I
nte
rna
t
io
na
l J
o
urna
l o
f
Adv
a
nces in Applie
d Science
s
(
I
J
AAS)
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
,
p
p
.
352
~
3
6
0
I
SS
N:
2252
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8
8
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,
DOI
:
1
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.
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.
v
14
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i
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.
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352
J
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:
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ttp
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a
a
s
.
ia
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r
e.
co
m
A deep
learning
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a
sed my
o
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rdia
l inf
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rction cla
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ticle
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J
u
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R
ev
is
ed
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6
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2
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Acc
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ted
Ap
r
2
3
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2
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2
5
Ac
u
te
m
y
o
c
a
rd
ial
in
fa
rc
ti
o
n
(
A
M
I)
c
a
rries
a
sig
n
ifi
c
a
n
t
risk
,
e
m
p
h
a
siz
in
g
th
e
c
rit
ica
l
n
e
e
d
f
o
r
p
re
c
ise
d
iag
n
o
sis
a
n
d
p
r
o
m
p
t
trea
tme
n
t
o
f
th
e
re
sp
o
n
si
b
le
les
io
n
.
C
o
n
se
q
u
e
n
tl
y
,
we
d
e
v
ise
d
a
n
e
u
ra
l
n
e
two
r
k
a
lg
o
rit
h
m
i
n
th
is
i
n
v
e
sti
g
a
ti
o
n
to
id
e
n
ti
f
y
m
y
o
c
a
rd
ial
in
fa
rc
ti
o
n
(
MI
)
fro
m
e
lec
tr
o
c
a
rd
io
g
ra
m
s
(ECG
s)
a
u
to
n
o
m
o
u
sl
y
.
A
n
ECG
is
a
sta
n
d
a
rd
d
iag
n
o
stic
to
o
l
f
o
r
id
e
n
ti
fy
in
g
a
c
u
te
M
I
d
u
e
to
it
s
a
ffo
r
d
a
b
il
it
y
,
sa
fe
ty
,
a
n
d
ra
p
i
d
re
p
o
rti
n
g
.
M
a
n
u
a
l
a
n
a
ly
sis
o
f
E
CG
r
e
su
lt
s
b
y
c
a
rd
io
lo
g
ists
is
b
o
th
ti
m
e
-
c
o
n
su
m
in
g
a
n
d
p
ro
n
e
to
e
rr
o
r
s.
Th
is
p
a
p
e
r
p
r
o
p
o
se
s
a
d
e
e
p
lea
rn
in
g
a
lg
o
rit
h
m
th
a
t
c
a
n
c
a
p
tu
re
a
n
d
a
u
to
m
a
ti
c
a
ll
y
c
las
sify
m
u
lt
i
p
le
fe
a
tu
re
s
o
f
a
n
ECG
sig
n
a
l.
We
p
r
o
p
o
se
a
h
y
b
ri
d
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
ra
l
n
e
two
r
k
(
CNN
)
a
n
d
lo
n
g
sh
o
rt
-
term
m
e
m
o
ry
(LS
T
M
)
fo
r
a
u
to
m
a
ti
c
a
ll
y
d
iag
n
o
sin
g
M
I.
T
o
g
e
n
e
ra
te
th
e
h
y
b
ri
d
CNN
-
LS
T
M
m
o
d
e
l,
we
p
ro
p
o
se
d
3
9
m
o
d
e
ls
with
h
y
p
e
r
p
a
ra
m
e
ter
tu
n
i
n
g
.
As
a
re
su
l
t,
th
e
b
e
st
m
o
d
e
l
is
m
o
d
e
l
3
5
,
wi
t
h
8
6
.
8
6
%
a
c
c
u
ra
c
y
,
7
5
.
2
8
%
se
n
sit
iv
it
y
a
n
d
sp
e
c
ifi
c
it
y
,
a
n
d
8
3
.
5
6
%
p
re
c
isio
n
.
Th
e
a
lg
o
rit
h
m
b
a
se
d
o
n
a
h
y
b
ri
d
CN
N
-
LS
TM
d
e
m
o
n
stra
tes
n
o
tab
le
e
ffic
a
c
y
in
a
u
to
n
o
m
o
u
sly
d
iag
n
o
si
n
g
AMI
a
n
d
d
e
term
in
in
g
t
h
e
l
o
c
a
ti
o
n
o
f
M
I
fr
o
m
ECG
s.
K
ey
w
o
r
d
s
:
C
las
s
if
icatio
n
Dee
p
lear
n
in
g
E
lectr
o
ca
r
d
io
g
r
am
My
o
ca
r
d
ial
in
f
a
r
ctio
n
Sin
g
le
-
lead
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
An
n
is
a
Dar
m
awa
h
y
u
n
i
I
n
tellig
en
t Sy
s
tem
s
R
esear
ch
Gr
o
u
p
,
Facu
lty
o
f
C
o
m
p
u
ter
S
cien
ce
,
Un
iv
er
s
itas
Sriwijay
a
Palem
b
an
g
,
3
0
1
3
9
,
I
n
d
o
n
esia
E
m
ail:
r
is
et.
an
n
is
ad
ar
m
awa
h
y
u
n
i@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
My
o
ca
r
d
ial
in
f
a
r
ctio
n
(
MI
)
,
c
o
m
m
o
n
l
y
k
n
o
wn
as
a
h
ea
r
t
at
tack
,
h
ap
p
e
n
s
wh
en
th
e
f
lo
w
o
f
o
x
y
g
en
-
r
ich
b
lo
o
d
to
a
s
ec
tio
n
o
f
th
e
h
ea
r
t
is
r
ed
u
ce
d
,
ca
u
s
in
g
d
am
ag
e
o
r
d
ea
th
to
th
at
p
ar
t
o
f
th
e
h
ea
r
t
[
1
]
–
[
3
]
.
T
h
is
co
n
d
itio
n
is
p
r
ed
o
m
i
n
an
tly
ca
u
s
ed
b
y
c
o
r
o
n
a
r
y
ar
te
r
y
d
is
ea
s
e,
also
r
ef
er
r
ed
t
o
as
co
r
o
n
ar
y
h
ea
r
t
d
is
ea
s
e.
T
h
e
p
r
im
ar
y
r
is
k
f
ac
to
r
s
f
o
r
th
is
d
is
ea
s
e
in
clu
d
e
an
u
n
h
ea
lth
y
d
iet,
lack
o
f
p
h
y
s
ical
ac
tiv
ity
,
to
b
ac
c
o
u
s
e,
an
d
ex
ce
s
s
iv
e
alco
h
o
l
co
n
s
u
m
p
tio
n
.
T
o
d
etec
t
MI
test
s
s
u
ch
as
t
h
e
elec
tr
o
ca
r
d
io
g
r
am
(
E
C
G)
a
n
d
ca
r
d
iac
en
zy
m
e
test
s
ar
e
u
s
ed
.
Ho
wev
er
,
ca
r
d
i
ac
en
zy
m
es
ca
n
o
n
ly
b
e
d
etec
t
ed
s
ev
er
al
h
o
u
r
s
a
f
ter
th
e
attac
k
an
d
m
ay
p
r
o
v
id
e
in
ac
cu
r
ate
r
esu
lts
if
test
ed
to
o
s
o
o
n
.
C
o
n
v
er
s
ely
,
E
C
G
o
f
f
er
s
q
u
ick
er
r
esu
lts
,
f
ac
ilit
atin
g
ea
r
ly
in
ter
v
en
tio
n
b
ef
o
r
e
f
u
r
th
e
r
test
s
ar
e
co
n
d
u
c
ted
[
4
]
–
[
6
]
.
An
E
C
G
is
a
d
ev
ice
th
at
m
ea
s
u
r
es
th
e
h
ea
r
t'
s
elec
tr
ic
al
ac
tiv
ity
.
C
ar
d
io
lo
g
is
ts
ca
n
id
en
tify
ab
n
o
r
m
alities
in
ce
r
tain
a
r
ea
s
o
f
th
e
h
ea
r
t
b
y
an
aly
zin
g
th
e
elec
tr
ical
ac
tiv
ity
th
r
o
u
g
h
th
e
h
ea
r
t
m
u
s
cle
[
3
]
.
T
h
e
P
wav
e
is
g
en
er
ated
b
y
th
e
s
in
o
atr
ial
n
o
d
e,
th
e
h
ea
r
t'
s
p
ac
em
ak
er
,
an
d
in
d
icate
s
atr
ial
d
ep
o
lar
izatio
n
o
r
co
n
tr
ac
tio
n
.
T
h
e
QR
S
co
m
p
lex
r
ep
r
esen
ts
th
e
atr
io
v
e
n
tr
icu
l
ar
n
o
d
e
a
n
d
s
h
o
ws
v
en
tr
icu
la
r
d
ep
o
lar
izatio
n
o
r
co
n
tr
ac
tio
n
,
wh
ile
th
e
T
wav
e
in
d
icate
s
v
en
tr
icu
lar
r
elax
atio
n
o
r
r
ep
o
lar
izatio
n
[
7
]
,
[
8
]
.
Du
r
in
g
an
MI
,
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
A
d
ee
p
lea
r
n
in
g
-
b
a
s
ed
myo
ca
r
d
ia
l in
fa
r
ctio
n
cla
s
s
ifica
tio
n
b
a
s
ed
o
n
s
in
g
le
-
lea
d
…
(
A
n
n
is
a
Da
r
ma
w
a
h
yu
n
i
)
353
E
C
G
m
ay
s
h
o
w
a
p
r
o
lo
n
g
ed
ST
in
ter
v
al,
ST
-
s
eg
m
en
t
elev
atio
n
o
r
d
ep
r
ess
io
n
,
an
d
ch
an
g
es
in
th
e
T
wav
e
s
h
ap
es.
T
h
e
ST
s
eg
m
en
t
b
e
g
i
n
s
at
th
e
J
p
o
in
t,
wh
ich
f
o
llo
w
s
th
e
S
wav
e,
an
d
en
d
s
at
th
e
o
n
s
et
o
f
th
e
T
wav
e
[
9
]
,
[
1
0
]
.
Ma
n
u
al
an
aly
s
is
o
f
E
C
G
r
esu
l
ts
b
y
ca
r
d
io
l
o
g
is
ts
is
b
o
th
tim
e
-
co
n
s
u
m
in
g
an
d
p
r
o
n
e
to
er
r
o
r
s
.
Ma
n
y
attem
p
ts
h
av
e
r
ec
en
tly
b
ee
n
m
ad
e
to
u
s
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
els
to
au
to
m
atica
lly
d
e
tect
MI
f
r
o
m
E
C
G
s
ig
n
als
[
1
1
]
.
Desp
ite
th
eir
s
tr
o
n
g
p
er
f
o
r
m
an
ce
in
MI
p
r
ed
ictio
n
,
th
ese
m
ac
h
in
e
l
ea
r
n
in
g
tech
n
i
q
u
es
n
ec
ess
itate
h
an
d
cr
af
ted
f
ea
tu
r
e
ex
tr
ac
tio
n
,
wh
ich
is
ex
tr
em
ely
en
g
in
ee
r
in
g
-
in
ten
s
iv
e
an
d
s
ig
n
if
ican
tly
r
elies
o
n
h
u
m
a
n
k
n
o
wled
g
e
f
o
r
m
a
n
u
al
p
ar
am
eter
twea
k
in
g
.
An
E
C
G
s
ig
n
al'
s
n
u
m
er
o
u
s
ch
a
r
ac
ter
is
tics
ca
n
b
e
au
to
m
atica
lly
class
if
ied
b
y
a
d
ee
p
lear
n
in
g
alg
o
r
ith
m
.
C
lass
if
y
in
g
m
u
ltil
ea
d
o
r
s
in
g
le
-
lead
E
C
G
d
ata
to
au
to
m
atica
lly
d
iag
n
o
s
e
p
r
o
b
l
em
s
lik
e
atr
ial
f
ib
r
illati
o
n
,
h
y
p
er
tr
o
p
h
ic
ca
r
d
io
m
y
o
p
ath
y
,
an
em
ia,
an
d
o
t
h
er
ailm
en
ts
is
o
n
e
u
s
e
o
f
d
ee
p
le
ar
n
in
g
in
th
e
m
e
d
ical
d
o
m
ain
[
1
2
]
,
[
1
3
]
.
I
n
o
r
d
er
to
au
to
m
at
ically
d
iag
n
o
s
e
MI
,
th
e
g
o
al
o
f
th
is
s
tu
d
y
was
to
cr
ea
te
a
h
y
b
r
id
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
,
an
d
ass
ess
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
.
I
n
o
r
d
e
r
t
o
f
in
d
r
e
g
io
n
al
p
atter
n
s
with
in
th
e
co
n
v
o
lu
tio
n
win
d
o
w,
C
NNs
ca
n
ex
tr
ac
t
l
o
ca
l
f
ea
tu
r
es
f
r
o
m
th
e
E
C
G
s
ig
n
al
s
er
ies.
T
h
r
o
u
g
h
weig
h
t
-
s
h
ar
in
g
,
th
e
C
NN
co
n
v
o
l
u
tio
n
lay
e
r
m
a
k
es
it
p
o
s
s
ib
le
to
ex
tr
ac
t
an
d
lear
n
lo
w
-
lev
el
h
ier
ar
ch
ical
an
d
in
v
a
r
ian
t
ch
ar
ac
ter
is
tics
f
r
o
m
u
n
p
r
o
ce
s
s
ed
d
ata
[
1
4
]
,
[
1
5
]
.
W
e
also
s
u
g
g
est
th
e
L
STM
ar
ch
itectu
r
e
as
a
cl
ass
if
ier
.
B
y
u
s
in
g
m
u
ltip
licativ
e
g
ates
to
k
ee
p
a
s
tead
y
er
r
o
r
f
lo
w
th
r
o
u
g
h
t
h
e
in
ter
n
al
s
tates
o
f
m
em
o
r
y
ce
ll
s
,
L
STM
,
a
k
in
d
o
f
r
ec
u
r
r
en
t
n
etwo
r
k
,
s
o
lv
es
th
e
g
r
ad
ien
t
is
s
u
e
th
at
ar
is
es
in
r
ec
u
r
r
en
t
n
e
u
r
al
n
etw
o
r
k
s
(
R
NNs).
L
o
n
g
-
ter
m
d
ep
en
d
e
n
cies in
E
C
G
s
eq
u
en
c
es h
av
e
b
ee
n
s
u
cc
ess
f
u
lly
ca
p
tu
r
ed
b
y
L
STM
[
1
6
]
,
[
1
7
]
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
2
.
1
.
Da
t
a
prepa
ra
t
i
o
n
T
h
e
PTB
-
XL
d
atab
as
e,
th
e
b
i
g
g
est
p
u
b
licly
av
ailab
le
elec
tr
o
ca
r
d
io
g
r
ap
h
y
d
ataset
to
d
ate,
was
ju
s
t
m
ad
e
av
ailab
le
f
o
r
u
s
e
in
th
is
in
v
esti
g
atio
n
[
1
8
]
.
W
ith
a
to
ta
l
o
f
1
8
,
8
8
5
d
if
f
er
e
n
t
p
atien
ts
'
1
0
-
s
ec
o
n
d
,
1
2
-
lead
E
C
Gs
ar
e
in
clu
d
ed
i
n
th
e
d
ata
b
ase,
f
o
r
a
to
tal
o
f
2
1
,
8
3
7
en
tr
ies.
Of
th
ese
r
ec
o
r
d
s
,
5
,
4
8
6
b
e
lo
n
g
to
MI
p
atien
ts
an
d
9
,
5
2
8
b
elo
n
g
to
h
ea
lth
y
c
o
n
tr
o
ls
(
HC
)
.
MI
r
ec
o
r
d
s
co
n
tain
ed
eig
h
t
s
u
b
-
MI
,
i.e
.
,
ac
u
t
e
lef
t
MI
(
AL
MI
)
,
ac
u
te
MI
(
AM
I
)
,
an
ter
o
s
ep
ta
l
MI
(
ASMI
)
,
im
p
e
n
d
in
g
lef
t
MI
(
I
L
MI
)
,
im
p
en
d
in
g
M
I
/in
f
er
io
r
MI
(
I
MI
)
,
is
o
lated
p
o
s
ter
io
r
lef
t
MI
(
I
PL
MI
)
,
is
o
lated
p
o
s
ter
io
r
MI
(
I
P
MI
)
,
an
d
lef
t
MI
(
L
MI
)
.
T
h
e
r
ec
o
r
d
s
ar
e
o
f
f
e
r
ed
in
two
f
o
r
m
ats
with
v
ar
y
in
g
s
am
p
lin
g
f
r
eq
u
en
cies:
5
0
0
an
d
1
0
0
Hz.
T
h
e
5
0
0
Hz
f
iles
ar
e
d
o
wn
s
am
p
led
,
an
d
th
e
r
ec
o
r
d
s
ar
e
k
ep
t in
wav
ef
o
r
m
d
atab
ase
(
W
FDB
)
f
o
r
m
at
with
a
r
eso
lu
tio
n
o
f
1
µV
/LSB
.
T
ab
le
1
co
n
tain
s
a
lis
t
o
f
all
th
e
r
ec
o
r
d
s
th
at
wer
e
ex
p
er
im
e
n
ted
with
.
Fig
u
r
e
1
p
r
esen
t
s
th
e
s
am
p
le
r
ec
o
r
d
s
o
f
HC
(
Fig
u
r
e
1
(
a)
)
an
d
MI
(
Fig
u
r
e
1
(
b
)
)
.
T
ab
le
1
.
T
h
e
ex
p
e
r
im
en
ted
r
ec
o
r
d
s
o
f
t
h
e
PTB
-
XL
d
atab
ase
C
l
a
s
s
R
e
c
o
r
d
s
HC
9
,
5
2
8
MI
A
LM
I
1
6
3
A
M
I
2
9
0
A
S
M
I
1
,
8
8
3
I
LM
I
3
9
3
I
M
I
2
,
3
2
9
I
P
LM
I
50
I
P
M
I
30
LM
I
1
3
2
To
t
a
l
1
4
,
7
9
8
(
a)
(
b
)
Fig
u
r
e
1
.
T
h
e
s
am
p
le
r
ec
o
r
d
s
o
f
(
a)
HC
an
d
(
b
)
MI
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
:
3
5
2
-
360
354
2
.
2
.
E
lect
ro
c
a
rdio
g
ra
m
pre
-
pro
ce
s
s
ing
E
C
G
s
ig
n
als
ca
n
b
ec
o
m
e
co
r
r
u
p
ted
d
u
r
in
g
ac
q
u
is
itio
n
d
u
e
t
o
v
ar
io
u
s
ar
tifa
cts
an
d
in
ter
f
e
r
en
ce
s
s
u
ch
as
m
u
s
cle
co
n
tr
ac
tio
n
,
b
aselin
e
d
r
if
t,
elec
tr
o
d
e
c
o
n
tact
n
o
is
e,
an
d
p
o
wer
lin
e
in
ter
f
e
r
en
ce
[
1
9
]
–
[
2
1
]
.
B
ec
au
s
e
it
ca
n
s
p
lit
an
E
C
G
s
ig
n
al
in
t
o
s
ev
er
al
f
r
e
q
u
en
c
y
b
a
n
d
s
an
d
ef
f
ec
tiv
ely
r
ep
r
esen
t
n
o
n
-
s
tatio
n
ar
y
s
ig
n
als,
th
e
d
is
cr
ete
wav
elet
tr
an
s
f
o
r
m
(
DW
T
)
is
f
r
eq
u
en
tly
u
s
ed
f
o
r
p
r
e
p
r
o
ce
s
s
in
g
E
C
G
s
ig
n
a
ls
(
n
o
is
e
r
em
o
v
al)
[
1
9
]
–
[
2
1
]
.
T
h
e
d
is
cr
ete
in
p
u
t
s
ig
n
al
is
p
as
s
ed
th
r
o
u
g
h
a
n
u
m
b
er
o
f
lo
w
-
p
ass
an
d
h
ig
h
-
p
a
s
s
f
ilter
s
in
o
r
d
er
f
o
r
th
e
DW
T
to
f
u
n
ctio
n
.
W
ith
wav
elet
co
ef
f
icien
ts
d
ictatin
g
t
h
e
n
u
m
b
e
r
o
f
d
ec
o
m
p
o
s
itio
n
le
v
els
f
o
r
a
s
er
ies
o
f
s
ig
n
al
p
r
o
ce
s
s
in
g
p
r
o
ce
d
u
r
es,
it
an
aly
ze
s
s
ig
n
als
at
v
ar
io
u
s
r
eso
lu
tio
n
lev
els.
T
h
e
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SNR
)
,
wh
ich
o
f
f
e
r
s
d
etails
o
n
s
ig
n
a
l
q
u
ality
,
is
u
s
ed
to
g
a
u
g
e
h
o
w
ef
f
ec
tiv
e
d
e
n
o
is
in
g
is
.
T
h
e
SNR
r
esu
lts
ar
e
d
is
p
lay
ed
in
T
a
b
le
2
.
Acc
o
r
d
i
n
g
to
th
e
r
esu
lts
,
b
io
r
1
.
3
h
as t
h
e
g
r
ea
test
SNR
r
atin
g
,
m
ea
s
u
r
in
g
1
2
.
9
6
2
d
B
.
T
h
is
s
tu
d
y
h
as
b
ala
n
ce
d
th
e
am
p
litu
d
e
r
a
n
g
e
f
o
r
co
m
p
u
t
atio
n
al
ef
f
icien
cy
af
ter
r
em
o
v
in
g
E
C
G
n
o
is
e.
Usi
n
g
o
n
e
o
f
th
e
p
r
o
c
ess
in
g
s
u
b
p
ac
k
ag
es
th
at
in
clu
d
es
W
FD
B
s
ig
n
al
-
p
r
o
ce
s
s
in
g
to
o
ls
f
o
r
r
ea
d
in
g
,
wr
itin
g
,
an
d
p
r
o
ce
s
s
in
g
W
FDB
s
ig
n
als
an
d
an
n
o
tatio
n
s
,
w
e
ap
p
lied
a
n
o
r
m
aliza
tio
n
b
o
u
n
d
.
B
y
s
ettin
g
th
e
lo
wer
lim
it
to
ze
r
o
an
d
th
e
u
p
p
er
lim
it
to
o
n
e,
th
e
v
al
u
es
o
f
th
e
s
ig
n
al
d
ata
wer
e
m
o
d
if
ied
to
f
all
in
s
id
e
a
p
r
ed
eter
m
in
e
d
r
an
g
e.
T
h
e
s
ig
n
al
len
g
th
o
f
a
n
E
C
G
s
ig
n
al
is
1
,
0
0
0
n
o
d
es.
T
h
e
E
C
G
s
ig
n
als
h
av
e
b
ee
n
d
iv
id
e
d
in
to
4
0
0
n
o
d
es
in
o
r
d
er
to
d
ete
r
m
in
e
f
ix
le
n
g
th
as sh
o
wn
i
n
Fig
u
r
e
2.
T
ab
le
2
.
T
h
e
r
esu
lts
o
f
av
e
r
ag
ed
SNR
W
a
v
e
l
e
t
f
u
n
c
t
i
o
n
S
N
R
v
a
l
u
e
(
a
v
e
r
a
g
e
d
)
sy
m
5
1
1
.
3
1
2
sy
m
6
1
1
.
1
8
5
sy
m
7
1
2
.
7
1
0
sy
m
8
1
1
.
5
5
2
d
b
2
1
0
.
5
6
0
d
b
4
1
1
.
6
3
5
d
b
5
1
2
.
9
1
1
d
b
6
1
1
.
7
8
6
d
b
7
1
1
.
6
6
2
b
i
o
r
1
.
3
1
2
.
9
6
2
b
i
o
r
6
.
8
1
1
.
6
4
4
h
a
a
r
1
1
.
6
6
2
Fig
u
r
e
2
.
T
h
e
s
eg
m
en
ted
E
C
G
s
ig
n
als in
to
4
0
0
n
o
d
es
2
.
3
.
A
hy
brid
co
nv
o
lutio
na
l
neura
l net
wo
rk
a
nd
lo
ng
s
ho
rt
-
t
er
m m
e
m
o
ry
A
o
n
e
-
d
im
en
s
io
n
al
(
1
D
)
-
C
N
N
is
a
ty
p
e
o
f
C
NN
s
p
ec
if
ica
lly
d
esig
n
ed
to
p
r
o
ce
s
s
o
n
e
-
d
im
en
s
io
n
al
d
ata,
s
u
ch
as tim
e
s
er
ie
s
o
r
s
e
q
u
en
ce
s
[
2
2
]
–
[
2
4
]
.
Un
lik
e
th
e
m
o
r
e
co
m
m
o
n
two
-
d
im
e
n
s
io
n
al
(
2
D
)
-
C
NNs u
s
ed
f
o
r
im
ag
es,
1
D
-
C
NNs
ar
e
p
ar
t
icu
lar
ly
ef
f
ec
tiv
e
f
o
r
task
s
in
v
o
lv
in
g
s
eq
u
en
tial
d
ata.
I
n
1
D
-
C
NNs,
f
ilter
s
s
lid
e
o
v
er
th
e
i
n
p
u
t
d
ata
i
n
o
n
e
d
im
en
s
io
n
,
ty
p
ically
alo
n
g
th
e
tim
e
ax
is
.
T
h
ese
f
ilter
s
d
etec
t
p
att
er
n
s
s
u
ch
as
tr
en
d
s
o
r
p
er
io
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1
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
A
d
ee
p
lea
r
n
in
g
-
b
a
s
ed
myo
ca
r
d
ia
l in
fa
r
ctio
n
cla
s
s
ifica
tio
n
b
a
s
ed
o
n
s
in
g
le
-
lea
d
…
(
A
n
n
is
a
Da
r
ma
w
a
h
yu
n
i
)
357
A
co
n
f
u
s
io
n
m
atr
ix
(
C
M)
is
a
to
o
l
u
s
ed
in
m
ac
h
in
e
l
ea
r
n
in
g
an
d
s
tatis
tic
s
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
a
class
if
icatio
n
alg
o
r
ith
m
[
2
8
]
,
[
2
9
]
.
I
t
p
r
o
v
id
es
a
s
u
m
m
a
r
y
o
f
th
e
p
r
e
d
ictio
n
r
esu
lts
o
n
a
class
if
icatio
n
p
r
o
b
lem
,
s
h
o
wi
n
g
th
e
n
u
m
b
e
r
o
f
c
o
r
r
ec
t
a
n
d
in
co
r
r
ec
t
p
r
ed
ictio
n
s
b
r
o
k
en
d
o
wn
b
y
ea
c
h
class
.
T
h
is
allo
ws
f
o
r
a
d
etailed
a
n
a
ly
s
is
o
f
h
o
w
well
t
h
e
class
if
ier
is
p
er
f
o
r
m
in
g
.
A
C
M
f
o
r
a
b
in
ar
y
class
if
icatio
n
p
r
o
b
lem
is
ty
p
ica
lly
a
2
×
2
tab
le,
b
u
t
it
ca
n
b
e
ex
te
n
d
ed
to
an
N
×
N
tab
le
f
o
r
m
u
lti
-
class
cla
s
s
if
icatio
n
p
r
o
b
lem
s
.
As
p
r
esen
ted
in
Fig
u
r
e
3
,
th
er
e
a
r
e
2
0
5
an
d
5
3
1
m
is
class
if
ied
as
HC
an
d
MI
.
T
h
is
is
b
ec
au
s
e
th
er
e
ar
e
s
u
b
-
MI
as
r
ep
r
esen
ted
as M
I
.
T
h
e
h
ea
r
t
r
ec
eiv
es
n
o
u
r
is
h
m
e
n
t
f
r
o
m
s
ev
er
al
ar
ter
ies,
m
ak
in
g
it
p
o
s
s
ib
le
f
o
r
MI
to
h
a
p
p
en
i
n
v
a
r
io
u
s
r
eg
i
o
n
s
.
I
f
th
e
b
lo
o
d
s
u
p
p
ly
to
a
n
y
o
f
th
ese
ar
ea
s
is
in
ter
r
u
p
ted
,
th
e
elec
tr
ical
ac
tiv
ity
o
f
th
e
m
u
s
cle
f
ib
er
s
in
th
at
r
e
g
io
n
b
eg
i
n
s
to
alter
.
T
h
e
s
p
ec
if
ic
alter
atio
n
s
o
b
s
er
v
ed
in
E
C
G
r
ec
o
r
d
in
g
s
v
ar
y
b
ased
o
n
th
e
elec
tr
o
d
es u
tili
ze
d
.
Fig
u
r
e
3
.
T
h
e
h
ea
tm
a
p
C
M
o
f
HC
an
d
MI
class
if
icatio
n
4.
CO
NCLU
SI
O
N
MI
is
an
in
ju
r
y
to
th
e
h
ea
r
t
m
u
s
cle
b
r
o
u
g
h
t
o
n
b
y
a
th
r
o
m
b
u
s
o
b
s
tr
u
ctin
g
th
e
c
o
r
o
n
a
r
y
ar
ter
ies,
wh
ich
s
to
p
s
b
lo
o
d
f
lo
w.
I
f
th
is
co
n
d
itio
n
is
n
o
t
tr
ea
te
d
q
u
ick
l
y
to
r
e
o
p
en
th
e
c
o
r
o
n
a
r
y
a
r
ter
y
v
ia
p
e
r
cu
tan
e
o
u
s
o
r
s
u
r
g
ical
p
r
o
ce
d
u
r
es,
it
m
ay
r
esu
lt
in
ir
r
ev
er
s
ib
le
d
a
m
ag
e,
in
clu
d
in
g
m
y
o
ca
r
d
ial
tis
s
u
e
d
ea
th
.
T
h
er
ef
o
r
e,
in
o
r
d
er
to
a
v
o
id
c
o
m
p
licatio
n
s
lik
e
ca
r
d
iac
f
ailu
r
e,
ar
r
h
y
t
h
m
ia,
an
d
d
ea
th
,
ea
r
ly
d
etec
tio
n
an
d
d
iag
n
o
s
is
ar
e
ess
en
tial.
E
C
G
i
s
co
m
m
o
n
ly
u
s
ed
to
d
iag
n
o
s
e
ac
u
te
MI
,
al
th
o
u
g
h
it
is
s
en
s
itiv
e
to
in
ter
-
o
b
s
er
v
er
v
ar
iab
ilit
y
an
d
r
eq
u
ir
es
ex
p
er
t
in
ter
p
r
etatio
n
.
Ma
n
u
ally
a
n
aly
zin
g
E
C
G
d
ata
b
y
a
ca
r
d
io
l
o
g
is
t
tak
es
a
lo
t
o
f
tim
e
an
d
is
er
r
o
r
-
p
r
o
n
e.
A
d
ee
p
lear
n
in
g
m
eth
o
d
th
at
ca
n
au
t
o
m
atica
lly
r
ec
o
g
n
ize
a
n
d
ca
te
g
o
r
ize
a
v
a
r
iety
o
f
E
C
G
s
ig
n
al
p
r
o
p
er
ties
is
p
r
o
p
o
s
ed
i
n
t
h
is
s
tu
d
y
.
A
h
y
b
r
i
d
C
NN
-
L
STM
b
ased
o
n
a
d
ee
p
lear
n
in
g
alg
o
r
ith
m
was
p
r
o
p
o
s
e
d
in
th
is
p
a
p
er
.
T
o
g
en
e
r
ate
th
e
h
y
b
r
id
C
NN
-
L
STM
m
o
d
el,
we
p
r
o
p
o
s
ed
3
9
m
o
d
els
wit
h
h
y
p
er
p
ar
am
eter
tu
n
in
g
.
As
a
r
esu
lt,
th
e
b
est
m
o
d
el
is
m
o
d
el
3
5
h
as
8
6
.
8
6
%
ac
cu
r
ac
y
,
7
5
.
2
8
%
s
en
s
itiv
ity
an
d
s
p
ec
if
icity
,
an
d
8
3
.
5
6
%
p
r
ec
is
io
n
.
T
h
e
alg
o
r
ith
m
b
ased
o
n
a
h
y
b
r
id
C
NN
-
L
STM
d
em
o
n
s
tr
ates
n
o
tab
le
ef
f
icac
y
in
au
to
n
o
m
o
u
s
ly
d
iag
n
o
s
in
g
AM
I
an
d
d
eter
m
in
in
g
th
e
lo
ca
tio
n
o
f
MI
f
r
o
m
E
C
Gs.
ACK
NO
WL
E
DG
E
M
E
NT
S
W
e
th
an
k
th
e
I
n
tellig
en
t
S
y
s
tem
R
esear
ch
G
r
o
u
p
(
I
Sy
s
R
G)
,
Facu
lty
o
f
C
o
m
p
u
ter
Scien
ce
,
Un
iv
er
s
itas
Sriwijay
a
,
I
n
d
o
n
e
s
ia.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
was
f
u
n
d
ed
b
y
t
h
e
I
n
tellig
en
t
Sy
s
tem
R
esear
ch
Gr
o
u
p
,
Facu
lty
o
f
C
o
m
p
u
te
r
Scien
ce
,
Un
iv
er
s
itas
Sriwijay
a,
I
n
d
o
n
esia.
T
h
e
f
u
n
d
in
g
b
o
d
y
h
as
p
lay
e
d
a
r
o
le
in
th
e
d
esig
n
o
f
th
e
s
tu
d
y
an
d
co
llectio
n
,
an
aly
s
is
an
d
in
te
r
p
r
etatio
n
o
f
d
ata,
an
d
i
n
wr
itin
g
th
e
m
an
u
s
cr
ip
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
14
,
No
.
2
,
J
u
n
e
2
0
2
5
:
3
5
2
-
360
358
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT
)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
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f
Aut
ho
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So
Va
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Vi
Su
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a
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W
in
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Nu
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l A
f
if
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✓
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B
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✓
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Sit
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u
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m
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✓
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J
o
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d
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Ma
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✓
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R
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C
h
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lid
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RE
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NC
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[
1
]
X
.
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l
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[
6
]
S
.
M
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.
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