I
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s
ia
n J
o
urna
l o
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E
lect
rica
l En
g
ineering
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Co
m
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Science
Vo
l.
3
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.
3
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b
er
2
0
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5
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p
p
.
1
9
38
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1
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45
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SS
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2
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1
1
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1
/ijeecs.v
3
9
.i
3
.
pp
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38
-
1
9
45
1938
J
o
ur
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g
e
:
h
ttp
:
//ij
ee
cs.ia
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m
Exploring
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pa
ct
o
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rti
ficial i
ntelligence
driven
so
lutions
o
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s
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n
o
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p
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a
c
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te
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ti
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o
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r
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s
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ti
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t.
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is
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ted
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h
a
t
a
rti
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l
i
n
telli
g
e
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c
e
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h
a
s
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e
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g
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o
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ti
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l
c
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re
ss
th
is
sta
te
o
f
d
iag
n
o
sis
e
m
e
rg
e
n
c
y
.
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c
u
rre
n
t
e
ra
o
f
re
se
a
rc
h
wo
rk
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th
e
re
h
a
s
b
e
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v
a
rio
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s
imp
l
e
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e
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o
d
e
l
a
n
d
re
v
iew
wo
rk
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s
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e
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n
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t
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rd
s
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ti
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g
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o
r
d
e
term
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in
g
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se
t
o
f
c
a
rd
iac
a
rre
st;
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o
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v
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r,
th
e
re
a
r
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v
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rio
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s
co
n
trad
icti
o
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n
d
s
h
o
rtc
o
m
in
g
wh
ich
is
q
u
it
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c
h
a
ll
e
n
g
in
g
to
b
e
e
x
trac
ted
.
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n
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e
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e
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u
rre
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t
m
a
n
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sc
ri
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t
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n
ts
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m
e
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d
o
l
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g
y
b
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re
se
n
ti
n
g
c
o
re
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o
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o
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ies
o
f
re
c
e
n
t
AI
-
m
e
th
o
d
s
t
o
wa
rd
s
e
a
rly
d
e
tec
ti
o
n
o
f
c
a
rd
iac
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st.
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rio
u
s
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n
d
a
r
d
d
a
tas
e
t
h
a
s
b
e
e
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ied
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fin
d
a
ss
o
c
iate
d
a
d
v
a
n
tag
e
s
a
n
d
li
m
it
a
t
io
n
t
h
a
t
re
strict
th
e
a
c
tu
a
l
p
o
ten
ti
a
l
o
f
AI
to
p
re
d
ictio
n
.
Th
e
o
u
tc
o
m
e
p
re
se
n
ts
n
o
v
e
l
h
ig
h
li
g
h
ts
o
f
re
se
a
rc
h
g
a
p
,
trad
e
-
o
ff,
a
n
d
c
ris
p
h
ig
h
li
g
h
ts
o
f
e
ffe
c
ti
v
e
n
e
ss
o
f
e
x
isti
n
g
AI
a
p
p
ro
a
c
h
e
s
a
s
a
stu
d
y
c
o
n
tri
b
u
ti
o
n
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
C
ar
d
iac
ar
r
est
Data
s
et
Diag
n
o
s
is
Pre
d
ictio
n
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
:
T
ejash
r
ee
Ven
k
atesh
a
Dep
ar
tm
en
t o
f
C
o
m
p
u
ter
Scie
n
ce
an
d
E
n
g
in
ee
r
in
g
,
Pre
s
id
en
cy
Un
iv
er
s
ity
B
en
g
alu
r
u
,
Kar
n
atak
a
,
I
n
d
ia
E
m
ail:
tejash
r
ee
d
h
an
an
jay
@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
is
s
tu
d
y
p
er
tain
s
to
a
s
ev
er
e
m
ed
ical
em
e
r
g
en
c
y
k
n
o
wn
as
ca
r
d
iac
ar
r
est
lead
in
g
to
s
t
o
p
p
ag
e
o
f
cir
cu
latio
n
o
f
b
lo
o
d
in
h
u
m
an
b
o
d
y
.
I
t
ca
n
b
e
d
eter
m
in
ed
b
y
v
ar
i
o
u
s
s
tan
d
ar
d
s
y
m
p
to
m
s
v
iz.
s
ev
er
e
p
ain
in
ch
est,
n
o
b
r
ea
th
i
n
g
,
n
o
p
u
ls
e,
s
u
d
d
en
lo
s
s
o
f
co
n
s
cio
u
s
n
ess
.
T
h
e
s
tan
d
ar
d
tr
ea
tm
en
t
ex
er
cised
ar
e
ad
v
an
ce
d
ca
r
d
iac
life
s
u
p
p
o
r
t
s
y
s
tem
,
d
ef
ib
r
illatio
n
,
an
d
ca
r
d
io
p
u
lm
o
n
ar
y
r
esu
s
citatio
n
(
C
PR
)
.
Ho
wev
er
,
it
is
v
er
y
r
ar
e
ca
s
e
to
o
b
s
er
v
e
s
u
cc
ess
r
ate
o
f
ea
r
ly
p
r
ed
ictio
n
m
eth
o
d
in
m
ed
ical
s
cien
ce
as
it
d
em
an
d
s
d
o
m
in
a
n
tly
p
r
o
ac
tiv
e
m
ea
s
u
r
es.
Ar
tific
ial
in
tellig
en
ce
(
AI
)
,
wh
ich
is
ca
p
ab
le
o
f
s
o
lv
i
n
g
c
o
m
p
lex
r
ea
l
wo
r
ld
p
r
o
b
lem
,
h
as
s
ig
n
if
ican
t
co
n
tr
ib
u
ti
o
n
to
wa
r
d
s
v
ar
io
u
s
p
r
e
d
ictiv
e
an
d
s
o
p
h
is
ticated
an
aly
s
is
in
cr
itical
d
is
ea
s
es
[
1
]
.
I
n
p
er
s
p
ec
tiv
e
o
f
ca
r
d
iac
ar
r
est
,
elec
tr
o
ca
r
d
io
g
r
ap
h
d
ata
(
E
C
G)
ca
n
b
e
m
o
n
ito
r
ed
b
y
AI
to
d
eter
m
in
e
th
e
ab
n
o
r
m
alities
in
h
ea
r
t
r
ate
as
an
in
d
icato
r
s
to
war
d
s
ar
r
h
y
th
m
ias.
Var
io
u
s
ty
p
es
o
f
wea
r
ab
le
d
ev
ices
u
s
in
g
AI
ca
n
b
e
also
d
e
p
lo
y
e
d
to
war
d
s
tr
ac
k
in
g
v
ar
io
u
s
d
ir
ec
t
o
r
i
n
d
ir
ec
t
in
d
icato
r
s
o
r
v
ital
s
ig
n
al
in
r
ea
l
t
im
e
f
o
r
co
n
tin
u
o
u
s
m
o
n
ito
r
in
g
.
T
h
e
s
o
le
in
ten
tio
n
is
to
tr
ac
e
an
y
f
o
r
m
o
f
ea
r
ly
war
n
in
g
s
ig
n
.
AI
i
n
f
o
r
m
o
f
m
ac
h
in
e
lear
n
in
g
,
d
ee
p
lear
n
in
g
,
n
atu
r
a
l
lan
g
u
a
g
e
p
r
o
ce
s
s
in
g
(
NL
P)
c
an
b
e
s
ig
n
if
ican
tly
u
s
ed
f
o
r
an
aly
zin
g
b
io
s
ig
n
als
f
o
r
p
r
ed
ictin
g
th
e
s
tate
o
f
c
r
iticality
o
f
p
atien
t
alo
n
g
with
p
r
o
p
er
class
if
icatio
n
[
2
]
.
W
h
en
a
p
atien
t
is
ad
m
itte
d
to
h
o
s
p
ital,
AI
-
b
ased
m
o
n
ito
r
i
n
g
to
o
ls
ca
n
b
e
u
s
ed
f
o
r
r
ea
l
-
tim
e
tr
ac
k
in
g
o
f
all
th
e
tr
en
d
s
o
f
v
ital
s
ig
n
al
f
o
r
s
p
ee
d
in
g
u
p
t
h
e
p
r
o
ce
s
s
o
f
ea
r
ly
d
etec
tio
n
.
Ap
a
r
t
f
r
o
m
E
C
G
d
ata,
v
ar
io
u
s
o
th
er
f
o
r
m
o
f
in
p
u
t
d
ata
e.
g
.
r
ad
io
lo
g
ical
s
ca
n
d
ata
ca
n
b
e
also
u
s
ed
to
war
d
s
in
v
esti
g
atin
g
in
ter
n
al
p
r
o
b
lem
s
with
in
h
e
ar
t
f
o
r
d
eter
m
in
in
g
an
y
f
o
r
m
o
f
ca
r
d
io
v
ascu
lar
is
s
u
es.
Ad
o
p
tio
n
o
f
AI
al
g
o
r
ith
m
s
u
its
well
esp
ec
ially
in
th
e
ca
s
e
o
f
lar
g
e
d
ataset
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
xp
lo
r
in
g
th
e
imp
a
ct
o
f a
r
tifi
cia
l in
tellig
en
ce
d
r
iven
s
o
lu
tio
n
s
o
n
ea
r
ly
…
(
Teja
s
h
r
ee
V
en
ka
tesh
a
)
1939
to
war
d
s
co
n
s
tr
u
ctin
g
a
r
is
k
ass
ess
m
en
t
m
o
d
el
b
ased
o
n
im
ag
in
g
s
tu
d
ies,
lab
o
r
ato
r
y
r
esu
lts
,
v
ital
s
ig
n
als
an
d
m
ed
ical
h
is
to
r
y
.
Var
io
u
s
r
elate
d
wo
r
k
h
as b
ee
n
ca
r
r
ied
o
u
t i
n
th
is
r
eg
ar
d
to
u
n
d
er
s
tan
d
th
e
e
x
is
tin
g
co
n
tr
ib
u
tio
n
o
f
AI
in
ea
r
ly
d
etec
tio
n
o
f
ca
r
d
iac
ar
r
est.
T
h
e
wo
r
k
ca
r
r
ied
o
u
t
b
y
Alam
g
ir
et
a
l.
[
3
]
h
av
e
d
is
cu
s
s
ed
v
ar
io
u
s
ap
p
r
o
ac
h
es
o
f
AI
wh
er
e
m
ac
h
in
e
lear
n
in
g
a
p
p
r
o
ac
h
es
p
lay
e
d
a
d
o
m
in
an
t
r
o
le
in
p
r
ed
ictio
n
o
f
ca
r
d
iac
ar
r
est.
T
h
e
s
tu
d
y
b
y
Alm
an
s
o
u
r
i
et
a
l.
[
4
]
in
f
e
r
r
ed
b
etter
d
etec
ti
o
n
p
e
r
f
o
r
m
an
ce
c
o
u
ld
b
e
d
o
n
e
b
y
co
m
b
in
i
n
g
AI
with
im
ag
e
an
aly
s
is
an
d
y
et
it
d
em
an
d
s
m
o
r
e
v
alid
atio
n
,
wh
ich
is
cu
r
r
e
n
tly
m
is
s
in
g
in
ex
is
tin
g
s
tu
d
ies.
Su
n
et
a
l
.
[
5
]
ad
v
o
ca
ted
v
ar
io
u
s
s
co
p
e
o
f
AI
to
war
d
s
d
iag
n
o
s
in
g
ca
r
d
io
v
ascu
lar
d
is
ea
s
es
(
C
VD)
;
h
o
wev
er
,
au
th
o
r
s
s
tated
im
p
en
d
in
g
s
h
o
r
tco
m
in
g
s
o
f
AI
tech
n
o
l
o
g
y
to
b
e
o
v
e
r
co
m
e
d
.
Ho
lm
s
tr
o
m
et
a
l.
[
6
]
h
av
e
p
r
esen
ted
a
f
r
am
ewo
r
k
o
f
AI
to
war
d
s
ev
alu
atin
g
th
r
ea
t
o
f
ca
r
d
iac
ar
r
est
wh
er
e
th
e
au
th
o
r
s
h
av
e
u
s
ed
d
ee
p
lear
n
in
g
m
eth
o
d
.
Var
io
u
s
p
r
o
b
lem
s
an
d
ch
all
en
g
es
h
av
e
b
ee
n
en
co
u
n
ter
e
d
wh
ile
r
e
v
iewin
g
ex
is
tin
g
AI
-
b
ased
liter
atu
r
es
wh
ich
ar
e
as
f
o
llo
ws:
i)
ex
is
tin
g
AI
m
o
d
els
h
av
e
b
ee
n
im
p
lem
en
te
d
o
n
lim
ited
lab
eled
d
ata
wh
ich
af
f
ec
ts
th
e
d
ec
is
io
n
m
a
k
in
g
,
i
i)
a
p
er
f
ec
t
d
iag
n
o
s
tic
ap
p
r
o
a
ch
to
war
d
s
ea
r
ly
o
n
s
et
o
f
ca
r
d
iac
ar
r
est
d
em
an
d
s
its
s
y
s
tem
to
b
e
b
o
th
r
ea
l
-
tim
e
an
d
p
er
f
o
r
m
co
n
tin
u
o
u
s
m
o
n
ito
r
in
g
;
h
o
wev
er
,
th
er
e
is
a
s
ig
n
if
ican
t
tr
ad
e
-
o
f
f
b
etwe
en
th
em
,
iii)
ex
is
tin
g
AI
-
b
ased
s
o
lu
tio
n
to
war
d
s
a
d
ap
tab
ilit
y
an
d
g
en
er
aliza
tio
n
is
s
u
b
-
o
p
tim
ally
ac
co
m
p
lis
h
ed
o
win
g
to
n
o
n
-
in
clu
s
io
n
o
f
p
atien
t
v
ar
iab
ili
ty
an
d
en
v
ir
o
n
m
e
n
tal
f
ac
to
r
.
Ap
ar
t
f
r
o
m
t
h
is
,
ex
is
tin
g
r
ev
iew
wo
r
k
d
o
esn
’
t
o
f
f
er
clea
r
in
s
ig
h
t
o
f
co
r
e
tax
o
n
o
m
ies
o
f
r
ec
e
n
t
ap
p
r
o
ac
h
es
wh
ile
n
o
t
m
u
ch
co
m
p
ar
ativ
e
d
is
cu
s
s
io
n
is
ca
r
r
ied
o
u
t to
war
d
s
th
e
is
s
u
es p
er
tain
in
g
to
its
d
ataset.
T
h
e
r
ef
o
r
e,
th
e
g
o
al
o
f
t
h
e
p
r
o
p
o
s
ed
s
tu
d
y
is
to
p
r
esen
t
a
c
o
m
p
ac
t
an
d
p
o
in
t
-
to
-
p
o
in
t
d
is
cu
s
s
io
n
o
f
cu
r
r
en
t
s
tate
o
f
AI
b
ased
m
et
h
o
d
s
to
war
d
s
ea
r
ly
o
n
s
et
o
f
c
ar
d
iac
ar
r
est.
T
h
e
v
alu
e
-
ad
d
e
d
co
n
tr
i
b
u
tio
n
ar
e
as
f
o
llo
ws:
i)
th
e
s
tu
d
y
h
as
r
ev
i
ewe
d
an
d
p
r
esen
ted
co
r
e
tax
o
n
o
m
y
o
f
AI
-
b
ased
ap
p
r
o
ac
h
e
s
to
war
d
s
d
etec
tin
g
ca
r
d
iac
ar
r
est,
ii)
r
e
v
iewe
d
m
eth
o
d
s
h
a
v
e
b
ee
n
p
r
esen
ted
w
ith
r
esp
ec
t
to
th
eir
e
f
f
ec
tiv
en
e
s
s
an
d
lim
itatio
n
s
,
iii)
Var
io
u
s
d
ataset
ad
o
p
ted
to
war
d
s
cu
r
r
en
t
f
o
r
m
o
f
i
n
v
esti
g
atio
n
h
as
b
ee
n
p
r
esen
ted
with
al
l
th
eir
s
p
ec
if
icatio
n
an
d
is
s
u
es
as
w
ell,
iv
)
a
cr
is
p
d
is
cu
s
s
io
n
o
f
r
esear
ch
g
ap
an
d
tr
a
d
e
-
o
f
f
h
as
b
ee
n
p
r
esen
ted
to
u
n
d
er
s
tan
d
t
h
e
cu
r
r
en
t stan
ce
o
f
ex
is
tin
g
AI
m
et
h
o
d
o
lo
g
ies.
2.
M
E
T
H
O
D
A
d
esk
r
esear
ch
m
eth
o
d
o
lo
g
y
h
as
b
ee
n
u
s
ed
f
o
r
th
is
p
u
r
p
o
s
e
wh
er
e
m
an
u
s
cr
ip
ts
p
er
tain
in
g
to
d
etec
tio
n
o
f
ca
r
d
iac
ar
r
est
u
s
in
g
v
a
r
ied
AI
m
eth
o
d
o
lo
g
y
h
as
b
ee
n
c
o
llected
.
Fig
u
r
e
1
s
h
o
ws
th
at
in
itially
ab
s
tr
ac
t
s
cr
ee
n
in
g
h
as
b
ee
n
p
e
r
f
o
r
m
e
d
to
war
d
s
p
r
im
ar
y
f
ilte
r
in
g
wh
ich
also
ca
r
r
y
o
u
t
id
en
tific
atio
n
o
f
is
s
u
es.
Pre
s
en
ce
o
f
an
y
d
u
p
licated
s
tu
d
y
h
as
b
ee
n
elim
in
ate
d
f
o
llo
wed
b
y
p
er
f
o
r
m
in
g
s
ec
o
n
d
ar
y
f
ilter
in
g
wh
ich
co
n
s
is
ts
o
f
in
d
ep
t
h
s
tu
d
y
o
f
ac
tu
al
e
x
p
er
im
e
n
tal
d
etails
g
iv
en
in
t
h
e
f
ilter
e
d
m
a
n
u
s
cr
ip
t
with
r
esp
ec
t
to
ad
o
p
ted
r
esear
c
h
m
eth
o
d
an
d
ac
co
m
p
lis
h
ed
s
tu
d
y
o
u
tco
m
e.
I
n
clu
s
io
n
cr
iter
ia
u
s
ed
ar
e
o
n
l
y
AI
b
ased
jo
u
r
n
als
p
u
b
lis
h
ed
b
etwe
en
2
0
2
0
-
2
0
2
4
wh
ile
ex
clu
s
io
n
cr
iter
ia
is
an
y
co
n
f
e
r
en
ce
p
ap
er
s
o
r
p
a
p
er
s
with
o
u
t
d
etailed
in
f
o
r
m
atio
n
o
f
ad
o
p
ted
m
eth
o
d
s
,
d
ataset,
o
r
r
esu
lts
.
T
h
e
o
u
tco
m
e
o
f
ad
o
p
tin
g
th
is
m
eth
o
d
is
ex
tr
ac
ted
r
esear
c
h
g
ap
a
n
d
tr
a
d
e
-
o
f
f
th
at
ass
is
t
s
in
u
n
d
er
s
tan
d
in
g
th
e
ef
f
ec
tiv
en
ess
o
f
ex
is
tin
g
AI
-
m
o
d
els.
Fig
u
r
e
1
.
Ad
o
p
ted
m
eth
o
d
o
lo
g
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
38
-
1
9
45
1940
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
er
e
ar
e
v
ar
io
u
s
ty
p
es
o
f
AI
s
ch
em
es
tar
g
etin
g
to
war
d
s
ea
r
ly
d
iag
n
o
s
is
o
f
ca
r
d
iac
ar
r
est.
All
th
ese
r
ev
i
ewe
d
ap
p
r
o
ac
h
es
u
s
es
its
in
p
u
t
f
r
o
m
v
a
r
io
u
s
s
o
u
r
ce
s
,
w
h
ich
is
tr
an
s
f
o
r
m
ed
t
o
d
ig
itize
d
s
tate
f
o
llo
wed
b
y
ap
p
ly
in
g
d
if
f
er
en
t
f
o
r
m
s
o
f
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
es
(
as
s
h
o
wn
in
T
ab
le
1
)
.
I
r
r
esp
ec
tiv
e
o
f
d
if
f
er
e
n
t
m
eth
o
d
o
l
o
g
ies,
th
e
ag
en
d
a
o
f
th
ese
AI
a
p
p
r
o
ac
h
es
r
e
m
ain
s
s
am
e
wh
ich
is
to
d
eter
m
in
e
th
e
ea
r
ly
o
n
s
et
o
f
ca
r
d
iac
ar
r
est.
Fo
llo
win
g
ar
e
th
e
tax
o
n
o
m
ies
o
f
th
e
r
ev
i
ewe
d
AI
-
b
ased
ap
p
r
o
ac
h
es
r
ep
o
r
ted
ly
u
s
ed
f
o
r
id
en
tify
in
g
ea
r
ly
d
etec
tio
n
o
f
ca
r
d
iac
ar
r
est:
a)
An
aly
s
is
o
f
elec
tr
o
ca
r
d
io
g
r
a
m
(
E
C
G)
s
ig
n
al:
r
esear
ch
er
s
h
av
e
im
p
lem
en
te
d
d
ee
p
n
e
u
r
al
n
etwo
r
k
s
(
DNNs)
an
d
co
n
v
o
lu
tio
n
al
n
e
u
r
al
n
etwo
r
k
s
(
C
NNs
)
to
class
if
y
E
C
G
s
ig
n
als
in
r
ea
l
-
tim
e
(
Kr
astev
a
et
a
l
.
[
7
]
,
Ah
m
ed
et
a
l
.
[
8
]
,
Den
g
et
a
l
.
[
9
]
)
.
Ma
ch
in
e
lear
n
in
g
m
o
d
els
ca
n
s
ee
p
atter
n
s
in
E
C
G
d
ata
th
at
d
en
o
te
wh
e
n
a
p
atien
t
m
a
y
g
o
in
to
ca
r
d
iac
a
r
r
est.
T
h
ese
m
o
d
els
ar
e
tr
ain
ed
to
d
etec
t
s
u
b
tl
e
ar
r
h
y
t
h
m
ias
an
d
o
th
er
p
r
ec
u
r
s
o
r
s
to
a
ca
r
d
iac
ar
r
est.
L
o
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
n
etwo
r
k
s
h
as
also
b
ee
n
ex
ten
s
iv
ely
u
s
ed
in
th
is
r
eg
ar
d
s
esp
ec
ially
f
o
r
tim
e
-
s
er
ies
E
C
G
d
ata
an
aly
s
is
(
Z
ac
ar
ia
s
et
a
l
.
[
1
0
]
)
.
T
h
ese
n
etwo
r
k
s
ar
e
a
d
ep
t
at
le
ar
n
in
g
f
r
o
m
s
eq
u
en
tial
d
ata
an
d
ca
n
lear
n
to
p
r
e
d
ict
wh
en
a
ca
r
d
iac
ev
en
t
will h
ap
p
en
b
y
r
ec
o
g
n
izin
g
tim
e
-
d
ep
en
d
en
t p
atter
n
s
th
at
p
r
ec
ed
e
an
ar
r
est.
b)
W
ea
r
ab
le
d
ev
ice
b
ased
a
p
p
r
o
ac
h
es:
d
ev
ices
lik
e
s
m
ar
twatch
es
o
r
ch
est
s
tr
ap
s
th
at
ar
e
p
o
wer
ed
b
y
AI
h
av
e
s
en
s
o
r
s
th
at
tr
ac
k
h
ea
r
t
r
ate,
r
h
y
th
m
,
an
d
o
th
er
v
ital
s
ig
n
s
in
r
ea
l
tim
e
(
Alim
b
ay
ev
a
et
a
l
.
[
1
1
]
,
C
h
o
wd
h
u
r
y
et
a
l
.
[
1
2
]
)
.
On
s
o
m
e
o
cc
asio
n
s
th
ese
d
ev
ices
u
tili
ze
AI
alg
o
r
ith
m
s
to
d
etec
t
p
o
s
s
ib
le
ea
r
ly
s
ig
n
s
o
f
ca
r
d
iac
ar
r
est
o
r
s
ev
e
r
e
ar
r
h
y
th
m
ias
an
d
war
n
m
ed
ical
h
ea
lth
ca
r
e
team
s
o
r
ca
r
e
g
iv
er
s
.
T
h
ese
wea
r
ab
les
ar
e
em
p
o
wer
in
g
r
ea
l
-
tim
e
m
o
n
ito
r
in
g
o
f
h
e
ar
t
h
ea
lth
,
p
o
ten
tially
lead
i
n
g
to
ea
r
lier
in
ter
v
en
tio
n
s
th
at
ca
n
s
to
p
a
h
e
ar
t f
r
o
m
g
o
in
g
in
to
f
u
ll c
ar
d
i
ac
ar
r
est.
c)
Pre
d
ictiv
e
m
o
d
ellin
g
ap
p
r
o
ac
h
:
b
ased
o
n
lar
g
e
d
atasets
,
r
esear
ch
er
s
ar
e
u
s
in
g
m
ac
h
in
e
le
ar
n
in
g
m
o
d
els
to
p
r
e
d
ict
th
e
r
is
k
o
f
ca
r
d
iac
a
r
r
est
(
Ng
u
y
e
n
a
n
d
B
y
eo
n
[
1
3
]
,
Yu
et
a
l
.
[
1
4
]
,
Og
u
n
p
o
la
et
a
l
.
[
1
5
]
)
.
T
h
ese
m
o
d
els
ca
n
ass
es
s
th
e
r
is
k
f
o
r
an
o
n
co
m
in
g
h
ea
r
t
attac
k
b
y
ex
am
in
in
g
s
ev
er
al
d
ata
p
o
in
ts
ab
o
u
t
s
ev
er
al
p
atien
ts
(
d
em
o
g
r
ap
h
ic
d
ata
,
m
ed
ical
h
is
to
r
y
,
E
C
G
d
ata,
an
d
b
lo
o
d
p
r
ess
u
r
e)
.
Similar
ly
,
it
c
an
b
e
tr
ain
ed
o
n
p
r
ev
i
o
u
s
ca
s
e
d
ata
to
h
ig
h
lig
h
t
u
n
d
er
ly
i
n
g
p
atter
n
s
th
at
m
ay
s
er
v
e
as
ea
r
ly
in
d
icato
r
s
o
f
a
p
o
ten
tial
ca
r
d
iac
ev
en
t.
d)
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o
in
t
o
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s
ig
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d
d
ata
p
r
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ce
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s
in
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in
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tech
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q
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ality
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E
C
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s
o
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eh
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l
.
[
1
6
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Pin
to
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l
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[
1
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.
AI
m
o
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[
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2
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ch
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r
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l
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[
2
3
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lv
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l
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[
2
4
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ts
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tim
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T
ab
le
1
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Su
m
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p
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Evaluation Warning : The document was created with Spire.PDF for Python.
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3
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3
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2
.
I
dentif
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a
p
Af
ter
r
ev
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th
e
ex
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g
AI
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ap
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s
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ly
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to
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lex
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ciate
d
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th
e
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n
v
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al
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All
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esear
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s
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tifie
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ar
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as
f
o
llo
ws:
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Few
av
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o
f
lab
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d
ata:
f
r
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m
p
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s
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in
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I
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ab
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est ca
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o
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r
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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5
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Sep
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20
25
:
1
9
38
-
1
9
45
1942
-
Op
p
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r
tu
n
ity
:
th
er
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is
a
d
em
a
n
d
o
f
co
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o
n
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lab
o
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ato
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d
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s
to
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m
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ized
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d
la
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elled
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ataset.
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e
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iased
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im
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alan
ce
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ataset:
alm
o
s
t
m
ajo
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ity
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f
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p
lo
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u
f
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er
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im
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alan
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ata
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f
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f
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h
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n
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itio
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d
m
u
ch
less
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en
t
o
f
ca
r
d
i
ac
ar
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est.
T
h
is
ev
en
tu
ally
r
esu
lts
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iasi
n
g
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d
s
u
b
-
o
p
tim
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ed
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a
n
ce
o
f
AI
m
o
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els
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w
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o
f
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clu
s
io
n
o
f
r
ar
e
o
r
u
n
d
er
r
ated
ca
s
es.
-
Op
p
o
r
tu
n
ity
:
on
e
p
o
s
s
ib
le
way
to
m
in
im
ize
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is
g
ap
is
to
w
ar
d
s
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s
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e
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weig
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g
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s
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h
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tu
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ies
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s
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if
icatio
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em
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s
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e
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r
r
ied
o
u
t to
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s
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h
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g
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e
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aliza
tio
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ab
ilit
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c)
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ac
k
s
p
r
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tical
s
u
p
p
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g
o
f
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is
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m
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m
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d
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o
f
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ac
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r
ate,
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ess
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y
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p
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r
m
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k
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o
f
s
o
p
h
is
ticated
o
p
er
atio
n
,
AI
m
o
d
els
in
d
u
ce
laten
cy
,
m
ay
b
e
s
m
all,
b
u
t
n
o
t
en
o
u
g
h
to
ca
ter
u
p
f
o
r
em
e
r
g
e
n
cy
s
itu
atio
n
r
eliab
ly
.
-
Op
p
o
r
tu
n
ity
:
o
n
e
f
ea
s
ib
le
wa
y
to
ad
d
r
ess
th
is
g
ap
is
to
co
n
s
tr
u
ct
a
n
o
v
el
m
ac
h
i
n
e
lear
n
in
g
m
o
d
el
with
d
is
cr
ete
ch
a
r
ac
ter
is
tics
s
u
p
p
o
r
tin
g
lo
w
laten
c
y
m
o
n
ito
r
in
g
c
o
n
s
is
ten
tly
o
n
r
ea
l
-
tim
e
.
An
o
th
e
r
f
ea
s
ib
ilit
y
is
to
war
d
s
ad
o
p
tin
g
s
tr
ea
m
in
g
an
aly
tics
a
n
d
e
d
g
e
co
m
p
u
tin
g
f
o
r
l
o
w
laten
cy
o
p
er
ati
o
n
s
f
o
r
co
n
s
is
ten
t m
o
n
ito
r
in
g
.
d)
I
n
teg
r
atio
n
o
f
m
u
ltimo
d
al
d
ata:
o
n
e
in
ter
esti
n
g
o
b
s
er
v
at
io
n
in
t
h
e
r
e
v
iew
s
h
o
ws
th
a
t
ad
o
p
tio
n
o
f
m
u
ltimo
d
al
c
o
u
ld
p
o
ten
tially
o
p
tim
ize
t
h
e
d
etec
tio
n
p
e
r
f
o
r
m
an
ce
o
f
ca
r
d
iac
a
r
r
est.
Fo
r
th
is
p
u
r
p
o
s
e
,
v
ar
io
u
s
attr
ib
u
tes
v
iz.
clin
ical
n
o
tes,
p
atien
t
h
is
to
r
y
d
an
r
esp
ir
atio
n
r
ate
s
h
o
u
ld
b
e
co
n
s
id
er
ed
wh
ile
m
o
d
ellin
g
.
Alth
o
u
g
h
,
all
th
es
e
in
f
o
r
m
atio
n
r
esid
es
with
in
s
o
m
e
o
f
th
e
s
tan
d
ar
d
d
ataset,
ex
is
tin
g
A
I
s
o
lu
tio
n
is
y
et
f
o
u
n
d
to
em
p
h
ases
o
n
ly
to
war
d
s
E
C
G
a
s
s
in
g
le
s
ig
n
al
m
o
d
alities
.
T
h
is
b
i
ased
ad
o
p
tio
n
r
estricts th
e
d
etec
tio
n
p
er
f
o
r
m
an
ce
co
n
tex
t
u
ally
.
-
Op
p
o
r
tu
n
ity
:
s
tu
d
ies to
war
d
s
a
d
o
p
tin
g
v
ar
io
u
s
m
o
d
alities
s
h
o
u
ld
b
e
en
co
u
r
ag
ed
.
e)
L
ac
k
s
in
teg
r
atio
n
with
clin
ical
wo
r
k
f
lo
ws
:
f
o
r
an
o
p
tim
al
an
d
r
eliab
le
an
aly
s
is
,
it
i
s
n
ec
ess
ar
y
f
o
r
an
AI
m
o
d
el
to
b
e
clin
ically
in
teg
r
ated
with
wo
r
k
f
lo
w
o
f
h
o
s
p
i
tal.
Fo
r
an
ex
am
p
le,
an
AI
m
o
d
el
ca
n
b
e
p
o
s
s
ib
ly
in
teg
r
ated
with
clin
ical
to
o
ls
,
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
,
an
d
o
th
e
r
f
o
r
m
o
f
n
o
tific
atio
n
s
y
s
tem
.
No
s
u
ch
m
o
d
el
is
y
et
witn
ess
e
d
wh
ich
ev
e
n
tu
ally
r
esu
lts
in
d
elay
ed
ac
tio
n
o
r
p
o
s
s
ib
le
m
is
s
ed
war
n
in
g
.
-
Op
p
o
r
tu
n
ity
:
if
n
o
t
s
u
ch
in
teg
r
atio
n
is
f
ea
s
ib
le
f
o
r
s
h
o
r
t
-
tim
e
r
esear
ch
wo
r
k
,
th
er
e
is
s
till
h
ig
h
er
h
o
p
es
co
n
s
id
er
ed
wid
e
r
a
n
d
d
iv
er
s
e
s
et
o
f
d
ataset
with
an
in
clu
s
io
n
o
f
m
o
d
u
les
to
r
ep
lica
te
th
e
r
ea
l
-
tim
e
s
ce
n
ar
io
ev
e
n
in
s
im
u
latio
n
m
o
d
e.
Su
ch
s
tu
d
y
p
atter
n
s
ca
n
tak
e
th
e
A
I
m
o
d
el
m
o
r
e
ap
p
licab
le
f
o
r
b
etter
c
h
an
ce
s
o
f
clin
ical
in
teg
r
atio
n
.
3
.
3
.
I
dentif
ied r
esea
rc
h t
ra
de
-
o
f
f
Fro
m
th
e
d
is
cu
s
s
io
n
o
f
id
en
tif
ied
r
esear
ch
g
ap
in
p
r
io
r
s
u
b
-
s
ec
tio
n
3
.
2
,
it
is
n
o
ted
th
at
th
e
r
e
ar
e
s
till
b
etter
o
p
p
o
r
tu
n
ity
t
o
war
d
s
im
p
r
o
v
is
in
g
t
h
e
s
h
o
r
tco
m
i
n
g
s
.
Ho
wev
er
,
s
u
ch
o
p
p
o
r
tu
n
ity
t
o
war
d
s
d
esig
n
in
g
a
s
o
lu
tio
n
s
h
o
u
ld
also
ad
d
r
ess
v
ar
io
u
s
tr
ad
e
-
o
f
f
,
wh
ich
is
ac
tu
ally
m
o
r
e
ch
allen
g
in
g
.
T
h
e
p
r
im
e
o
r
ig
in
atio
n
o
f
s
u
ch
tr
ad
e
-
o
f
f
m
ai
n
ly
ar
is
es
f
r
o
m
th
e
le
g
ac
y
is
s
u
es
o
f
AI
i
ts
elf
alo
n
g
with
s
ev
er
e
d
eg
r
e
e
o
f
s
o
p
h
is
ticatio
n
d
em
an
d
e
d
f
o
r
a
tr
u
e
d
etec
tio
n
o
f
ca
r
d
iac
a
r
r
est.
Fo
llo
win
g
ar
e
s
o
m
e
o
f
th
e
cr
itical
tr
ad
e
-
o
f
f
:
-
Pre
d
ictio
n
an
d
m
o
n
ito
r
in
g
:
p
r
ed
ictio
n
o
n
r
ea
l
-
tim
e
e
m
p
h
asi
ze
s
o
n
id
en
tif
y
in
g
cr
itical
ev
e
n
ts
f
o
llo
win
g
b
y
g
en
er
atin
g
n
o
tific
atio
n
s
in
cr
itical
cir
cu
m
s
tan
ce
s
.
Ho
wev
er
,
m
o
n
ito
r
i
n
g
o
v
er
lo
n
g
-
ter
m
is
m
o
r
e
ass
o
ciate
d
with
co
llectin
g
s
e
q
u
en
tial
d
ata
f
o
llo
wed
b
y
tr
e
n
d
an
aly
s
is
ess
en
tial
f
o
r
p
r
ed
ictin
g
r
is
k
in
f
u
tu
r
e.
Hen
ce
,
AI
m
o
d
els
to
w
ar
d
s
in
s
tan
tan
eo
u
s
p
r
ed
ictio
n
ca
n
m
is
s
id
en
tify
in
g
th
e
r
is
k
attr
ib
u
tes
th
at
ca
n
b
e
ac
tu
ally
ca
p
tu
r
ed
u
s
in
g
lo
n
g
-
ter
m
m
o
n
ito
r
in
g
a
p
p
r
o
a
ch
es,
wh
ile
th
e
latter
i
s
al
s
o
ass
o
ciate
d
with
lar
g
e
r
eso
u
r
ce
co
n
s
u
m
p
tio
n
.
-
Gen
er
aliza
tio
n
an
d
s
p
ec
ializatio
n
:
an
y
e
x
p
licit
m
o
d
el
f
o
c
u
s
in
g
o
n
s
p
ec
ializ
atio
n
ca
n
o
f
f
er
in
cr
ea
s
ed
d
etec
tio
n
r
ate;
h
o
wev
er
,
t
h
eir
ap
p
licab
ilit
y
is
q
u
esti
o
n
ab
le
wh
en
s
u
b
jecte
d
to
d
if
f
e
r
en
t
cl
in
ical
s
ettin
g
s
.
On
th
e
o
th
er
s
id
e,
th
e
m
o
d
el
claim
in
g
f
o
r
g
e
n
er
aliza
tio
n
,
wh
en
s
u
b
jecte
d
to
s
p
ec
if
ic
ca
s
e
m
ay
u
n
d
e
r
p
er
f
o
r
m
.
-
Per
f
o
r
m
an
ce
i
n
r
ea
l
-
tim
e
an
d
ac
cu
r
ac
y
:
th
e
AI
m
o
d
els
em
p
h
asizin
g
o
n
r
ea
l
-
tim
e
p
e
r
f
o
r
m
an
ce
d
em
an
d
s
a
lo
w
-
laten
cy
s
y
s
tem
wh
ile
A
I
m
o
d
el
f
o
cu
s
in
g
to
ac
co
m
p
lis
h
h
ig
h
e
r
ac
cu
r
ac
y
f
o
cu
s
s
es
o
n
co
m
p
lex
ities
an
d
p
atter
n
s
o
f
d
ata
wh
ich
ev
en
tu
ally
tak
es
m
o
r
e
co
m
p
u
tatio
n
al
r
eso
u
r
ce
s
an
d
tim
e.
Hen
ce
,
AI
-
m
o
d
els
with
h
ig
h
er
ac
cu
r
ac
y
c
o
u
ld
b
e
ev
en
tu
ally
s
lo
wer
wh
ile
m
o
d
el
f
o
cu
s
in
g
o
n
r
ea
l
-
tim
e
p
er
f
o
r
m
an
ce
ca
n
ac
tu
ally
m
is
s
o
u
t c
r
itical
in
f
o
r
m
atio
n
n
ec
ess
ar
y
f
o
r
d
etec
tin
g
ea
r
ly
o
n
s
et
o
f
ca
r
d
iac
a
r
r
est.
All
th
e
ab
o
v
e
-
m
en
tio
n
e
d
tr
ad
e
-
o
f
f
e
q
u
ally
d
e
m
an
d
s
an
im
p
o
r
tan
ce
to
b
e
ad
d
r
ess
ed
.
A
b
etter
m
o
d
ellin
g
b
y
jo
in
t c
o
n
s
id
er
atio
n
o
f
tr
a
d
e
-
o
f
f
an
d
g
ap
s
co
u
ld
lead
to
e
f
f
ici
en
t A
I
m
o
d
el
t
o
war
d
s
ea
r
ly
d
e
tectio
n
p
r
o
b
lem
.
T
h
is
r
ev
iew
em
p
h
asized
th
e
wid
e
s
p
ec
tr
u
m
o
f
AI
-
b
ased
te
ch
n
iq
u
es
b
ein
g
i
n
v
esti
g
ated
f
o
r
th
e
ea
r
ly
d
iag
n
o
s
is
o
f
ca
r
d
iac
ar
r
est.
A
cr
u
cial
f
in
d
in
g
is
th
at,
wh
ile
g
r
ea
t
p
r
o
g
r
ess
h
as
b
ee
n
ac
h
iev
e
d
in
d
ev
el
o
p
in
g
A
I
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
E
xp
lo
r
in
g
th
e
imp
a
ct
o
f a
r
tifi
cia
l in
tellig
en
ce
d
r
iven
s
o
lu
tio
n
s
o
n
ea
r
ly
…
(
Teja
s
h
r
ee
V
en
ka
tesh
a
)
1943
s
o
lu
tio
n
s
,
th
e
f
ield
is
s
till
in
its
ea
r
ly
p
h
as
es
o
f
r
ea
l
-
wo
r
ld
im
p
lem
en
tatio
n
.
AI
ap
p
r
o
a
ch
es,
s
u
ch
as
d
ee
p
lear
n
in
g
m
o
d
els
lik
e
C
NNs
an
d
L
STM
s
,
wea
r
ab
le
d
e
v
ice
-
b
ased
m
o
n
ito
r
in
g
,
a
n
d
p
r
ed
ict
iv
e
m
o
d
elin
g
,
h
a
v
e
d
em
o
n
s
tr
ated
p
r
o
m
is
e
in
d
ete
ctin
g
ea
r
ly
in
d
icato
r
s
o
f
ca
r
d
i
ac
ar
r
est.
Ho
wev
er
,
o
b
s
tacle
s
r
em
ai
n
,
p
ar
ticu
lar
ly
in
ter
m
s
o
f
d
ataset
co
n
s
tr
ain
ts
,
m
o
d
el
g
e
n
er
aliza
tio
n
,
r
ea
l
-
tim
e
m
o
n
ito
r
i
n
g
ca
p
a
b
ilit
ies,
an
d
co
n
n
ec
tio
n
with
clin
ical
o
p
er
atio
n
s
.
Fu
r
th
er
m
o
r
e,
d
esp
ite
ad
v
an
ce
s
in
AI
,
an
o
n
g
o
i
n
g
d
if
f
ic
u
lty
in
m
o
s
t
tech
n
iq
u
es
is
th
e
im
b
alan
ce
d
an
d
f
r
eq
u
en
tl
y
p
o
o
r
ly
lab
eled
d
atasets
,
wh
ich
lim
it
AI
m
o
d
els'
ca
p
ac
ity
to
ef
f
ec
tiv
ely
g
en
er
alize
ac
r
o
s
s
v
ar
ied
clin
ical
s
itu
atio
n
s
.
Fu
r
th
er
m
o
r
e,
lo
w
-
laten
cy
m
o
d
els
th
at
ca
n
o
p
e
r
ate
in
r
e
al
-
tim
e
em
er
g
en
cy
cir
cu
m
s
tan
ce
s
ar
e
b
ec
o
m
i
n
g
i
n
cr
ea
s
in
g
ly
im
p
o
r
tan
t f
o
r
p
r
o
m
p
t in
t
er
v
en
tio
n
.
C
o
m
p
ar
ed
to
ea
r
lier
s
tu
d
ies
in
th
is
s
ec
to
r
,
th
is
r
ev
iew
co
n
tr
ib
u
tes
to
a
b
etter
k
n
o
wled
g
e
o
f
th
e
o
b
s
tacle
s
an
d
p
o
ten
tial
ad
v
a
n
ce
m
en
ts
in
AI
-
d
r
iv
e
n
ca
r
d
i
ac
ar
r
est
d
etec
tio
n
.
W
h
ile
p
r
ev
io
u
s
ev
al
u
atio
n
s
f
o
cu
s
ed
o
n
th
e
p
a
r
ticu
lar
ap
p
licatio
n
o
f
AI
a
p
p
r
o
ac
h
es,
th
is
s
tu
d
y
tak
es
a
m
o
r
e
in
-
d
ep
th
lo
o
k
at
th
e
in
teg
r
atio
n
o
f
v
ar
ied
d
atasets
,
tax
o
n
o
m
ies o
f
AI
tech
n
iq
u
es,
an
d
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
r
ea
l
-
tim
e
p
er
f
o
r
m
a
n
ce
an
d
m
o
d
el
co
r
r
ec
tn
ess
.
R
ec
o
g
n
izin
g
s
p
ec
if
ic
g
ap
s
,
s
u
ch
as
th
e
n
ee
d
f
o
r
lab
ele
d
d
ata,
m
u
lt
i
m
o
d
al
in
teg
r
atio
n
,
an
d
clin
ical
wo
r
k
f
lo
w
in
teg
r
atio
n
,
th
is
an
aly
s
is
p
r
esen
ts
n
ew
id
ea
s
an
d
d
ir
ec
tio
n
th
at
ar
e
co
n
s
is
ten
t
with
th
e
cu
r
r
en
t
s
tatu
s
o
f
th
e
f
ield
.
Pr
ev
io
u
s
r
esear
ch
h
as
p
r
im
ar
ily
d
em
o
n
s
tr
ated
th
e
p
o
ten
tial
o
f
AI
in
d
etec
tin
g
ar
r
h
y
th
m
ias
an
d
p
r
e
d
ictin
g
c
ar
d
iac
ev
en
ts
f
r
o
m
E
C
G
d
ata
,
b
u
t
th
is
s
tu
d
y
f
o
c
u
s
es
o
n
th
e
b
r
o
a
d
er
s
co
p
e
o
f
m
u
ltimo
d
al
an
d
wea
r
ab
le
d
ev
ice
-
b
ased
ap
p
r
o
ac
h
es,
wh
ic
h
ar
e
b
ec
o
m
i
n
g
m
o
r
e
v
iab
le
in
th
e
f
ield
o
f
ca
r
d
iac
h
ea
lth
m
o
n
ito
r
in
g
.
T
h
e
f
i
n
d
in
g
s
o
f
th
is
an
aly
s
is
h
ig
h
li
g
h
t
t
h
e
co
m
p
lex
i
n
ter
p
lay
b
etwe
e
n
d
ata
q
u
ality
,
r
ea
l
-
tim
e
r
eq
u
ir
em
en
ts
,
an
d
clin
ical
ac
ce
p
tan
ce
,
wh
ich
h
as
n
o
t
b
ee
n
ad
eq
u
ately
ad
d
r
ess
ed
in
p
r
ev
io
u
s
p
u
b
licatio
n
s
.
4.
CO
NCLU
SI
O
N
T
o
s
u
m
m
ar
ize,
wh
ile
AI
-
d
r
iv
en
tech
n
o
lo
g
ies
f
o
r
ea
r
ly
id
en
tific
atio
n
o
f
ca
r
d
iac
a
r
r
est
h
o
ld
s
ig
n
if
ican
t p
r
o
m
is
e,
o
v
e
r
co
m
i
n
g
d
if
f
icu
lties
s
u
ch
as d
ata
q
u
ality
,
m
o
d
el
in
ter
p
r
eta
b
ilit
y
,
r
e
al
-
tim
e
m
o
n
ito
r
in
g
,
an
d
clin
ical
in
teg
r
atio
n
is
c
r
itical
to
r
ea
lizin
g
t
h
eir
f
u
ll
p
o
ten
tial.
Ad
d
r
ess
in
g
t
h
ese
s
h
o
r
tco
m
in
g
s
th
r
o
u
g
h
n
ew
r
esear
ch
will
im
p
r
o
v
e
th
e
ac
cu
r
ac
y
an
d
d
ep
en
d
ab
ilit
y
o
f
AI
m
o
d
els
wh
ile
also
p
av
in
g
th
e
p
ath
f
o
r
m
o
r
e
ef
f
ec
tiv
e
an
d
p
r
o
m
p
t
tr
ea
tm
e
n
ts
in
u
r
g
en
t
ca
r
d
iac
ca
r
e
.
T
h
e
f
u
tu
r
e
o
f
AI
in
ca
r
d
iac
ar
r
est
d
etec
tio
n
d
ep
e
n
d
s
o
n
co
n
s
tan
t
co
llab
o
r
atio
n
a
m
o
n
g
r
esear
ch
er
s
,
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als,
an
d
p
h
y
s
ician
s
to
d
ev
elo
p
s
o
lid
,
p
r
ac
tical,
an
d
et
h
ical
s
o
lu
tio
n
s
th
at
ca
n
s
av
e
liv
es.
At
p
r
e
s
en
t,
th
er
e
ar
e
v
ar
io
u
s
d
eg
r
e
es
o
f
s
tu
d
y
m
o
d
els
h
ar
n
ess
in
g
AI
to
war
d
s
d
etec
tio
n
o
f
ca
r
d
iac
ar
r
est u
s
in
g
d
if
f
e
r
en
t ty
p
es o
f
d
ata.
I
t h
as b
ee
n
n
o
ted
th
at
th
er
e
ar
e
v
ar
io
u
s
s
ca
les
o
f
in
n
o
v
ativ
e
a
p
p
r
o
ac
h
es
ev
o
lv
ed
f
r
o
m
m
ac
h
in
e
an
d
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es
m
ain
ly
i
n
th
is
p
er
s
p
ec
tiv
e;
h
o
wev
er
,
th
e
r
e
ar
e
also
s
h
o
r
tco
m
in
g
s
wh
ich
is
n
ec
ess
ar
y
to
b
e
ad
d
r
ess
ed
.
T
h
e
co
n
tr
ib
u
tio
n
a
n
d
n
o
v
elty
o
f
th
is
r
ev
iew
wo
r
k
a
r
e
as
f
o
llo
ws:
i)
th
e
s
tu
d
y
p
r
esen
ts
a
v
er
y
cr
is
p
d
is
cu
s
s
io
n
o
n
n
ewly
id
en
tifie
d
tax
o
n
o
m
y
o
f
AI
-
b
ased
m
eth
o
d
s
to
war
d
s
d
etec
tio
n
o
f
ca
r
d
i
ac
ar
r
est
to
f
in
d
ass
o
ciate
d
b
en
ef
its
an
d
u
n
s
o
lv
ed
is
s
u
es,
ii)
d
if
f
er
en
t
f
r
o
m
ex
is
t
in
g
r
ev
iew
wo
r
k
,
th
e
cu
r
r
en
t
p
ap
er
h
as
p
r
esen
te
d
d
is
cu
s
s
i
o
n
o
f
d
ataset
alo
n
g
with
th
eir
lim
itatio
n
to
ass
is
t
f
u
tu
r
e
r
esear
ch
e
r
s
ch
o
o
s
in
g
th
e
n
ec
ess
ar
y
d
ataset
f
o
r
th
eir
s
tu
d
y
,
iii)
h
ig
h
lig
h
ts
o
f
r
esear
ch
g
ap
with
p
o
s
s
ib
le
o
p
p
o
r
tu
n
it
y
h
as
b
ee
n
s
tated
,
an
d
iv
)
p
o
in
t
-
to
-
p
o
in
t
b
r
ief
in
g
o
f
id
en
tifie
d
co
r
e
tr
ad
e
-
o
f
f
h
as b
ee
n
p
r
esen
ted
wh
ich
ar
e
ess
en
tial to
b
e
ad
d
r
ess
ed
in
u
p
co
m
in
g
s
tu
d
ies.
T
h
e
f
u
tu
r
e
wo
r
k
will b
e
to
war
d
s
ad
d
r
ess
in
g
th
e
p
r
o
b
l
em
s
id
en
tifie
d
in
cu
r
r
e
n
t
r
e
v
iew
wo
r
k
u
s
in
g
n
ewly
c
o
n
s
tr
u
cted
m
o
d
els
o
f
m
ac
h
in
e
lear
n
in
g
alo
n
g
with
f
lex
ib
le
co
n
s
tr
ain
t
m
o
d
ell
in
g
.
Mo
r
e
im
p
o
r
ta
n
ce
will
b
e
g
iv
en
to
s
elec
t
p
r
o
m
in
e
n
t
attr
ib
u
tes u
n
d
e
r
d
y
n
am
ic
en
v
ir
o
n
m
en
t
to
s
elec
t m
o
s
t
d
o
m
i
n
an
t
in
d
icato
r
to
wa
r
d
s
ea
r
ly
d
etec
tio
n
o
f
ca
r
d
iac
ar
r
est.
L
o
o
k
in
g
ah
ea
d
,
th
er
e
ar
e
v
a
r
io
u
s
in
tr
ig
u
in
g
s
tu
d
y
to
p
ics.
First,
th
e
is
s
u
e
o
f
d
ataset
im
b
a
la
n
ce
ca
n
b
e
ad
d
r
ess
ed
u
s
in
g
ap
p
r
o
ac
h
es
s
u
ch
as
s
y
n
th
etic
d
ata
g
en
e
r
atio
n
an
d
a
d
v
an
ce
d
d
ata
au
g
m
e
n
tatio
n
alg
o
r
ith
m
s
,
wh
ich
h
elp
to
b
alan
ce
th
e
p
r
e
v
alen
ce
o
f
ca
r
d
iac
ar
r
est ca
s
es
with
n
o
r
m
al
h
ea
r
t c
o
n
d
itio
n
d
ata.
An
o
th
er
way
to
im
p
r
o
v
e
p
r
ed
ictio
n
ac
c
u
r
ac
y
i
s
to
in
teg
r
ate
m
u
ltimo
d
al
d
ata
s
o
u
r
ce
s
,
s
u
ch
as E
C
G,
p
atien
t h
is
to
r
y
,
v
ital sig
n
s
,
an
d
ev
e
n
v
id
eo
an
aly
s
is
.
R
esear
ch
ef
f
o
r
ts
s
h
o
u
ld
also
b
e
d
ir
ec
ted
o
n
d
ev
el
o
p
in
g
AI
m
o
d
els
with
lo
we
r
laten
cy
f
o
r
r
ea
l
-
tim
e
d
etec
tio
n
an
d
in
ter
v
e
n
tio
n
,
as
well
as
in
v
esti
g
ati
n
g
ed
g
e
co
m
p
u
tin
g
f
o
r
m
o
r
e
ef
f
icien
t
d
ata
p
r
o
ce
s
s
in
g
.
I
n
ad
d
itio
n
,
AI
s
o
lu
tio
n
s
m
u
s
t
b
e
ef
f
ec
t
iv
ely
in
teg
r
ate
d
in
to
h
ea
lth
ca
r
e
o
p
e
r
atio
n
s
.
T
h
is
in
v
o
lv
es
s
ea
m
less
in
teg
r
atio
n
with
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
(
E
HR
s
)
an
d
o
t
h
er
m
o
n
ito
r
in
g
s
y
s
tem
s
to
en
ab
le
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
an
d
h
elp
p
h
y
s
ician
s
p
r
o
v
id
e
p
r
o
m
p
t in
ter
v
en
tio
n
s
.
Fu
tu
r
e
s
tu
d
y
co
u
ld
lo
o
k
in
t
o
th
e
eth
ical
im
p
licatio
n
s
o
f
AI
in
h
ea
lth
ca
r
e,
s
p
ec
if
ically
d
ata
p
r
iv
ac
y
,
m
o
d
el
o
p
en
n
ess
,
a
n
d
th
e
ac
ce
p
ta
b
ilit
y
o
f
AI
-
g
en
er
ated
s
u
g
g
esti
o
n
s
in
cl
in
ical
co
n
te
x
ts
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
9
,
No
.
3
,
Sep
tem
b
er
20
25
:
1
9
38
-
1
9
45
1944
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
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
T
ejash
r
ee
V
en
k
atesh
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Su
n
d
ar
ar
ajan
Sar
av
an
a
Ku
m
ar
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
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