v
e
cla
r
ity
an
d
r
ed
u
ce
n
o
is
e,
as illu
s
tr
ated
in
Fig
u
r
e
2
.
T
h
is
m
eth
o
d
i
n
clu
d
es
d
ec
o
m
p
o
s
in
g
th
e
E
E
G/E
C
G
s
ig
n
al
u
s
i
n
g
wav
elet
p
ac
k
et
tr
an
s
f
o
r
m
(
W
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E
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f
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t
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T
h
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r
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r
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5
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n
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f
e
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tr
ess
lev
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E
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f
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tu
r
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o
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s
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tical
m
ea
s
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r
es,
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p
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s
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d
f
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d
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p
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r
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b
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ar
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in
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d
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T
a
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l
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1
3
E
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G
f
e
a
t
u
r
es
c
a
p
t
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r
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b
r
a
i
n
s
i
g
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al
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s
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e
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t
r
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,
te
m
p
o
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n
d
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p
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t
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t
r
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s
-
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l
a
t
e
d
p
a
t
te
r
n
s
.
T
ab
le
2
.
E
x
tr
ac
ted
m
u
ltip
le
E
E
G
f
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tu
r
es
F
e
a
t
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r
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o
u
p
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o
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e
a
t
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r
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g
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p
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a
t
e
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F
e
a
t
u
r
e
s
1
S
t
a
t
i
st
i
c
a
l
m
e
a
s
u
r
e
M
e
a
n
,
S
D
,
v
a
r
i
a
t
i
o
n
,
m
e
d
i
a
n
,
s
k
e
w
n
e
ss
2
Te
mp
o
r
a
l
f
e
a
t
u
r
e
A
c
t
i
v
i
t
y
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k
ea
ch
f
ea
t
u
r
e'
s
im
p
o
r
tan
ce
.
A
co
n
s
is
ten
cy
c
h
ec
k
en
s
u
r
es
th
at
th
e
co
m
p
ar
is
o
n
s
r
em
ain
r
eliab
le
an
d
lo
g
ical,
h
elp
in
g
to
d
eter
m
in
e
th
e
m
o
s
t sig
n
if
ican
t
f
ea
tu
r
es f
o
r
s
tr
ess
d
etec
tio
n
.
Fig
u
r
e
4
.
Ar
c
h
im
ed
es o
p
tim
iz
atio
n
alg
o
r
ith
m
Fig
u
r
e
5
.
Hier
ar
c
h
ically
f
lo
wc
h
ar
t o
f
a
n
aly
tical
h
ier
ar
c
h
y
p
r
o
ce
s
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
1
6
4
7
-
1
6
5
5
1652
2
.
2
.
5
.
Str
ess
det
ec
t
io
n t
hro
ug
h P
DCNN
-
L
S
T
M
T
h
e
A
o
A
-
AHP
a
p
p
r
o
ac
h
g
e
n
er
ates
a
1
D
f
ea
tu
r
e
v
ec
to
r
,
p
ass
in
g
th
r
o
u
g
h
PDC
NN
co
n
v
o
lu
tio
n
al
lay
er
s
.
B
atch
n
o
r
m
aliza
tio
n
a
n
d
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U)
ac
tiv
atio
n
en
h
an
ce
n
o
n
lin
ea
r
ity
.
Po
o
lin
g
lay
er
s
r
ed
u
ce
s
p
atial
d
im
e
n
s
io
n
s
wh
ile
r
etain
in
g
cr
itical
in
f
o
r
m
ati
o
n
.
Su
b
s
eq
u
en
tly
,
t
h
e
L
STM
n
etwo
r
k
ca
p
tu
r
es
tem
p
o
r
al
d
ep
e
n
d
en
cies
u
s
in
g
m
em
o
r
y
ce
lls
an
d
g
ates
with
Sig
m
o
id
an
d
T
a
n
h
ac
tiv
ati
o
n
s
.
B
y
co
m
b
in
in
g
s
p
atial
f
ea
tu
r
e
ex
tr
ac
tio
n
with
s
eq
u
en
tial
p
r
o
ce
s
s
in
g
,
th
e
PDC
N
N
-
L
STM
ef
f
ec
tiv
ely
an
aly
s
es
s
p
atially
d
ep
en
d
e
n
t
d
ata.
T
h
is
m
o
d
el
o
p
tim
izes
lay
er
s
izes,
d
r
o
p
o
u
t
r
ates,
an
d
lear
n
in
g
r
ates
d
u
r
in
g
tr
ain
in
g
,
m
ak
in
g
it
s
u
itab
le
f
o
r
d
etec
tin
g
co
m
p
lex
p
atter
n
s
lik
e
s
tr
ess
.
3.
E
XP
E
R
I
M
E
N
T
A
L
RE
SUL
T
S AN
D
D
I
SC
USS
I
O
N
E
x
p
er
im
en
tal
r
esu
lts
s
h
o
w
th
a
t
v
ar
io
u
s
alg
o
r
ith
m
s
d
etec
t
s
tr
ess
f
r
o
m
E
E
G
an
d
E
C
G
d
ata
e
f
f
ec
tiv
ely
.
T
r
ad
itio
n
al
m
eth
o
d
s
lik
e
d
e
cisi
o
n
tr
ee
s
(
C
T
)
an
d
k
-
n
ea
r
est
n
eig
h
b
o
r
s
(
KNN)
s
tr
u
g
g
le
with
co
m
p
lex
p
atter
n
s
,
wh
ile
SVM,
esp
ec
ially
th
e
r
ad
ial
b
asis
f
u
n
ctio
n
(
R
B
F)
v
ar
ian
t,
h
an
d
les
n
o
n
-
lin
ea
r
ity
b
etter
.
E
n
s
em
b
le
m
eth
o
d
s
im
p
r
o
v
e
ac
c
u
r
ac
y
b
u
t la
g
b
eh
in
d
d
ee
p
lear
n
in
g
m
o
d
els.
Fig
u
r
e
6
h
ig
h
lig
h
ts
PDC
NN
-
L
STM
as
a
to
p
p
er
f
o
r
m
e
r
,
ac
h
iev
in
g
9
7
.
3
%
ac
cu
r
ac
y
an
d
1
0
0
%
p
r
ec
is
io
n
b
y
c
o
m
b
in
in
g
s
p
atial
an
d
tem
p
o
r
al
f
ea
tu
r
es
f
o
r
ef
f
ec
tiv
e
s
tr
ess
d
etec
tio
n
.
T
h
e
r
esear
ch
test
s
th
e
PDC
NN
-
L
STM
alg
o
r
ith
m
f
o
r
s
tr
ess
d
etec
tio
n
u
s
in
g
E
E
G
-
o
n
ly
,
E
C
G
-
o
n
ly
,
an
d
m
u
lti
m
o
d
al
(
E
E
G+
E
C
G)
co
n
f
ig
u
r
atio
n
s
.
I
t
h
ig
h
lig
h
ts
th
is
alg
o
r
ith
m
'
s
p
er
f
o
r
m
an
ce
,
an
d
T
ab
le
4
c
o
m
p
a
r
es
its
ac
cu
r
ac
y
with
lead
in
g
m
eth
o
d
s
an
d
co
n
f
ir
m
s
its
ef
f
ec
tiv
en
ess
f
o
r
s
tr
ess
r
ec
o
g
n
itio
n
in
ea
ch
s
etu
p
.
Fig
u
r
e
6
.
C
o
m
p
a
r
ativ
e
an
aly
s
i
s
o
f
alg
o
r
ith
m
s
T
ab
le
4
.
Per
f
o
r
m
an
ce
ev
alu
ati
o
n
o
f
th
e
p
r
o
p
o
s
ed
s
tr
ess
d
etec
tio
n
s
y
s
tem
s
ag
ain
s
t state
-
of
-
th
e
-
ar
t
A
u
t
h
o
r
B
i
o
s
i
g
n
a
l
u
se
d
D
e
e
p
l
e
a
r
n
i
n
g
m
o
d
e
l
A
c
c
u
r
a
c
y
[
1
5
]
EEG
C
N
N
6
0
.
2
1
%
[
1
6
]
EEG
D
e
e
p
C
N
N
6
4
.
2
0
%
[
1
7
]
EEG
C
N
N
7
7
.
9
0
%
[
1
8
]
EEG
EEG
-
C
o
n
v
8
2
.
9
5
%
[
1
9
]
EEG
3
-
D
A
l
e
x
N
e
t
C
N
N
8
6
.
1
2
%
[
2
0
]
EEG
S
y
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e
t
r
i
c
D
C
A
N
8
7
.
6
2
%
[
2
1
]
EEG
2
-
D
C
N
N
9
3
.
0
0
%
[
2
2
]
EEG
Tw
o
-
l
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y
e
r
LST
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9
3
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2
7
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[
2
3
]
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o
n
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t
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8
4
.
4
8
%
[
2
4
]
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W
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+
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8
2
.
5
7
%
[
2
5
]
EC
G
,
ED
A
F
D
A
8
7
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5
%
[
2
6
]
EC
G
,
ED
A
,
B
V
P
ANN
7
9
%
[
2
7
]
EEG
,
E
C
G
P
C
A
,
S
V
M
7
9
.
5
4
%
[
2
8
]
EEG
,
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C
G
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G
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6
.
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[
2
9
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EEG
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A
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C
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V
M
8
6
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P
r
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se
d
m
o
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a
l
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G
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C
N
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+
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8
8
.
6
EEG
95
EEG
+
E
C
G
9
7
.
3
N
o
t
e
.
ED
A
:
e
l
e
c
t
r
o
d
e
r
mal
a
c
t
i
v
i
t
y
,
B
V
P
:
b
l
o
o
d
v
o
l
u
me
p
u
l
s
e
,
EM
G
:
e
l
e
c
t
r
o
my
o
g
r
a
p
h
y
,
G
W
O
:
g
r
e
y
w
o
l
f
o
p
t
i
mi
z
e
r
,
F
D
A
:
f
u
n
c
t
i
o
n
a
l
d
a
t
a
a
n
a
l
y
si
s
,
ANN
:
a
r
t
i
f
i
c
i
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
A
d
va
n
ce
d
s
tr
ess
d
etec
tio
n
w
it
h
o
p
timiz
ed
fea
tu
r
e
s
elec
tio
n
a
n
d
h
yb
r
id
…
(
S
a
n
g
ita
A
jit P
a
ti
l
)
1653
3
.
1
.
Arc
hite
ct
ure
des
ig
n o
f
t
he
pro
po
s
ed
m
ultim
o
da
l st
re
s
s
det
ec
t
io
n
T
h
is
s
tu
d
y
im
p
r
o
v
es
ac
cu
r
ac
y
b
y
in
te
g
r
atin
g
t
h
e
o
p
tim
ize
d
Ao
A
-
AHP
f
ea
tu
r
e
s
elec
tio
n
tech
n
iq
u
e
with
a
n
o
v
el
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
ar
ch
itectu
r
e,
as
s
h
o
wn
in
Fig
u
r
e
7
.
T
h
e
p
r
o
ce
s
s
b
eg
in
s
b
y
d
en
o
is
in
g
E
E
G
an
d
E
C
G
s
ig
n
als
to
en
h
an
ce
d
ata
q
u
ality
.
Ao
A
-
AHP
th
en
s
elec
ts
th
e
m
o
s
t
r
elev
a
n
t
f
ea
tu
r
es,
b
o
o
s
tin
g
m
o
d
el
p
er
f
o
r
m
a
n
ce
.
T
h
e
ex
p
e
r
im
en
ts
ev
alu
ate
h
o
w
Ao
A
-
A
HP
im
p
ac
ts
ac
cu
r
ac
y
ac
r
o
s
s
t
h
r
ee
co
n
f
i
g
u
r
atio
n
s
.
I
n
E
C
G
s
tr
ess
d
etec
tio
n
,
Ao
A
-
AHP
s
elec
t
s
5
0
f
ea
tu
r
es
an
d
r
aises
ac
cu
r
ac
y
to
9
1
.
7
9
%,
co
m
p
ar
ed
to
8
8
.
6
%
with
all
7
2
f
ea
tu
r
es
u
s
in
g
PD
C
NN+
L
STM
.
I
n
E
E
G
s
tr
ess
d
etec
tio
n
,
Ao
A
-
AHP
with
3
5
0
f
ea
tu
r
es
in
c
r
ea
s
es
ac
cu
r
ac
y
to
9
6
.
5
%,
u
p
f
r
o
m
9
5
%
with
all
5
1
3
f
ea
tu
r
es.
Fo
r
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
,
Ao
A
-
AHP
with
3
5
0
f
ea
tu
r
es
b
o
o
s
ts
ac
cu
r
ac
y
to
9
8
.
6
%,
s
u
r
p
ass
in
g
th
e
9
7
.
3
%
ac
h
iev
ed
with
all
5
8
6
f
ea
tu
r
es.
Ov
er
all,
Ao
A
-
AHP
s
ig
n
if
ican
tly
en
h
an
ce
s
ac
cu
r
a
cy
b
y
o
p
tim
izin
g
f
ea
tu
r
e
s
el
ec
tio
n
,
r
ed
u
ci
n
g
d
im
en
s
io
n
ali
ty
,
an
d
p
r
eser
v
in
g
ess
en
tial in
f
o
r
m
atio
n
.
Fig
u
r
e
7
.
Ar
c
h
itectu
r
e
d
esig
n
o
f
m
u
ltimo
d
al
s
tr
ess
d
etec
tio
n
4.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
im
p
r
o
v
es
s
tr
ess
d
etec
tio
n
b
y
em
p
lo
y
in
g
a
h
y
b
r
id
a
r
ch
itectu
r
e
th
at
co
m
b
in
e
s
a
L
STM
m
o
d
el
with
a
PDC
NN
to
an
aly
ze
E
E
G
an
d
E
C
G
s
ig
n
als.
I
t
en
h
an
ce
s
f
ea
tu
r
e
s
elec
tio
n
b
y
in
teg
r
atin
g
th
e
Ao
A
with
th
e
AHP.
T
h
is
p
r
o
p
o
s
ed
s
y
s
tem
ef
f
ec
tiv
ely
ad
d
r
e
s
s
es
n
o
is
e
an
d
h
ig
h
d
im
en
s
io
n
ality
,
ac
h
iev
in
g
s
ig
n
if
ican
t
ac
cu
r
ac
y
im
p
r
o
v
e
m
en
ts
:
E
C
G
s
tr
ess
d
etec
tio
n
r
is
es
f
r
o
m
8
8
.
6
%
to
9
1
.
7
9
%,
E
E
G
d
etec
tio
n
im
p
r
o
v
es f
r
o
m
9
5
% to
9
6
.
6
%,
an
d
th
e
m
u
ltimo
d
al
a
p
p
r
o
ac
h
r
ea
ch
es 9
8
.
6
% a
cc
u
r
ac
y
.
T
h
ese
ad
v
an
ce
m
e
n
ts
h
av
e
s
u
b
s
tan
tial
im
p
licatio
n
s
f
o
r
c
lin
ical
p
r
ac
tice
an
d
in
d
u
s
tr
ia
l
s
ettin
g
s
.
C
lin
ically
,
th
e
s
y
s
tem
en
ab
les
ea
r
lier
a
n
d
m
o
r
e
p
r
ec
is
e
id
e
n
tific
atio
n
o
f
s
tr
ess
-
r
elate
d
co
n
d
itio
n
s
,
lead
in
g
to
tim
ely
in
ter
v
en
tio
n
an
d
b
etter
m
en
tal
h
ea
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