I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
pu
t
er
Science
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
,
p
p
.
1
5
8
0
~
1
587
I
SS
N:
2
502
-
4
7
52
,
DOI
: 1
0
.
1
1
5
9
1
/ijee
cs
.v
3
7
.
i
3
.
pp
1
5
8
0
-
1
5
8
7
1580
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs
.
ia
esco
r
e.
co
m
Att
en
tion
de
ficit
a
nd hy
pera
ctivity
diso
rder
cla
ss
ifi
c
a
tion
in
qua
ntit
a
tive
EE
G
s
ig
na
ls usin
g
m
a
chine
l
ea
rni
ng
a
lg
o
rithms
Sy
if
a
ni I
hfa
dza
Aliy
a
h
1
,
Sa
s
t
ra
K
us
um
a
Wij
a
y
a
1
,
Yet
t
y
Ra
m
li
2
1
De
p
a
r
t
me
n
t
o
f
P
h
y
si
c
s,
F
a
c
u
l
t
y
o
f
M
a
t
h
e
m
a
t
i
c
s
a
n
d
N
a
t
u
r
a
l
S
c
i
e
n
c
e
s
,
U
n
i
v
e
r
si
t
a
s
I
n
d
o
n
e
si
a
,
D
e
p
o
k
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
o
f
N
e
u
r
o
l
o
g
y
,
F
a
c
u
l
t
y
o
f
M
e
d
i
c
i
n
e
,
U
n
i
v
e
r
s
i
t
a
s I
n
d
o
n
e
si
a
,
D
e
p
o
k
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
No
v
21
,
2
0
2
3
R
ev
is
ed
Oct
2
,
202
4
Acc
ep
ted
Oct
7
,
2
0
2
4
Atten
ti
o
n
d
e
ficit
a
n
d
h
y
p
e
ra
c
ti
v
i
ty
d
iso
r
d
e
r
(AD
HD
)
c
las
sifica
ti
o
n
m
e
th
o
d
a
s a
q
u
a
n
ti
tativ
e
o
b
se
rv
a
ti
o
n
h
a
s b
e
e
n
c
o
n
ti
n
u
a
ll
y
imp
ro
v
e
d
t
o
a
ss
ist
m
e
d
ica
l
p
ra
c
ti
ti
o
n
e
rs.
Cu
rre
n
t
ly
,
m
a
c
h
i
n
e
lea
rn
in
g
a
l
g
o
rit
h
m
s
su
c
h
a
s
k
-
n
e
a
re
st
n
e
ig
h
b
o
rs
(KN
N),
m
u
lt
il
a
y
e
r
p
e
r
c
e
p
tro
n
(M
L
P
),
a
n
d
su
p
p
o
rt
v
e
c
t
o
r
m
a
c
h
in
e
(S
VM)
a
re
wid
e
ly
u
se
d
.
T
h
is
stu
d
y
p
ro
p
o
se
d
a
fe
a
tu
re
e
x
trac
ti
o
n
m
e
th
o
d
f
o
r
q
u
a
n
t
it
a
ti
v
e
e
lec
tro
e
n
c
e
p
h
a
lo
g
r
a
p
h
y
(
q
EE
G
)
d
a
ta
d
e
riv
e
d
fro
m
th
e
c
o
n
ti
n
u
o
u
s wa
v
e
let
tran
sfo
rm
(C
WT
)
to
c
las
s
ify
c
h
il
d
re
n
wit
h
AD
HD
v
e
rsu
s
h
e
a
lt
h
y
su
b
jec
ts.
S
u
b
se
q
u
e
n
tl
y
,
t
h
is
stu
d
y
c
o
m
p
a
re
d
th
e
p
e
rfo
rm
a
n
c
e
o
f
t
h
e
c
las
sifica
ti
o
n
p
i
p
e
li
n
e
b
e
fo
re
a
n
d
a
fter
t
h
e
imp
lem
e
n
tatio
n
o
f
p
rin
c
i
p
a
l
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis
(
P
CA)
o
n
th
e
fe
a
tu
re
s
p
rio
r
to
p
r
o
c
e
ss
in
g
wit
h
m
a
c
h
in
e
lea
r
n
in
g
a
l
g
o
ri
th
m
s.
T
h
e
re
su
lt
s
r
e
v
e
a
led
th
a
t
th
e
o
v
e
ra
ll
p
e
rf
o
rm
a
n
c
e
o
f
th
e
c
las
sifiers
c
o
n
siste
n
tl
y
imp
r
o
v
e
d
a
fter
th
e
imp
lem
e
n
tati
o
n
o
f
P
CA.
Th
e
re
su
lt
s
h
ig
h
li
g
h
t
th
e
v
a
ry
in
g
imp
a
c
t
o
f
P
CA
o
n
c
las
sifier
p
e
rfo
rm
a
n
c
e
,
with
KN
N
sh
o
win
g
a
n
im
p
ro
v
e
m
e
n
t
i
n
tes
ti
n
g
a
c
c
u
ra
c
y
fro
m
6
1
.
8
4
%
t
o
6
9
.
2
1
%
fo
ll
o
wi
n
g
P
CA
imp
lem
e
n
tatio
n
,
wh
il
e
t
h
e
o
t
h
e
r
c
las
sifiers
sh
o
we
d
d
e
terio
ra
ti
o
n
in
p
e
rfo
rm
a
n
c
e
.
Th
e
se
fin
d
in
g
s
su
g
g
e
st
th
a
t
wh
il
e
P
C
A
m
a
y
b
e
b
e
n
e
ficia
l
fo
r
so
m
e
c
las
sifiers
,
it
s
imp
a
c
t
o
n
p
e
rfo
rm
a
n
c
e
v
a
ries
d
e
p
e
n
d
in
g
o
n
t
h
e
sp
e
c
ifi
c
c
h
a
ra
c
teristics
o
f
th
e
d
a
tas
e
t
a
n
d
t
h
e
c
las
sifier
u
ti
li
z
e
d
.
M
o
re
o
v
e
r,
th
is
st
u
d
y
p
r
o
v
id
e
s
in
sig
h
t
fo
r
fu
t
u
re
imp
lem
e
n
tati
o
n
o
f
th
e
c
las
sifica
ti
o
n
m
e
th
o
d
f
o
r
AD
H
D
p
a
ti
e
n
ts
a
c
ro
ss
a
m
o
re
sp
e
c
i
fic
c
li
n
ica
l
ra
n
g
e
o
f
th
e
sp
e
c
tru
m
.
K
ey
w
o
r
d
s
:
ADHD
Mach
in
e
lear
n
in
g
class
if
icatio
n
Prin
cip
al
co
m
p
o
n
en
t a
n
al
y
s
is
Qu
an
titativ
e
E
E
G
W
av
elet
tr
an
s
f
o
r
m
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
:
Sas
tr
a
Ku
s
u
m
a
W
ijay
a
Dep
ar
tm
en
t o
f
Ph
y
s
ics,
Facu
lty
o
f
Ma
th
e
m
atics a
n
d
Natu
r
al
Scien
ce
s
,
Un
iv
er
s
itas
I
n
d
o
n
esia
Dep
o
k
,
W
est J
av
a,
I
n
d
o
n
esia
E
m
ail:
s
k
wijay
a@
s
c
i.u
i.a
c.
id
1.
I
NT
RO
D
UCT
I
O
N
Atten
tio
n
d
ef
icit
an
d
h
y
p
er
ac
t
iv
ity
d
is
o
r
d
er
(
ADHD
)
is
a
n
eu
r
o
d
e
v
elo
p
m
e
n
tal
d
is
o
r
d
er
th
at
af
f
ec
ts
m
illi
o
n
s
o
f
c
h
ild
r
en
an
d
ad
u
lt
s
wo
r
ld
wid
e
,
an
d
is
ch
a
r
ac
ter
i
ze
d
b
y
d
is
r
u
p
ti
v
e
in
atten
tio
n
,
ex
ce
s
s
iv
e
ac
tiv
ity
,
an
d
im
p
u
ls
iv
e
ac
tio
n
s
[
1
]
.
ADHD
im
p
ac
ts
ar
o
u
n
d
5
–
7
%
o
f
ch
ild
r
en
an
d
2
–
5%
o
f
ad
u
lts
g
lo
b
ally
[
2
]
.
T
h
e
m
o
s
t
co
m
m
o
n
m
eth
o
d
s
f
o
r
p
s
y
ch
iatr
is
ts
,
p
ed
iatr
ician
s
,
n
eu
r
o
lo
g
is
ts
,
an
d
p
s
y
ch
o
lo
g
i
s
ts
o
n
ADH
D
ar
e
clin
ical
o
b
s
er
v
atio
n
s
[
3
]
.
Ho
w
ev
er
,
in
r
ec
en
t y
ea
r
s
,
m
eth
o
d
s
b
ased
o
n
b
r
ain
elec
tr
ical
s
ig
n
a
ls
th
r
o
u
g
h
s
p
ec
tr
al
an
aly
s
is
h
av
e
aid
ed
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als
in
th
e
d
ia
g
n
o
s
is
o
f
ADHD
[
4
]
.
E
lectr
o
en
ce
p
h
alo
g
r
ap
h
y
(
E
E
G)
is
a
n
o
n
-
in
v
asiv
e
m
eth
o
d
f
o
r
ac
q
u
ir
in
g
elec
tr
ical
ac
tiv
ity
o
r
ig
in
atin
g
f
r
o
m
n
eu
r
o
n
s
in
th
e
b
r
ain
th
at
ca
n
b
e
m
ea
s
u
r
ed
th
r
o
u
g
h
th
e
s
ca
lp
[
5
]
.
T
h
is
p
r
o
ce
d
u
r
e
in
v
o
l
v
es
p
lacin
g
elec
tr
o
d
es
o
n
t
h
e
s
ca
lp
to
r
ec
o
r
d
E
E
G
s
ig
n
als.
EEG
h
as
p
r
o
v
en
t
o
b
e
v
alu
ab
le
to
o
l
in
ass
is
tin
g
th
e
q
u
an
titativ
e
d
iag
n
o
s
is
o
f
ADHD
as
it
p
r
o
v
id
es
in
f
o
r
m
atio
n
ab
o
u
t b
r
ain
elec
tr
ical
ac
tiv
ity
[
6
]
.
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:
2
5
0
2
-
4
7
52
A
tten
tio
n
d
eficit
a
n
d
h
yp
era
cti
vity
d
is
o
r
d
er c
la
s
s
if
ica
tio
n
…
(
S
yifa
n
i I
h
fa
d
z
a
A
liya
h
)
1581
E
E
G
h
as
ev
o
lv
ed
in
to
q
u
an
ti
tativ
e
E
E
G
(
q
E
E
G)
,
wh
er
e
E
E
G
s
ig
n
als
ar
e
m
ap
p
ed
f
o
r
th
eir
b
r
ai
n
ac
tiv
ity
p
atter
n
s
u
s
in
g
d
ig
ital
s
ig
n
als
an
d
m
ath
em
atica
l
alg
o
r
ith
m
s
[
7
]
.
I
n
2
0
0
5
,
Nied
e
r
m
e
y
er
[
8
]
h
ig
h
lig
h
ted
th
e
r
o
le
o
f
q
E
E
G
in
u
n
d
er
s
tan
d
in
g
b
r
ai
n
ac
tiv
ity
p
atter
n
s
an
d
th
eir
ass
o
ciatio
n
s
with
co
g
n
itiv
e
d
is
o
r
d
er
s
.
T
h
e
q
E
E
G
s
ig
n
al
is
a
u
s
ef
u
l
to
o
l
f
o
r
m
ea
s
u
r
in
g
an
d
an
aly
zin
g
b
r
ain
ac
tiv
ity
th
at
p
lay
s
a
cr
u
cial
r
o
le
in
id
en
tify
in
g
s
p
ec
if
ic
p
atter
n
s
th
at
in
d
icat
e
ce
r
tain
s
y
m
p
to
m
s
an
d
aid
in
g
in
th
e
tr
ea
tm
en
t
o
f
v
ar
i
o
u
s
m
en
tal
h
ea
lth
d
is
o
r
d
er
s
,
s
u
ch
as
ADHD
[
9
]
.
T
h
e
f
r
eq
u
en
c
y
r
an
g
e
o
f
E
E
G
s
ig
n
als
v
ar
y
,
b
u
t
th
e
m
o
s
t
co
m
m
o
n
ar
e
d
elta
(
0
.
5
-
4
Hz)
,
th
eta
(
4
-
7
Hz)
,
a
lp
h
a
(
8
-
12
Hz)
,
s
ig
m
a
(
1
2
-
16
Hz)
,
an
d
b
eta
(
1
3
-
30
Hz)
[
1
0
]
.
T
h
e
s
tu
d
y
b
y
B
ar
r
y
et
a
l.
[
1
1
]
i
n
2
0
0
7
f
o
u
n
d
th
at
ch
ild
r
en
with
ADHD
ex
h
ib
ited
lo
wer
b
r
ain
ac
tiv
ity
i
n
th
e
b
eta
f
r
eq
u
e
n
c
y
r
an
g
e
(
1
3
-
2
1
Hz)
in
th
e
f
r
o
n
tal
ar
ea
.
T
h
is
s
u
g
g
ests
th
at
ch
ild
r
en
with
ADHD
ex
p
er
ien
ce
d
is
r
u
p
tio
n
s
in
th
ei
r
ab
ilit
y
to
f
o
cu
s
atten
tio
n
an
d
r
eg
u
late
b
e
h
av
io
r
.
I
n
2
0
1
2
,
L
o
o
a
n
d
Ma
k
eig
[
1
2
]
f
o
u
n
d
th
at
b
r
ain
ac
tiv
ity
p
atter
n
s
o
f
c
h
ild
r
en
with
ADHD
d
if
f
er
ed
f
r
o
m
th
o
s
e
ca
teg
o
r
ized
as
n
e
u
r
o
ty
p
ical.
T
h
e
y
d
is
co
v
er
ed
th
at
b
r
ain
ac
tiv
ity
in
ch
ild
r
en
with
AD
HD
s
h
o
wed
lo
wer
b
eta
f
r
e
q
u
en
cy
an
d
h
ig
h
e
r
th
eta
f
r
e
q
u
e
n
cy
ac
tiv
ity
in
th
e
f
r
o
n
tal
a
n
d
te
m
p
o
r
al
ar
ea
s
,
wh
er
ea
s
n
eu
r
o
ty
p
ical
c
h
ild
r
e
n
ex
h
i
b
ited
m
o
r
e
s
tab
le
b
r
ai
n
ac
tiv
ity
ac
r
o
s
s
th
e
en
tire
b
r
ain
.
T
h
e
co
n
tin
u
o
u
s
wav
elet
tr
an
s
f
o
r
m
(
C
W
T
)
h
as
b
ee
n
u
s
ed
t
o
o
b
tain
d
etailed
in
f
o
r
m
atio
n
ab
o
u
t
tim
e
s
er
ies
s
ig
n
als
s
u
ch
as
q
E
E
G
[
1
3
]
.
T
h
is
m
eth
o
d
s
g
e
n
er
ates
tim
e
-
f
r
eq
u
e
n
cy
an
d
n
o
n
lin
ea
r
f
ea
tu
r
es
th
at
ca
n
s
er
v
es
as
a
q
u
an
titativ
e
to
o
l
to
d
etec
t
th
e
ac
tiv
ity
o
f
h
u
m
a
n
b
r
ain
[
1
4
]
.
Q
E
E
G
s
tu
d
ies
in
in
d
iv
id
u
als
with
ADHD
h
av
e
in
d
icate
d
s
p
ec
if
i
c
d
if
f
er
e
n
ce
s
on
b
r
ain
ac
tiv
it
y
s
u
ch
as
lo
wer
b
r
ain
ac
tiv
ity
lev
els
an
d
d
is
tin
ct
f
r
eq
u
e
n
cies
[
1
5
]
.
Var
io
u
s
m
a
ch
in
e
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
as
k
-
n
ea
r
est
n
eig
h
b
o
r
(
K
NN)
,
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
(
C
NN)
,
r
an
d
o
m
f
o
r
est,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
,
h
av
e
b
ee
n
ap
p
lied
to
class
if
y
E
E
G
s
ig
n
als
as
n
o
r
m
a
l
o
r
in
d
i
ca
tiv
e
o
f
ADH
D
o
r
m
en
tal
d
is
o
r
d
er
s
s
u
ch
as
d
ep
r
ess
io
n
[
1
6
]
-
[
1
8
]
.
I
n
ap
p
ly
in
g
th
ese
alg
o
r
ith
m
s
,
E
E
G
s
ig
n
al
s
d
ata
ar
e
co
llected
an
d
p
r
ep
r
o
ce
s
s
ed
b
ef
o
r
e
b
ein
g
i
n
p
u
t
i
n
to
th
e
m
o
d
el
.
T
h
e
m
o
d
el
is
th
en
tr
ain
ed
u
s
in
g
q
E
E
G
s
ig
n
al
d
ata
f
r
o
m
in
d
iv
id
u
als
with
an
d
with
o
u
t
AD
HD,
en
ab
lin
g
it
to
d
is
tin
g
u
is
h
b
etwe
en
th
e
two
g
r
o
u
p
s
[
1
9
]
.
T
h
e
u
s
e
o
f
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
to
p
r
o
ce
s
s
q
E
E
G
s
ig
n
als
o
f
f
er
s
n
u
m
er
o
u
s
ad
v
a
n
tag
es
,
s
u
ch
as
th
e
ab
ilit
y
to
h
an
d
le
lar
g
e
d
atasets
with
h
ig
h
ac
cu
r
ac
y
an
d
th
e
a
b
ilit
y
to
ad
d
r
ess
in
d
iv
id
u
al
v
a
r
iab
ilit
y
in
th
e
d
ata
[
2
0
]
.
Prin
cip
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A)
i
s
o
n
e
o
f
th
e
m
o
s
t
co
m
m
o
n
tech
n
iq
u
es p
air
ed
wi
th
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
to
im
p
r
o
v
e
d
ata
v
ar
iab
ilit
y
,
th
er
eb
y
en
h
an
ce
th
e
class
if
ier
p
er
f
o
r
m
a
n
ce
[
2
1
]
.
T
h
ese
s
tu
d
ies
d
em
o
n
s
tr
ate
th
at
m
ac
h
in
e
lear
n
in
g
ca
n
ass
is
t
m
ed
ical
p
r
ac
titi
o
n
er
in
th
e
ea
r
ly
clin
ical
d
iag
n
o
s
is
o
f
ADHD
[
2
2
]
.
2.
M
E
T
H
O
D
T
h
is
s
tu
d
y
in
v
o
lv
e
d
s
ix
s
tag
es
,
as
illu
s
tr
ated
in
Fig
u
r
e
1
.
T
h
e
s
tag
es
in
clu
d
ed
q
E
E
G
d
ata
a
cq
u
is
itio
n
,
p
r
ep
r
o
ce
s
s
in
g
,
p
r
o
ce
s
s
in
g
,
PC
A
,
clas
s
if
icatio
n
m
o
d
el
s
el
ec
tio
n
,
an
d
m
o
d
el
ev
alu
atio
n
.
Du
r
in
g
E
E
G
d
at
a
ac
q
u
is
itio
n
,
th
e
f
o
cu
s
was
o
n
g
ath
er
in
g
d
ata
f
r
o
m
v
ar
io
u
s
elec
tr
o
d
e
p
lace
m
en
ts
as
s
o
u
r
c
es
o
f
E
E
G
s
ig
n
als
.
Du
r
in
g
th
e
p
r
ep
r
o
ce
s
s
in
g
s
tag
e,
th
e
E
E
G
s
ig
n
als
wer
e
f
ilt
er
ed
to
r
em
o
v
e
an
y
u
n
w
an
te
d
n
o
is
e.
I
n
th
e
n
ex
t
s
tep
,
th
e
p
r
o
ce
s
s
in
g
s
tag
e
i
n
v
o
lv
ed
o
f
th
e
C
W
T
,
f
o
llo
wed
b
y
f
ea
tu
r
e
e
x
tr
ac
tio
n
f
r
o
m
th
e
d
ata
wh
er
e
th
e
q
E
E
G
d
ata
was
tr
an
s
f
o
r
m
ed
to
ex
tr
ac
t
f
ea
tu
r
es
n
ee
d
ed
f
o
r
th
e
class
if
ier
m
o
d
els.
T
h
e
p
r
o
ce
s
s
ed
q
E
E
G
d
ata
n
o
w
co
n
s
i
s
tin
g
o
f
th
ese
f
ea
tu
r
es,
th
en
wen
t
th
r
o
u
g
h
th
e
PC
A
s
tag
e,
wh
er
e
PC
A
was
p
er
f
o
r
m
ed
t
o
r
ed
u
ce
th
e
lo
w
v
ar
ian
ce
d
ata
a
n
d
em
p
h
as
ize
h
ig
h
er
-
v
ar
ian
ce
f
ea
tu
r
es.
Su
b
s
eq
u
en
tly
,
th
e
m
ac
h
in
e
le
ar
n
in
g
class
if
icatio
n
em
p
lo
y
ed
th
r
ee
ty
p
es
o
f
class
if
ier
m
o
d
els:
KNN,
m
u
lt
ilay
er
p
er
ce
p
tr
o
n
(
ML
P),
a
n
d
SVM.
Af
ter
s
elec
tin
g
th
e
class
if
icatio
n
m
o
d
el,
th
e
s
tu
d
y
p
r
o
ce
ed
e
d
to
th
e
m
o
d
el
e
v
alu
atio
n
s
tag
e,
wh
er
e
th
e
p
er
f
o
r
m
an
ce
m
etr
ics
wer
e
ex
am
in
ed
to
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
th
e
m
o
d
els.
Fig
u
r
e
1
.
Flo
w
ch
a
r
t o
f
t
h
e
r
es
ea
r
ch
s
tag
es
2
.
1
.
E
E
G
da
t
a
a
c
qu
is
it
io
n
T
h
is
s
tu
d
y
u
s
ed
s
ec
o
n
d
ar
y
d
atasets
f
r
o
m
two
s
o
u
r
ce
s
;
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
d
ataset
an
d
th
e
Per
ed
a
et
a
l.
[
2
4
]
d
ataset.
B
o
th
d
atasets
wer
e
co
llected
th
r
o
u
g
h
a
s
im
ilar
ex
p
er
im
en
t,
wh
ich
g
ath
er
ed
ey
es c
lo
s
ed
an
d
ey
es
o
p
en
ed
q
E
E
G
s
ig
n
als
f
r
o
m
th
e
p
ar
ticip
an
ts
wh
ile
p
er
f
o
r
m
in
g
v
is
u
al
an
d
co
g
n
itiv
e
task
s
.
Deta
iled
in
f
o
r
m
atio
n
r
eg
ar
d
in
g
b
o
th
d
atasets
is
p
r
o
v
id
ed
in
T
ab
le
1
.
T
h
e
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
d
ataset
was
o
b
tain
ed
f
r
o
m
th
e
I
E
E
E
Data
p
o
r
t p
latf
o
r
m
u
p
lo
ad
ed
b
y
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
f
r
o
m
th
e
T
eh
r
an
Un
iv
er
s
ity
o
f
Me
d
ical
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
580
-
1
5
8
7
1582
Scien
ce
s
,
T
eh
r
an
.
T
h
e
q
E
E
G
s
ig
n
al
d
ata
o
f
th
e
d
ataset
o
f
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
wer
e
r
ec
o
r
d
ed
u
s
in
g
an
SD
-
C
2
4
d
ev
ice
with
a
2
4
-
b
it
ADC
at
a
s
am
p
lin
g
r
ate
o
f
1
2
8
Hz
f
r
o
m
1
9
ch
an
n
els
lo
ca
ted
at
Fz,
C
z,
Pz,
C
3
,
T
3
,
C
4
,
T
4
,
Fp
1
,
Fp
2
,
F3
,
F4
,
F7
,
F8
,
P3
,
P4
,
T
5
,
T
6
,
O1
,
an
d
O2
.
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
co
n
d
u
cted
a
s
tu
d
y
with
60
p
ar
ticip
an
ts
,
in
clu
d
in
g
3
0
s
u
b
jects
with
ADHD
an
d
3
0
h
ea
lth
y
s
u
b
jects
.
T
h
e
Per
ed
a
et
a
l.
[
2
4
]
d
ataset
was
s
o
u
r
ce
d
f
r
o
m
th
e
Fig
s
h
ar
e
p
latf
o
r
m
an
d
u
p
lo
ad
ed
b
y
Per
ed
a
et
a
l.
[
2
4
]
to
th
e
Un
iv
er
s
ity
o
f
L
a
L
ag
u
n
a,
Sp
ain
.
E
E
G
ey
es
clo
s
ed
an
d
ey
es
o
p
en
ed
d
ata
f
r
o
m
th
e
3
3
p
ar
ticip
an
ts
wer
e
r
ec
o
r
d
ed
u
s
in
g
a
Nih
o
n
Ko
h
d
en
Neu
r
o
f
ax
E
E
G
-
9
2
0
0
d
ev
ice
with
a
1
6
-
b
it
ADC
at
a
s
am
p
lin
g
r
ate
o
f
2
5
6
Hz
f
r
o
m
eig
h
t
ch
an
n
els
lo
ca
ted
at
Fp
1
,
Fp
2
,
C
3
,
C
4
,
T
3
,
T
4
,
O1
,
an
d
O2
.
T
ab
le
1
.
Qu
a
n
titativ
e
E
E
G
d
at
asets
in
f
o
r
m
atio
n
S
o
u
r
c
e
P
a
r
t
i
c
i
p
a
n
t
s
S
a
mp
l
i
n
g
r
a
t
e
A
D
C
B
i
t
ADHD
C
o
n
t
r
o
l
M
o
h
a
mm
a
d
i
e
t
a
l
.
[
2
3
]
3
0
su
b
j
e
c
t
s (
2
2
L;
8
P
;
9
.
6
2
±
1
.
7
5
y
e
a
r
s
o
l
d
)
3
0
su
b
j
e
c
t
s
(
2
5
L
;
5
P
;
9
.
8
5
±
1
.
7
7
y
e
a
r
s
o
l
d
)
2
5
6
H
z
2
4
b
i
t
P
e
r
e
d
a
e
t
a
l
.
[
2
4
]
1
9
su
b
j
e
c
t
s
(
1
9
L
;
8
.
5
0
±
1
.
7
4
y
e
a
r
s
o
l
d
)
1
4
su
b
j
e
c
t
s
(
1
4
L
;
8
.
2
1
±
1
.
7
4
y
e
a
r
s
o
l
d
)
1
2
8
H
z
1
6
b
i
t
2
.
2
.
P
re
pro
ce
s
s
ing
R
aw
q
E
E
G
d
ata
m
u
s
t
b
e
p
r
e
p
r
o
ce
s
s
ed
to
e
n
s
u
r
e
th
e
d
ata
ar
e
f
r
ee
f
r
o
m
n
o
is
e
an
d
s
ig
n
a
l
ar
tifa
cts
.
B
o
th
d
atasets
wer
e
co
n
v
er
ted
in
to
d
ata
f
r
am
es
an
d
n
o
r
m
al
ized
f
r
o
m
d
ig
ital
s
ig
n
als
to
a
n
alo
g
v
o
ltag
e
d
ata
with
m
icr
o
v
o
lt
m
ag
n
itu
d
e
s
a
s
ex
p
r
ess
ed
in
(
1
)
.
Su
b
s
eq
u
e
n
tly
,
th
e
s
en
s
o
r
p
o
s
itio
n
m
o
n
tag
e
was
ar
r
an
g
ed
ac
co
r
d
in
g
to
th
e
in
ter
n
atio
n
a
l
1
0
-
2
0
s
tan
d
ar
d
m
o
n
tag
e
s
y
s
tem
f
o
r
1
9
an
d
8
c
h
an
n
els.
T
h
e
o
u
t
p
u
t
o
f
th
e
co
n
v
er
s
io
n
f
r
o
m
a
d
ig
ital
s
ig
n
al
to
an
alo
g
r
eq
u
ir
e
s
k
n
o
wled
g
e
o
f
th
e
d
ev
ice
r
ef
er
en
ce
v
o
l
tag
e
an
d
a
n
alo
g
-
to
-
d
ig
ital c
o
n
v
er
te
r
b
it
d
e
p
th
,
as
p
r
o
v
id
e
d
in
th
e
d
ataset
in
f
o
r
m
atio
n
.
=
2
−
1
∙
(
1
)
2
.
3
.
P
r
o
ce
s
s
ing
2
.
3
.
1
.
Co
ntinuo
us
wa
v
elet
t
r
a
ns
f
o
rm
s
T
h
e
wav
elet
tr
an
s
f
o
r
m
is
an
o
r
th
o
g
o
n
al
f
u
n
ctio
n
th
at
s
er
v
es
as
a
to
o
l
to
d
ec
o
m
p
o
s
e
d
ata,
f
u
n
ctio
n
s
,
o
r
o
p
er
ato
r
s
in
to
d
if
f
er
en
t
f
r
eq
u
en
cy
co
m
p
o
n
e
n
ts
an
d
th
en
a
n
aly
ze
ea
ch
co
m
p
o
n
en
t
with
a
r
eso
lu
tio
n
ad
ap
ted
to
its
s
ca
le
.
A
s
ig
n
al
in
tim
e
-
d
o
m
ain
is
p
r
o
ce
s
s
ed
u
s
in
g
th
e
wav
elet
tr
an
s
f
o
r
m
in
f
r
eq
u
e
n
cy
-
d
o
m
ain
s
ig
n
als
with
in
a
s
p
ec
if
ied
f
r
eq
u
en
cy
r
an
g
e,
g
en
er
ate
tim
e
-
f
r
eq
u
en
cy
c
o
ef
f
icie
n
ts
.
On
e
ty
p
e
o
f
wav
elet
tr
an
s
f
o
r
m
atio
n
,
th
e
C
W
T
,
in
v
o
lv
es
p
r
o
ce
s
s
in
g
a
s
ig
n
al
with
a
s
p
ec
if
ied
co
n
tin
u
o
u
s
f
r
e
q
u
en
cy
r
at
h
er
th
an
d
is
cr
ete
f
r
eq
u
en
c
y
in
ter
v
als
.
T
h
e
r
esu
ltin
g
wav
elet
co
ef
f
icie
n
t
was
u
s
ed
as
th
e
b
asis
f
o
r
f
ea
tu
r
e
ex
tr
ac
tio
n
f
o
r
ea
ch
ch
an
n
el
p
er
s
u
b
ject
[
2
5
]
.
C
W
T
en
ab
les
th
e
tim
e
-
f
r
e
q
u
en
cy
an
aly
s
is
o
f
a
s
ig
n
al,
allo
win
g
th
e
id
en
tific
atio
n
an
d
ch
ar
ac
ter
izatio
n
o
f
f
ea
tu
r
es
at
d
if
f
er
en
t
s
ca
les
with
in
th
e
s
i
g
n
al.
T
h
is
tr
an
s
f
o
r
m
atio
n
is
b
ased
o
n
th
e
u
s
e
o
f
a
s
ca
led
co
n
tin
u
o
u
s
an
d
s
h
if
ted
wav
elet
f
u
n
ctio
n
[
2
6
]
.
T
h
e
C
W
T
o
f
a
s
ig
n
al
(
t)
with
r
es
p
ec
t
to
a
wav
elet
f
u
n
ctio
n
(
)
is
d
ef
in
ed
as
th
e
m
u
ltip
licatio
n
o
f
th
e
o
r
ig
in
al
s
ig
n
al
an
d
s
h
if
ted
an
d
s
ca
led
wav
elet
f
u
n
ctio
n
s
in
th
e
tim
e
d
o
m
ain
.
T
h
e
m
ath
e
m
atica
l f
o
r
m
u
la
f
o
r
th
e
CWT
i
s
ex
p
r
ess
ed
in
(
2
)
as f
o
llo
ws:
(
,
)
=
|
|
−
1
2
∙
∫
[
(
)
]
∙
[
−
]
(
2
)
wh
er
e
r
ep
r
esen
ts
th
e
s
ca
le
f
a
cto
r
,
b
r
ep
r
esen
ts
th
e
tr
an
s
latio
n
f
ac
to
r
,
(
t)
r
ep
r
esen
ts
th
e
o
r
ig
in
al
s
ig
n
al,
an
d
[
−
]
r
ep
r
esen
ts
th
e
s
ca
led
an
d
s
h
i
f
ted
wav
elet
f
u
n
ctio
n
s
.
T
h
e
t
y
p
e
o
f
wav
elet
u
s
ed
i
n
th
is
s
t
u
d
y
was
th
e
Mo
r
let
wav
elet
with
th
e
m
ath
e
m
atica
l f
o
r
m
u
la
in
(
3
)
with
:
(
)
=
e
xp
−
2
2
c
os
(
5
)
(
3
)
2
.
3
.
2
.
F
ea
t
ure
e
x
t
ra
ct
i
o
n
Featu
r
e
ex
tr
ac
tio
n
in
v
o
lv
e
d
th
e
ex
tr
ac
t
io
n
of
th
e
f
ea
tu
r
es
f
r
o
m
th
e
E
E
G
s
ig
n
als
o
f
ea
ch
c
h
an
n
el
p
e
r
p
ar
ticip
an
t
in
b
o
th
d
atasets
.
T
h
e
p
ar
ticip
a
n
t
q
E
E
G
d
ata
wer
e
d
iv
id
ed
in
to
f
r
eq
u
en
cy
b
a
n
d
s
u
s
in
g
th
e
wa
v
elet
m
eth
o
d
with
1
-
4
Hz
f
o
r
d
elta,
4
-
8
Hz
f
o
r
th
eta,
8
-
16
Hz
f
o
r
al
p
h
a,
1
6
-
3
2
Hz
f
o
r
b
eta,
an
d
3
2
-
6
4
Hz
f
o
r
g
am
m
a
b
a
n
d
s
.
T
h
e
C
W
T
m
eth
o
d
g
en
er
ates
two
c
o
ef
f
icien
ts
:th
o
s
e
with
lo
w
-
f
r
eq
u
e
n
cy
i
n
f
o
r
m
atio
n
ar
e
ca
lled
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:
2
5
0
2
-
4
7
52
A
tten
tio
n
d
eficit
a
n
d
h
yp
era
cti
vity
d
is
o
r
d
er c
la
s
s
if
ica
tio
n
…
(
S
yifa
n
i I
h
fa
d
z
a
A
liya
h
)
1583
ap
p
r
o
x
im
atio
n
co
e
f
f
icien
ts
,
a
n
d
th
o
s
e
with
h
ig
h
-
f
r
eq
u
e
n
c
y
in
f
o
r
m
atio
n
ar
e
ca
lled
d
eta
il
co
ef
f
icien
ts
.
T
h
e
co
ef
f
icien
ts
f
r
o
m
th
e
C
W
T
wer
e
co
m
p
u
ted
m
at
h
em
atica
lly
to
o
b
tain
f
o
u
r
f
ea
tu
r
es;
th
e
av
er
ag
e
o
f
s
ig
n
al
p
o
wer
o
r
e
n
er
g
y
,
s
ig
n
al
en
tr
o
p
y
,
m
ea
n
,
a
n
d
s
tan
d
ar
d
d
ev
iat
io
n
.
T
h
er
e
f
o
r
e,
th
e
t
o
tal
f
ea
tu
r
es
g
en
er
ated
f
r
o
m
ea
ch
p
ar
ticip
an
t
wo
u
ld
b
e
th
e
s
u
m
o
f
all
ch
an
n
els
m
u
ltip
lied
b
y
th
e
two
co
e
f
f
icien
ts
an
d
f
o
u
r
f
ea
t
u
r
es,
r
esu
ltin
g
in
a
to
tal
o
f
1
5
2
f
ea
t
u
r
es
p
er
p
ar
ticip
an
t
f
r
o
m
th
e
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
d
atase
t
with
1
9
ch
an
n
els
an
d
6
4
f
ea
tu
r
es
p
e
r
p
ar
ticip
an
t
f
r
o
m
th
e
Per
ed
a
et
a
l.
d
ataset.
T
h
e
to
tal
n
u
m
b
er
o
f
f
ea
tu
r
es
f
o
r
ea
ch
p
ar
ticip
an
t
was
m
u
ltip
lied
b
y
f
iv
e
f
r
e
q
u
en
c
y
b
a
n
d
s
.
T
h
er
ef
o
r
e,
th
e
n
u
m
b
er
o
f
in
p
u
ts
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els wa
s
3
0
0
f
o
r
th
e
M
o
h
a
m
m
ad
i
et
a
l.
[
2
3
]
d
ataset
an
d
1
6
5
f
o
r
th
e
Per
e
d
a
et
a
l.
[
2
4
]
d
ataset.
2
.
4
.
P
rincipa
l
co
m
po
nent
a
na
ly
s
is
PC
A
is
ca
p
ab
le
o
f
r
ed
u
cin
g
clu
s
ter
s
o
f
f
ea
tu
r
es
wi
th
lo
w
v
ar
ian
ce
,
th
er
eb
y
g
e
n
er
ati
n
g
h
ig
h
er
v
ar
ian
ce
th
at
co
u
ld
s
ig
n
i
f
ican
tly
in
cr
ea
s
e
th
e
p
er
f
o
r
m
an
c
e
o
f
class
if
icatio
n
m
o
d
els
[
2
7
]
.
T
h
e
eig
e
n
v
alu
e
r
ep
r
esen
ts
th
e
am
o
u
n
t
o
f
v
ar
i
an
ce
ca
p
tu
r
e
d
b
y
ea
ch
p
r
i
n
c
ip
al
co
m
p
o
n
en
t,
in
d
icatin
g
th
e
s
ig
n
if
ican
ce
o
f
th
e
co
r
r
esp
o
n
d
in
g
eig
e
n
v
ec
to
r
in
d
escr
ib
in
g
th
e
v
ar
iab
ilit
y
o
f
th
e
d
ata.
T
h
e
Kaiser
-
Gu
ttm
an
r
u
le
s
u
g
g
ests
r
etain
in
g
p
r
in
cip
al
co
m
p
o
n
e
n
t
s
with
eig
en
v
alu
es
g
r
ea
ter
t
h
an
1
,
in
d
icatin
g
th
at
t
h
ey
ex
p
lain
m
o
r
e
v
ar
ia
n
ce
th
an
p
r
in
cip
al
co
m
p
o
n
en
ts
wit
h
eig
en
v
alu
es
b
elo
w
th
e
lin
e;
th
ey
ar
e
co
n
s
id
er
ed
s
ig
n
if
ican
t
f
o
r
an
aly
s
is
[
2
8
]
.
T
h
e
r
esu
ltin
g
co
m
p
o
n
en
ts
o
f
th
e
PC
A
wer
e
v
is
u
alize
d
u
s
in
g
a
s
cr
ee
p
lo
t
to
s
h
o
w
h
o
w
th
e
d
ata
v
ar
ian
ce
ch
an
g
ed
wh
en
PC
A
was
ap
p
lied
.
Kaiser
’
s
lin
e
h
el
p
s
e
n
s
u
r
e
th
e
r
eten
tio
n
o
f
r
ep
r
e
s
en
tativ
e
p
r
in
cip
al
co
m
p
o
n
en
t
o
f
th
e
b
r
o
ad
er
p
o
p
u
latio
n
,
wh
ich
ca
n
b
e
r
elied
u
p
o
n
f
o
r
f
u
r
th
e
r
an
aly
s
is
an
d
in
ter
p
r
etatio
n
d
u
e
to
th
e
ir
h
ig
h
v
ar
ian
ce
d
ata.
T
h
is
lin
e
is
cr
u
cial
f
o
r
r
eliab
le
d
ata
an
aly
s
is
b
ec
au
s
e
it e
n
s
u
r
es
b
o
th
th
e
v
ar
ia
n
ce
an
d
r
eliab
ilit
y
o
f
th
e
f
ea
tu
r
e
r
e
d
u
ct
io
n
p
r
o
ce
s
s
.
2
.
5
.
M
a
chine
lea
rning
cla
s
s
i
f
ica
t
io
n
T
h
e
class
if
icatio
n
o
f
s
u
b
jects
f
alls
u
n
d
er
th
e
ca
teg
o
r
y
o
f
s
u
p
er
v
is
ed
lear
n
in
g
in
th
e
m
ac
h
in
e
lear
n
in
g
ar
ea
[
2
9
]
.
T
h
er
e
ar
e
v
ar
io
u
s
s
u
p
er
v
is
ed
lear
n
in
g
alg
o
r
ith
m
s
,
s
u
ch
as
KNN,
ML
P,
an
d
SVM.
KNN
is
th
e
f
u
n
d
am
e
n
tal
co
n
ce
p
t
o
f
f
in
d
in
g
k
-
n
ea
r
est
d
ata
p
o
i
n
ts
f
r
o
m
t
h
e
d
ata
to
b
e
class
if
ied
an
d
d
e
ter
m
in
in
g
th
e
class
lab
el
th
at
m
o
s
t
f
r
eq
u
e
n
tly
a
p
p
ea
r
s
am
o
n
g
th
e
k
-
n
eig
h
b
o
r
s
[
3
0
]
.
KNN
is
p
a
r
ticu
lar
ly
a
d
v
an
tag
e
o
u
s
wh
en
d
ea
lin
g
with
co
m
p
lex
an
d
n
o
n
lin
ea
r
p
atter
n
s
in
d
ata,
m
a
k
in
g
it
s
u
itab
le
f
o
r
task
s
wh
er
e
th
e
u
n
d
er
l
y
in
g
d
is
tr
ib
u
tio
n
is
n
o
t
well
u
n
d
er
s
to
o
d
o
r
is
h
ig
h
l
y
ir
r
e
g
u
lar
.
T
h
e
ML
P
is
a
ty
p
e
o
f
n
eu
r
al
n
et
wo
r
k
with
an
in
p
u
t
lay
er
,
h
id
d
en
lay
e
r
s
,
an
d
o
u
tp
u
t
lay
er
.
I
t
u
tili
ze
s
weig
h
ts
an
d
b
iases
to
tr
an
s
f
o
r
m
in
p
u
t
d
a
ta
th
r
o
u
g
h
m
u
ltip
le
lay
er
s
an
d
lear
n
s
co
m
p
lex
p
atter
n
s
d
u
r
in
g
tr
ai
n
in
g
.
ML
Ps
ar
e
co
m
m
o
n
ly
em
p
lo
y
ed
f
o
r
task
s
s
u
ch
as
class
if
icatio
n
an
d
r
eg
r
ess
io
n
in
m
ac
h
in
e
-
le
ar
n
in
g
ap
p
lic
atio
n
s
,
ca
p
ab
le
o
f
lear
n
in
g
co
m
p
lex
f
ea
tu
r
e
r
ep
r
esen
tatio
n
s
an
d
ac
h
iev
e
a
h
ig
h
p
r
ed
ictiv
e
ac
c
u
r
ac
y
,
p
ar
ticu
lar
ly
i
n
lar
g
e
-
s
ca
le
d
a
tasets
with
d
iv
er
s
e
f
ea
tu
r
es
[
3
1
]
.
SVM
ar
e
am
o
n
g
th
e
m
o
s
t
f
a
v
o
r
ed
m
ac
h
in
e
lear
n
in
g
al
g
o
r
ith
m
s
f
o
r
class
if
icatio
n
an
d
r
eg
r
ess
io
n
.
SVM
aim
s
to
f
in
d
th
e
b
est
h
y
p
er
p
lan
e
th
at
ca
n
s
ep
ar
ate
th
e
two
class
e
s
in
th
e
g
iv
en
d
ata.
T
h
is
h
y
p
er
p
lan
e
is
ch
o
s
en
b
y
m
a
x
im
izin
g
th
e
m
ar
g
in
,
wh
ich
is
th
e
d
is
tan
ce
b
etwe
en
th
e
h
y
p
er
p
lan
e
an
d
th
e
n
ea
r
est
p
o
in
ts
f
r
o
m
ea
ch
cla
s
s
[
3
2
]
.
SVM
is
esp
ec
ially
ef
f
ec
tiv
e
wh
en
m
a
n
ag
in
g
d
a
ta
with
n
u
m
er
o
u
s
d
im
en
s
io
n
s
an
d
ca
n
h
an
d
le
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
s
b
etwe
en
f
ea
tu
r
es
u
s
in
g
k
er
n
el
f
u
n
ctio
n
s
.
I
ts
ab
ilit
y
t
o
f
i
n
d
an
o
p
tim
al
h
y
p
er
p
lan
e
an
d
m
ax
im
ize
th
e
m
ar
g
in
m
ak
es
it
r
o
b
u
s
t
to
o
v
er
f
itti
n
g
an
d
en
s
u
r
es
a
g
o
o
d
g
en
er
aliza
tio
n
p
er
f
o
r
m
an
ce
.
2
.
6
.
M
o
del
e
v
a
lua
t
i
o
n
Mo
d
el
ev
alu
atio
n
is
th
e
last
s
tep
o
f
th
is
r
ese
ar
ch
m
eth
o
d
,
an
d
th
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
was
ev
alu
at
ed
b
ased
o
n
th
e
ac
cu
r
ac
y
o
f
th
e
test
in
g
a
n
d
tr
ai
n
in
g
o
f
f
ea
tu
r
e
d
atasets
.
T
h
e
p
er
f
o
r
m
an
ce
s
o
f
th
e
th
r
ee
class
if
ier
s
wer
e
ev
alu
ate
d
u
s
in
g
co
n
f
u
s
io
n
m
atr
ices.
Ad
d
itio
n
ally
,
th
e
ac
cu
r
ac
ies o
f
th
e
class
if
ier
s
b
e
f
o
r
e
an
d
af
ter
t
h
e
im
p
lem
en
tat
io
n
o
f
PC
A
wer
e
co
m
p
ar
ed
[
3
3
]
.
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
3
.
1
.
P
rincipa
l
co
m
po
nent
a
na
l
y
s
is
T
h
e
f
ea
tu
r
es
o
b
tain
ed
f
r
o
m
th
e
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
an
d
Per
ed
a
et
a
l.
[
2
4
]
d
atasets
wer
e
r
ed
u
ce
d
f
r
o
m
1
5
2
an
d
6
4
f
ea
tu
r
es,
r
es
p
ec
tiv
ely
,
an
d
wer
e
r
e
d
u
ce
d
u
s
in
g
PC
A
to
im
p
r
o
v
e
m
o
d
el
p
er
f
o
r
m
a
n
ce
.
T
h
e
r
ed
u
ce
d
f
ea
tu
r
es
wer
e
v
is
u
aliz
ed
u
s
in
g
a
s
cr
ee
p
lo
t,
as
illu
s
tr
ated
in
Fig
u
r
e
2
,
to
s
h
o
w
h
o
w
th
e
d
ata
v
ar
ia
n
ce
ch
an
g
ed
wh
e
n
PC
A
wa
s
a
p
p
li
ed
.
T
h
e
r
elatio
n
s
h
ip
b
etwe
en
t
h
e
p
r
in
cip
al
co
m
p
o
n
en
ts
an
d
e
ig
en
v
alu
es sh
o
wed
th
at
th
e
n
u
m
b
er
o
f
eig
en
v
alu
es
ten
d
ed
to
d
ec
r
ea
s
e
w
h
en
th
e
p
r
in
cip
al
c
o
m
p
o
n
en
t
in
cr
e
ased
,
r
ep
r
esen
tin
g
a
d
ec
r
ea
s
e
in
th
e
am
o
u
n
t o
f
v
ar
i
an
ce
.
T
h
e
r
ef
o
r
e
,
Kaiser
’
s
lin
e
at
an
eig
e
n
v
alu
e
o
f
o
n
e
was
u
t
ilized
to
d
eter
m
in
e
th
e
p
r
in
cip
al
co
m
p
o
n
en
ts
with
h
ig
h
-
v
a
r
ian
ce
.
Fig
u
r
e
2
(
a
)
illu
s
tr
ates
th
e
PC
A
s
cr
ee
p
lo
t
o
f
th
e
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
d
ataset,
w
h
ich
s
h
o
ws
th
at
th
e
r
e
a
r
e
2
2
h
ig
h
-
v
ar
ian
ce
co
m
p
o
n
en
ts
a
b
o
v
e
th
e
Kaiser
’
s
lin
e.
Fig
u
r
e
2
(
b
)
s
h
o
ws
th
at
th
er
e
ar
e
1
2
h
ig
h
v
ar
ia
n
ce
co
m
p
o
n
en
ts
d
er
iv
ed
f
r
o
m
th
e
Per
ed
a
et
a
l.
[
2
4
]
d
ataset.
T
h
ese
co
m
p
o
n
en
ts
wer
e
u
s
e
d
as
f
ea
tu
r
es
f
o
r
class
if
ier
s
t
o
im
p
r
o
v
e
th
e
p
er
f
o
r
m
an
ce
m
o
d
elin
g
.
Alth
o
u
g
h
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
580
-
1
5
8
7
1584
p
r
o
ce
s
s
in
g
ad
d
itio
n
al
co
m
p
o
n
en
ts
with
lo
wer
v
a
r
ian
ce
m
ay
len
g
th
e
n
th
e
tr
ain
in
g
p
r
o
ce
s
s
,
it
d
o
es
n
o
t
s
u
b
s
tan
tially
alter
th
e
m
o
d
el
r
esu
lts
an
d
ca
n
th
er
ef
o
r
e
b
e
d
is
r
eg
ar
d
e
d
.
(
a)
(
b
)
Fig
u
r
e
2
.
Scr
ee
p
lo
t o
f
(
a)
2
2
c
o
m
p
o
n
en
ts
an
d
(
b
)
1
2
co
m
p
o
n
en
ts
3
.
2
.
M
a
chine
lea
rning
cla
s
s
i
f
ica
t
io
n
T
h
e
r
ed
u
ce
d
d
ata
wer
e
th
en
r
an
d
o
m
ly
p
ar
titi
o
n
ed
in
to
test
in
g
an
d
tr
ain
in
g
s
ets
at
r
atio
s
o
f
2
0
%
an
d
8
0
%,
r
esp
ec
tiv
ely
.
Su
b
s
eq
u
en
tly
,
th
e
SVM,
KNN,
an
d
ML
P
class
if
ier
s
wer
e
u
tili
ze
d
to
class
if
y
ADHD
.
Fig
u
r
e
3
s
h
o
ws
th
e
co
n
f
u
s
io
n
m
atr
ice
s
co
n
tain
in
g
th
e
f
o
u
r
v
alu
es
o
f
th
e
m
o
d
el
p
r
ed
ictio
n
o
u
tco
m
e
f
r
o
m
th
e
th
r
ee
class
if
ier
s
.
T
r
u
e
-
p
o
s
itiv
e
r
ef
er
s
to
th
e
n
u
m
b
er
o
f
p
atien
ts
co
r
r
ec
tly
d
iag
n
o
s
ed
with
ADHD
an
d
tr
u
e
-
n
eg
ativ
e
r
ef
er
s
to
th
e
n
u
m
b
er
o
f
p
atien
ts
co
r
r
ec
tly
d
iag
n
o
s
ed
as
n
eu
r
o
ty
p
ical.
Fals
e
-
p
o
s
itiv
e
d
escr
ib
es
th
e
n
u
m
b
er
o
f
p
atien
ts
in
co
r
r
ec
tly
d
iag
n
o
s
ed
with
ADHD
an
d
f
alse
-
n
eg
ativ
e
d
escr
ib
es th
e
n
u
m
b
er
o
f
p
atien
ts
in
co
r
r
ec
tly
d
iag
n
o
s
ed
as
n
eu
r
o
ty
p
ical.
A
p
o
s
itiv
e
v
alu
e
is
r
ep
r
esen
ted
b
y
“1
”
an
d
a
n
eg
ativ
e
v
alu
e
is
r
ep
r
esen
ted
b
y
“0
”.
Fig
u
r
e
3
(
a)
illu
s
tr
ates
th
e
co
n
f
u
s
io
n
m
atr
ix
o
f
th
e
SVM,
Fig
u
r
e
3
(
b
)
th
e
KNN,
an
d
Fig
u
r
e
3
(
c)
th
e
ML
P.
T
h
e
p
er
f
o
r
m
an
ce
o
f
a
class
if
ier
is
co
n
s
id
er
ed
g
o
o
d
wh
en
th
e
co
n
f
u
s
io
n
m
atr
ix
ex
h
ib
its
a
lar
g
er
o
u
tp
u
t in
th
e
tr
u
e
-
p
o
s
itiv
e
an
d
tr
u
e
-
n
eg
ativ
e
ce
lls
.
(
a)
(
b
)
(
c)
Fig
u
r
e
3
.
C
o
n
f
u
s
io
n
m
atr
i
x
o
f
class
if
ier
s
: (
a)
SVM,
(
b
)
KNN,
an
d
(
c
)
ML
P
4.
M
O
DE
L
E
V
AL
U
AT
I
O
N
T
h
e
class
if
ier
s
u
s
ed
in
th
e
m
o
d
el
wer
e
th
e
ML
P,
SVM,
an
d
KNN.
T
h
r
ee
class
if
ier
s
wer
e
em
p
lo
y
ed
to
co
m
p
ar
e
th
e
m
o
d
els
f
o
r
class
if
y
in
g
q
E
E
G
d
ata
an
d
a
cq
u
ir
e
th
e
m
o
s
t
ac
cu
r
ate
an
d
r
o
b
u
s
t
m
o
d
el
f
o
r
class
if
icatio
n
.
T
h
e
Mo
h
a
m
m
a
d
i
et
a
l.
[
2
3
]
a
n
d
Per
ed
a
et
a
l
.
[
2
4
]
d
atasets
wer
e
co
m
b
i
n
ed
to
im
p
r
o
v
e
m
o
d
el
g
en
er
aliza
tio
n
an
d
in
cr
ea
s
e
th
e
am
o
u
n
t
o
f
tr
ain
in
g
d
ata.
T
h
e
co
m
b
in
ed
d
ataset
was
th
en
p
r
o
ce
s
s
ed
th
r
o
u
g
h
class
if
ier
s
b
o
th
with
PC
A
an
d
with
o
u
t
PC
A
to
ev
alu
ate
th
e
ef
f
ec
t
o
f
PC
A.
T
h
e
im
p
a
ct
o
f
PC
A
f
ea
tu
r
e
r
ed
u
ctio
n
o
n
th
e
p
e
r
f
o
r
m
an
ce
o
f
class
if
ier
s
was
an
aly
ze
d
f
r
o
m
th
e
p
r
o
v
id
ed
d
ata
in
T
a
b
l
e
s
2
an
d
3
.
Acr
o
s
s
th
e
th
r
ee
class
if
ier
s
,
ap
p
ly
in
g
PC
A
r
esu
lted
in
a
d
ec
r
ea
s
e
in
ac
cu
r
ac
y
f
o
r
b
o
th
tr
ain
in
g
a
n
d
test
in
g
d
atasets
co
m
p
ar
ed
to
s
ce
n
ar
i
o
s
with
o
u
t
PC
A
as
s
h
o
wn
in
T
ab
le
2
.
T
h
is
s
u
g
g
ests
th
at
th
e
PC
A
f
ea
t
u
r
e
r
ed
u
ctio
n
m
ig
h
t
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:
2
5
0
2
-
4
7
52
A
tten
tio
n
d
eficit
a
n
d
h
yp
era
cti
vity
d
is
o
r
d
er c
la
s
s
if
ica
tio
n
…
(
S
yifa
n
i I
h
fa
d
z
a
A
liya
h
)
1585
n
o
t
h
av
e
e
f
f
ec
tiv
ely
ca
p
tu
r
ed
th
e
u
n
d
e
r
ly
in
g
p
atter
n
s
in
th
e
d
ata
f
o
r
th
is
s
tu
d
y
.
W
h
en
PC
A
was
n
o
t
ap
p
lied
,
th
e
h
ig
h
est
tr
ain
in
g
ac
cu
r
ac
y
o
f
9
5
.
6
8
%
was
ac
h
iev
ed
b
y
th
e
ML
P
cla
s
s
if
ier
,
b
u
t
it
s
tes
ti
n
g
ac
cu
r
ac
y
d
r
o
p
p
e
d
s
ig
n
if
ican
tly
to
5
9
.
2
1
%,
in
d
ic
atin
g
p
o
ten
tial
o
v
e
r
f
itti
n
g
.
Similar
ly
,
a
h
ig
h
tr
ain
i
n
g
ac
cu
r
ac
y
of
8
7
.
3
1
%
was
ac
h
iev
ed
b
y
th
e
SVM,
b
u
t
it
ex
h
ib
ited
a
l
o
wer
test
in
g
ac
cu
r
ac
y
o
f
5
3
.
9
5
%,
s
u
g
g
esti
n
g
s
o
m
e
d
eg
r
ee
o
f
o
v
er
f
itti
n
g
.
I
n
co
n
tr
ast,
K
NN
d
is
p
lay
ed
m
o
d
e
r
ate
tr
ain
i
n
g
ac
cu
r
ac
y
o
f
7
4
.
0
7
%
an
d
th
e
h
ig
h
est
test
in
g
ac
cu
r
ac
y
o
f
6
1
.
8
4
%
am
o
n
g
th
e
th
r
ee
class
if
ier
s
with
o
u
t
PC
A.
Ho
wev
er
,
af
ter
PC
A
was
im
p
lem
en
ted
,
th
e
ac
cu
r
ac
y
o
f
all
class
if
ier
s
d
ec
r
ea
s
ed
ex
ce
p
t
th
e
KNN
.
T
h
e
ML
P
class
if
ier
s
till
ac
h
iev
ed
th
e
h
ig
h
est
tr
ain
in
g
ac
cu
r
ac
y
o
f
8
6
.
2
7
%
b
u
t
ex
p
e
r
ien
ce
d
a
s
ig
n
if
ican
t
d
r
o
p
in
test
in
g
ac
cu
r
ac
y
with
5
3
.
9
5
%
.
T
h
e
ac
cu
r
ac
y
o
f
SVM
also
d
ec
lin
ed
,
r
ea
ch
in
g
8
0
.
1
7
%
in
tr
ain
in
g
an
d
5
1
.
3
1
%
in
test
in
g
with
P
C
A
ap
p
lie
d
.
I
n
ter
esti
n
g
ly
,
th
e
KNN
test
in
g
ac
cu
r
ac
y
im
p
r
o
v
ed
to
6
9
.
2
1
%
with
PC
A,
in
d
i
ca
tin
g
a
p
o
ten
tial
im
p
r
o
v
em
e
n
t
in
g
en
er
aliza
tio
n
.
W
h
ile
ML
P
co
n
s
is
ten
tly
o
u
tp
er
f
o
r
m
ed
SVM
an
d
KNN
in
ter
m
o
f
tr
ain
in
g
ac
cu
r
ac
y
b
u
t
s
u
f
f
er
e
d
f
r
o
m
o
v
er
f
itti
n
g
is
s
u
es
.
SVM
,
o
n
th
e
o
th
er
h
an
d
,
d
em
o
n
s
tr
ated
g
r
ea
ter
r
o
b
u
s
tn
ess
t
o
o
v
er
f
itti
n
g
b
u
t
s
h
o
wed
lo
wer
o
v
er
all
ac
c
u
r
ac
y
o
v
er
all,
p
ar
ti
cu
lar
ly
wh
e
n
PC
A
was
u
s
ed
.
Ultim
ately
,
th
e
c
h
o
ice
b
etwe
en
ap
p
ly
in
g
PC
A
o
r
n
o
t
d
ep
en
d
s
o
n
th
e
s
p
ec
if
ic
r
eq
u
ir
em
en
ts
o
f
t
h
e
class
if
i
ca
tio
n
task
an
d
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
f
ea
tu
r
e
r
ed
u
ctio
n
a
n
d
ac
c
u
r
ac
y
.
T
ab
le
2
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
o
f
class
if
ier
s
with
o
u
t PC
A
C
l
a
s
si
f
i
e
r
P
a
r
a
me
t
e
r
s
A
c
c
u
r
a
c
y
Tr
a
i
n
i
n
g
(
%)
Te
st
i
n
g
(
%)
M
LP
h
i
d
d
e
n
l
a
y
e
r
(
3
4
,
3
4
,
3
4
)
9
5
.
6
8
5
9
.
2
1
S
V
M
3
4
3
5
s
u
p
p
o
r
t
v
e
c
t
o
r
s
8
7
.
3
1
5
3
.
9
5
K
N
N
4
n
e
i
g
h
b
o
r
s
7
4
.
0
7
6
1
.
8
4
T
ab
le
3
.
Per
f
o
r
m
an
ce
co
m
p
a
r
is
o
n
o
f
class
if
ier
s
with
PC
A
C
l
a
s
si
f
i
e
r
P
a
r
a
me
t
e
r
s
A
c
c
u
r
a
c
y
Tr
a
i
n
i
n
g
(
%)
Te
st
i
n
g
(
%)
M
LP
h
i
d
d
e
n
l
a
y
e
r
(
3
4
,
3
4
,
3
4
)
8
6
.
2
7
5
3
.
9
5
S
V
M
3
4
3
5
s
u
p
p
o
r
t
v
e
c
t
o
r
s
8
0
.
1
7
5
1
.
3
1
K
N
N
4
n
e
i
g
h
b
o
r
s
7
7
.
3
4
6
9
.
2
1
T
h
e
co
m
p
ar
is
o
n
o
f
class
if
ier
s
f
o
r
ADHD
d
iag
n
o
s
is
f
r
o
m
o
th
er
r
esear
ch
,
as
s
h
o
wn
in
T
ab
le
4
,
r
ev
ea
led
v
ar
y
in
g
p
er
f
o
r
m
a
n
ce
lev
els
ac
r
o
s
s
d
if
f
er
en
t
s
tim
u
l
atio
n
task
s
.
Fo
r
in
s
tan
ce
,
in
a
s
tu
d
y
b
y
Alch
alab
i
et
a
l.
[
3
4
]
,
th
e
SVM
class
if
ie
r
ac
h
iev
ed
an
ex
ce
p
tio
n
al
ac
cu
r
ac
y
o
f
9
8
.
6
%
d
u
r
in
g
a
f
o
cu
s
ed
g
am
in
g
task
.
Similar
ly
,
Mo
h
am
m
ad
i
et
a
l.
[
2
3
]
r
ep
o
r
ted
an
ac
c
u
r
ac
y
o
f
9
3
.
7
%
u
s
in
g
an
ML
P
class
if
ier
d
u
r
in
g
a
v
is
u
al
co
g
n
itiv
e
task
,
wh
e
r
ea
s
Yan
g
et
a
l.
[
3
5
]
o
b
tain
ed
a
n
ac
cu
r
ac
y
o
f
8
9
.
3
%
with
a
KNN
class
i
f
ier
d
u
r
i
n
g
a
m
o
to
r
task
with
in
ter
f
er
en
ce
.
C
o
n
s
is
ten
t
with
th
ese
f
in
d
in
g
s
,
th
i
s
r
esear
ch
f
o
cu
s
ed
o
n
u
tili
zin
g
E
E
G
s
ig
n
als
f
o
r
ADHD
d
iag
n
o
s
is
th
r
o
u
g
h
q
u
an
titativ
e
an
aly
s
is
an
d
m
ac
h
i
n
e
lear
n
in
g
alg
o
r
ith
m
s
,
wh
ile
also
ex
p
lo
r
in
g
t
h
e
ef
f
icac
y
o
f
s
ig
n
al
attr
ib
u
tes
s
u
ch
as
p
o
wer
,
en
t
r
o
p
y
,
av
e
r
ag
e,
an
d
s
tan
d
ar
d
d
ev
iatio
n
u
s
in
g
s
ig
n
al
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
lik
e
C
W
T
.
T
h
ese
r
esu
lt
s
p
r
o
v
id
e
v
alu
a
b
le
in
s
ig
h
ts
in
to
th
e
p
o
ten
tial
o
f
E
E
G
-
b
ased
class
if
icatio
n
m
eth
o
d
s
f
o
r
ADHD
d
iag
n
o
s
is
an
d
h
ig
h
lig
h
t th
e
im
p
o
r
tan
ce
o
f
f
u
r
th
e
r
r
esear
ch
i
n
th
is
ar
ea
.
T
ab
le
4
.
C
lass
if
ier
co
m
p
ar
is
o
n
s
o
n
class
if
y
in
g
ADHD
S
t
i
m
u
l
a
t
i
o
n
Cla
s
si
f
i
e
r
A
c
c
u
r
a
c
y
R
e
f
e
r
e
n
c
e
F
o
c
u
se
d
g
a
m
i
n
g
S
V
M
c
l
a
ss
i
f
i
e
r
9
8
.
6
%
A
l
c
h
a
l
a
b
i
e
t
a
l
.
[
3
4
]
V
i
su
a
l
c
o
g
n
i
t
i
v
e
t
a
s
k
M
LP
c
l
a
ssi
f
i
e
r
9
3
.
7
%
M
o
h
a
mm
a
d
i
e
t
a
l
.
[
2
3
]
M
o
t
o
r
i
c
t
a
s
k
w
i
t
h
i
n
t
e
r
f
e
r
e
n
c
e
K
N
N
c
l
a
ss
i
f
i
e
r
8
9
.
3
%
Y
a
n
g
e
t
a
l
.
[
3
5
]
5.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
d
e
m
o
n
s
tr
ated
th
e
f
ea
s
ib
ilit
y
o
f
u
s
in
g
q
E
E
G
s
ig
n
als
f
o
r
ADHD
class
if
icatio
n
th
r
o
u
g
h
q
u
an
titativ
e
an
aly
s
is
an
d
m
ac
h
in
e
lear
n
i
n
g
alg
o
r
ith
m
s
,
e
x
tr
ac
tin
g
f
ea
tu
r
es
s
u
c
h
as
p
o
wer
,
en
tr
o
p
y
,
av
er
ag
e
,
an
d
s
tan
d
a
r
d
d
ev
iatio
n
v
ia
t
h
e
CWT
.
PC
A
aid
ed
in
th
e
e
x
tr
ac
tio
n
o
f
h
i
g
h
v
ar
ian
ce
f
ea
tu
r
es,
r
ed
u
cin
g
o
v
er
f
itti
n
g
an
d
en
h
a
n
cin
g
cla
s
s
if
icatio
n
ac
cu
r
ac
y
.
Ho
we
v
e
r
,
th
e
im
p
ac
t
o
f
PC
A
v
ar
ied
d
ep
en
d
in
g
o
n
t
h
e
d
ataset
an
d
class
if
ier
u
tili
ze
d
.
No
tab
ly
,
th
e
SVM
class
if
ie
r
o
u
tp
er
f
o
r
m
e
d
th
e
o
th
e
r
s
,
ac
h
iev
in
g
a
5
3
.
9
5
%
tes
tin
g
ac
cu
r
ac
y
d
esp
ite
its
lo
wer
tr
ain
in
g
ac
cu
r
ac
y
o
f
8
7
.
3
1
%,
s
h
o
wca
s
in
g
r
o
b
u
s
t
g
en
er
aliza
tio
n
.
C
o
n
v
er
s
ely
,
th
e
ML
P
class
if
ier
'
s
h
ig
h
tr
ain
in
g
ac
cu
r
ac
y
o
f
9
5
.
6
8
%
d
r
o
p
p
ed
s
ig
n
if
ica
n
tly
to
5
9
.
2
1
%
in
test
in
g
,
in
d
icatin
g
p
o
ten
tial
o
v
er
f
itti
n
g
is
s
u
es.
T
h
e
KNN
cl
ass
if
ier
p
er
f
o
r
m
e
d
co
m
p
etitiv
ely
,
with
a
6
1
.
8
4
%
test
in
g
ac
cu
r
ac
y
,
wh
ich
n
o
ta
b
ly
im
p
r
o
v
e
d
to
6
9
.
2
1
%
with
PC
A,
s
u
g
g
esti
n
g
en
h
an
c
ed
g
en
er
aliza
tio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
52
In
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
,
Vo
l.
3
7
,
No
.
3
,
Ma
r
ch
20
2
5
:
1
580
-
1
5
8
7
1586
T
h
is
s
tu
d
y
o
f
f
e
r
s
v
alu
ab
le
i
n
s
ig
h
ts
f
o
r
o
p
tim
izin
g
ADH
D
d
iag
n
o
s
is
u
s
in
g
q
E
E
G
s
ig
n
als,
em
p
h
asizin
g
class
if
ier
r
o
b
u
s
tn
ess
an
d
g
en
er
aliza
tio
n
.
T
h
e
f
in
d
i
n
g
s
co
u
ld
ass
is
t
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als
in
im
p
r
o
v
in
g
d
iag
n
o
s
is
ac
cu
r
ac
y
an
d
q
u
a
n
tify
in
g
ADHD
with
in
a
cl
in
ical
s
p
ec
tr
u
m
.
Fu
tu
r
e
r
esear
ch
i
n
r
ef
in
in
g
m
ac
h
in
e
lear
n
in
g
h
y
p
er
p
ar
am
eter
s
co
u
l
d
f
u
r
th
e
r
en
h
a
n
ce
class
if
ier
p
er
f
o
r
m
a
n
ce
,
co
n
t
r
ib
u
tin
g
t
o
m
o
r
e
ef
f
ec
tiv
e
ADHD
class
if
icatio
n
m
eth
o
d
s
tailo
r
ed
to
th
e
s
p
ec
if
ic
clin
ical
r
an
g
e
o
f
th
e
d
is
o
r
d
e
r
.
ACK
NO
WL
E
DG
E
M
E
N
TS
T
h
is
s
tu
d
y
was
s
u
p
p
o
r
te
d
b
y
th
e
Un
iv
er
s
itas
I
n
d
o
n
esia
R
esear
ch
Fu
n
d
(
Gr
a
n
t
PUTI
UI
Q3
No
.
NKB
-
2
3
5
/UN2
.
R
ST/HKP
.
0
5
.
0
0
/2
0
2
3
).
RE
F
E
R
E
NC
E
S
[
1
]
A
meric
a
n
P
s
y
c
h
i
a
t
r
i
c
A
ss
o
c
i
a
t
i
o
n
,
“
D
i
a
g
n
o
st
i
c
a
n
d
s
t
a
t
i
st
i
c
a
l
m
a
n
u
a
l
o
f
men
t
a
l
d
i
s
o
r
d
e
r
s
(
D
S
M
-
5
)
,
”
Am
e
r
i
c
a
n
Psy
c
h
i
a
t
r
i
c
P
u
b
,
2
0
1
3
.
[
2
]
G
.
P
o
l
a
n
c
z
y
k
,
“
T
h
e
w
o
r
l
d
w
i
d
e
p
r
e
v
a
l
e
n
c
e
o
f
A
D
H
D
:
a
s
y
s
t
e
ma
t
i
c
r
e
v
i
e
w
a
n
d
m
e
t
a
r
e
g
r
e
ssi
o
n
a
n
a
l
y
si
s
,
”
Am
e
ri
c
a
n
J
o
u
r
n
a
l
o
f
Psy
c
h
i
a
t
r
y
,
v
o
l
.
1
6
4
,
n
o
.
6
,
p
.
9
4
2
,
J
u
n
.
2
0
0
7
,
d
o
i
:
1
0
.
1
1
7
6
/
a
p
p
i
.
a
j
p
.
1
6
4
.
6
.
9
4
2
.
[
3
]
T.
H
.
E
o
m
a
n
d
Y
.
H
.
K
i
m,
“
C
l
i
n
i
c
a
l
p
r
a
c
t
i
c
e
g
u
i
d
e
l
i
n
e
s
f
o
r
a
t
t
e
n
t
i
o
n
-
d
e
f
i
c
i
t
/
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
:
r
e
c
e
n
t
u
p
d
a
t
e
s,”
C
l
i
n
i
c
a
l
a
n
d
Ex
p
e
ri
m
e
n
t
a
l
P
e
d
i
a
t
r
i
c
s
,
v
o
l
.
6
7
,
n
o
.
1
,
p
p
.
2
6
–
3
4
,
J
a
n
.
2
0
2
4
,
d
o
i
:
1
0
.
3
3
4
5
/
c
e
p
.
2
0
2
1
.
0
1
4
6
6
.
[
4]
A
.
A
l
i
m
a
n
d
M
.
H
.
I
mt
i
a
z
,
“
A
u
t
o
m
a
t
i
c
i
d
e
n
t
i
f
i
c
a
t
i
o
n
o
f
c
h
i
l
d
r
e
n
w
i
t
h
A
D
H
D
f
r
o
m
EEG
b
r
a
i
n
w
a
v
e
s,
”
S
i
g
n
a
l
s
,
v
o
l
.
4
,
n
o
.
1
,
p
p
.
1
9
3
–
2
0
5
,
F
e
b
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
9
0
/
si
g
n
a
l
s4
0
1
0
0
1
0
.
[
5
]
S
.
B
e
n
i
c
z
k
y
a
n
d
D
.
L.
S
c
h
o
m
e
r
,
“
E
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
p
h
y
:
b
a
si
c
b
i
o
p
h
y
si
c
a
l
a
n
d
t
e
c
h
n
o
l
o
g
i
c
a
l
a
s
p
e
c
t
s
i
m
p
o
r
t
a
n
t
f
o
r
c
l
i
n
i
c
a
l
a
p
p
l
i
c
a
t
i
o
n
s,
”
E
p
i
l
e
p
t
i
c
D
i
s
o
rd
e
rs
,
v
o
l
.
2
2
,
n
o
.
6
,
p
p
.
6
9
7
–
7
1
5
,
D
e
c
.
2
0
2
0
,
d
o
i
:
1
0
.
1
6
8
4
/
e
p
d
.
2
0
2
0
.
1
2
1
7
.
[
6
]
R
.
G
a
r
g
a
n
d
R
.
V
e
r
m
a
,
“
E
EG
b
a
s
e
d
c
l
a
ssi
f
i
c
a
t
i
o
n
t
e
c
h
n
i
q
u
e
s
f
o
r
A
D
H
D
d
i
a
g
n
o
s
i
s:
a
r
e
v
i
e
w
,
”
J
o
u
rn
a
l
o
f
m
e
d
i
c
a
l
sys
t
e
m
s
,
v
o
l
.
4
0
(
4
)
,
p
p
.
3
0
5
–
3
1
4
,
2
0
1
6
.
[
7
]
M
.
N
u
w
e
r
,
“
A
ssessm
e
n
t
o
f
d
i
g
i
t
a
l
EEG
,
q
u
a
n
t
i
t
a
t
i
v
e
E
EG
,
a
n
d
EEG
b
r
a
i
n
ma
p
p
i
n
g
:
R
e
p
o
r
t
o
f
t
h
e
A
m
e
r
i
c
a
n
A
c
a
d
e
m
y
o
f
N
e
u
r
o
l
o
g
y
a
n
d
t
h
e
A
m
e
r
i
c
a
n
C
l
i
n
i
c
a
l
N
e
u
r
o
p
h
y
s
i
o
l
o
g
y
S
o
c
i
e
t
y
,
”
N
e
u
ro
l
o
g
y
,
v
o
l
.
4
9
,
n
o
.
1
,
p
p
.
2
7
7
–
2
9
2
,
J
u
l
.
1
9
9
7
,
d
o
i
:
1
0
.
1
2
1
2
/
W
N
L.
4
9
.
1
.
2
7
7
.
[
8
]
E.
N
i
e
d
e
r
me
y
e
r
a
n
d
F
.
L
.
d
a
S
i
l
v
a
,
“
El
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
p
h
y
:
b
a
s
i
c
p
r
i
n
c
i
p
l
e
s,
c
l
i
n
i
c
a
l
a
p
p
l
i
c
a
t
i
o
n
s,
a
n
d
r
e
l
a
t
e
d
f
i
e
l
d
s
(
5
t
h
e
d
.
)
,
”
L
i
p
p
i
n
c
o
t
t
Wi
l
l
i
a
m
s
& W
i
l
k
i
n
s
,
2
0
0
5
.
[
9
]
R
.
A
.
B
a
r
k
l
e
y
,
“
A
t
t
e
n
t
i
o
n
-
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
:
a
h
a
n
d
b
o
o
k
f
o
r
d
i
a
g
n
o
s
i
s
a
n
d
t
r
e
a
t
m
e
n
t
(
3
r
d
e
d
.
)
,
”
G
u
i
l
f
o
rd
Pre
s
s
,
2
0
0
6
.
[
1
0
]
C
.
S
.
N
a
y
a
k
a
n
d
A
.
C
.
A
n
i
l
k
u
mar,
“
E
EG
N
o
r
ma
l
W
a
v
e
f
o
r
ms,
”
2
0
2
4
.
[
1
1
]
R
.
J
.
B
a
r
r
y
,
A
.
R
.
C
l
a
r
k
e
,
S
.
J
.
J
o
h
n
st
o
n
e
,
a
n
d
C
.
R
.
B
r
o
w
n
,
“
EEG
d
i
f
f
e
r
e
n
c
e
s
i
n
c
h
i
l
d
r
e
n
b
e
t
w
e
e
n
e
y
e
s
-
c
l
o
s
e
d
a
n
d
e
y
e
s
-
o
p
e
n
r
e
st
i
n
g
c
o
n
d
i
t
i
o
n
s,
”
C
l
i
n
i
c
a
l
N
e
u
r
o
p
h
y
si
o
l
o
g
y
,
v
o
l
.
1
2
0
,
n
o
.
1
0
,
p
p
.
1
8
0
6
–
1
8
1
1
,
2
0
0
9
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
l
i
n
p
h
.
2
0
0
9
.
0
8
.
0
0
6
.
[
1
2
]
S
.
K
.
Lo
o
a
n
d
S
.
M
a
k
e
i
g
,
“
C
l
i
n
i
c
a
l
U
t
i
l
i
t
y
o
f
EEG
i
n
A
t
t
e
n
t
i
o
n
-
D
e
f
i
c
i
t
/
H
y
p
e
r
a
c
t
i
v
i
t
y
D
i
so
r
d
e
r
:
A
R
e
sea
r
c
h
U
p
d
a
t
e
,
”
N
e
u
ro
t
h
e
r
a
p
e
u
t
i
c
s
,
v
o
l
.
9
,
n
o
.
3
,
p
p
.
5
6
9
–
5
8
7
,
Ju
l
.
2
0
1
2
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
3
3
1
1
-
0
1
2
-
0
1
3
1
-
z.
[
1
3
]
S
.
A
l
t
u
n
,
A
.
A
l
k
a
n
,
a
n
d
H
.
A
l
t
u
n
,
“
A
u
t
o
m
a
t
i
c
d
i
a
g
n
o
si
s
o
f
a
t
t
e
n
t
i
o
n
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
so
r
d
e
r
w
i
t
h
c
o
n
t
i
n
u
o
u
s
w
a
v
e
l
e
t
t
r
a
n
sf
o
r
m
a
n
d
c
o
n
v
o
l
u
t
i
o
n
a
l
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
C
l
i
n
i
c
a
l
Ps
y
c
h
o
p
h
a
rm
a
c
o
l
o
g
y
a
n
d
N
e
u
r
o
sc
i
e
n
c
e
,
v
o
l
.
2
0
,
n
o
.
4
,
p
p
.
7
1
5
–
7
2
4
,
N
o
v
.
2
0
2
2
,
d
o
i
:
1
0
.
9
7
5
8
/
C
P
N
.
2
0
2
2
.
2
0
.
4
.
7
1
5
.
[
1
4
]
A
.
K
h
a
l
e
g
h
i
,
P
.
M
.
B
i
r
g
a
n
i
,
M
.
F
.
F
o
o
l
a
d
i
,
a
n
d
M
.
R
.
M
o
h
a
mm
a
d
i
,
“
A
p
p
l
i
c
a
b
l
e
f
e
a
t
u
r
e
s
o
f
e
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
m
f
o
r
A
D
H
D
d
i
a
g
n
o
si
s,
”
R
e
se
a
rc
h
o
n
B
i
o
m
e
d
i
c
a
l
En
g
i
n
e
e
ri
n
g
,
v
o
l
.
3
6
,
n
o
.
1
,
p
p
.
1
–
1
1
,
M
a
r
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
0
7
/
s
4
2
6
0
0
-
0
1
9
-
0
0
0
3
6
-
9.
[
1
5
]
R
.
J.
C
h
a
b
o
t
a
n
d
G
.
S
e
r
f
o
n
t
e
i
n
,
“
Q
u
a
n
t
i
t
a
t
i
v
e
e
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
p
h
i
c
a
n
a
l
y
si
s
o
f
b
o
y
s
w
i
t
h
a
t
t
e
n
t
i
o
n
-
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
:
s
e
l
e
c
t
e
d
f
i
n
d
i
n
g
s
,
”
J
o
u
r
n
a
l
o
f
C
h
i
l
d
N
e
u
r
o
l
o
g
y
,
v
o
l
.
1
1
,
n
o
.
2
,
p
p
.
8
1
–
8
9
,
1
9
9
6
.
[
1
6
]
Y
.
C
.
C
h
a
n
g
,
T
.
Y
.
C
h
e
n
,
C
.
C
.
L
u
,
a
n
d
H
.
C
.
C
h
u
,
“
A
r
e
v
i
e
w
o
f
EE
G
-
b
a
se
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
t
e
c
h
n
i
q
u
e
s
f
o
r
a
t
t
e
n
t
i
o
n
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
so
r
d
e
r
,
”
F
ro
n
t
i
e
rs
i
n
n
e
u
r
o
e
n
g
i
n
e
e
ri
n
g
,
1
1
,
2
2
.
,
2
0
1
8
.
[
1
7
]
R
.
A
.
A
p
s
a
r
i
a
n
d
S
.
K
.
W
i
j
a
y
a
,
“
E
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
p
h
i
c
s
p
e
c
t
r
a
l
a
n
a
l
y
s
i
s
t
o
h
e
l
p
d
e
t
e
c
t
d
e
p
r
e
ss
i
v
e
d
i
s
o
r
d
e
r
,
”
i
n
I
BI
O
ME
D
2
0
2
0
-
Pro
c
e
e
d
i
n
g
s
o
f
t
h
e
3
7
t
h
I
n
t
e
rn
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
B
i
o
m
e
d
i
c
a
l
E
n
g
i
n
e
e
ri
n
g
,
O
c
t
.
2
0
2
0
,
p
p
.
1
3
–
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
I
B
I
O
M
ED
5
0
2
8
5
.
2
0
2
0
.
9
4
8
7
6
1
4
.
[
1
8
]
M
.
R
.
M
o
h
a
mm
a
d
i
,
A
.
K
h
a
l
e
g
h
i
,
A
.
M
.
N
a
sr
a
b
a
d
i
,
S
.
R
a
f
i
e
i
v
a
n
d
,
M
.
B
e
g
o
l
,
a
n
d
H
.
Za
r
a
f
s
h
a
n
,
“
EEG
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
A
D
H
D
a
n
d
n
o
r
m
a
l
c
h
i
l
d
r
e
n
u
s
i
n
g
n
o
n
-
l
i
n
e
a
r
f
e
a
t
u
r
e
s
a
n
d
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
B
i
o
m
e
d
i
c
a
l
E
n
g
i
n
e
e
ri
n
g
L
e
t
t
e
rs
,
v
o
l
.
6
,
n
o
.
2
,
p
p
.
6
6
–
7
3
,
2
0
1
6
,
d
o
i
:
1
0
.
1
0
0
7
/
s
1
3
5
3
4
-
016
-
0
2
1
8
-
2.
[
1
9
]
M
.
J
a
l
i
l
i
,
M
.
A
.
R
.
K
o
r
d
e
st
a
n
i
,
a
n
d
H
.
A
d
e
l
i
,
“
E
EG
-
b
a
s
e
d
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
a
t
t
e
n
t
i
o
n
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
u
s
i
n
g
d
e
e
p
l
e
a
r
n
i
n
g
:
A
r
e
v
i
e
w
,
”
J
o
u
r
n
a
l
o
f
m
e
d
i
c
a
l
syst
e
m
s
,
v
o
l
.
4
4
(
1
1
)
,
p
.
5
4
7
,
2
0
2
0
.
[
2
0
]
P
a
w
a
n
a
n
d
R
.
D
h
i
ma
n
,
“
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
f
o
r
e
l
e
c
t
r
o
e
n
c
e
p
h
a
l
o
g
r
a
m
b
a
se
d
b
r
a
i
n
-
c
o
m
p
u
t
e
r
i
n
t
e
r
f
a
c
e
:
a
s
y
s
t
e
m
a
t
i
c
l
i
t
e
r
a
t
u
r
e
r
e
v
i
e
w
,
”
Me
a
s
u
reme
n
t
:
S
e
n
so
rs
,
v
o
l
.
2
8
,
p
.
1
0
0
8
2
3
,
A
u
g
.
2
0
2
3
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
me
a
se
n
.
2
0
2
3
.
1
0
0
8
2
3
.
[
2
1
]
R
.
K
o
t
t
a
i
m
a
l
a
i
,
M
.
P
.
R
a
j
a
se
k
a
r
a
n
,
V
.
S
e
l
v
a
m,
a
n
d
B
.
K
a
n
n
a
p
i
r
a
n
,
“
EEG
s
i
g
n
a
l
c
l
a
ss
i
f
i
c
a
t
i
o
n
u
si
n
g
p
r
i
n
c
i
p
a
l
c
o
mp
o
n
e
n
t
a
n
a
l
y
s
i
s
w
i
t
h
n
e
u
r
a
l
n
e
t
w
o
r
k
i
n
b
r
a
i
n
c
o
m
p
u
t
e
r
i
n
t
e
r
f
a
c
e
a
p
p
l
i
c
a
t
i
o
n
s,
”
i
n
2
0
1
3
I
E
EE
I
n
t
e
r
n
a
t
i
o
n
a
l
C
o
n
f
e
re
n
c
e
o
n
Em
e
rg
i
n
g
T
re
n
d
s
i
n
C
o
m
p
u
t
i
n
g
,
C
o
m
m
u
n
i
c
a
t
i
o
n
a
n
d
N
a
n
o
t
e
c
h
n
o
l
o
g
y
,
I
C
E
-
C
C
N
2
0
1
3
,
M
a
r
.
2
0
1
3
,
p
p
.
2
2
7
–
2
3
1
,
d
o
i
:
1
0
.
1
1
0
9
/
I
C
E
-
C
C
N
.
2
0
1
3
.
6
5
2
8
4
9
8
.
[
2
2
]
T.
C
h
e
n
,
I
.
T
a
c
h
ma
z
i
d
i
s
,
S
.
B
a
t
sa
k
i
s
,
M
.
A
d
a
mo
u
,
E.
P
a
p
a
d
a
k
i
s,
a
n
d
G
.
A
n
t
o
n
i
o
u
,
“
D
i
a
g
n
o
s
i
n
g
a
t
t
e
n
t
i
o
n
-
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
(
A
D
H
D
)
u
s
i
n
g
a
r
t
i
f
i
c
i
a
l
i
n
t
e
l
l
i
g
e
n
c
e
:
a
c
l
i
n
i
c
a
l
st
u
d
y
i
n
t
h
e
U
K
,
”
F
ro
n
t
i
e
rs
i
n
Psy
c
h
i
a
t
r
y
,
v
o
l
.
1
4
,
J
u
n
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
s
y
t
.
2
0
2
3
.
1
1
6
4
4
3
3
.
[
2
3
]
M
.
R
.
M
o
h
a
mm
a
d
i
,
A
.
K
h
a
l
e
g
h
i
,
A
.
M
.
N
a
sr
a
b
a
d
i
,
S
.
R
a
f
i
e
i
v
a
n
d
,
M
.
B
e
g
o
l
,
a
n
d
H
.
Za
r
a
f
s
h
a
n
,
“
EEG
c
l
a
ssi
f
i
c
a
t
i
o
n
o
f
A
D
H
D
a
n
d
n
o
r
m
a
l
c
h
i
l
d
r
e
n
u
s
i
n
g
n
o
n
-
l
i
n
e
a
r
f
e
a
t
u
r
e
s
a
n
d
n
e
u
r
a
l
n
e
t
w
o
r
k
,
”
Bi
o
m
e
d
i
c
a
l
En
g
i
n
e
e
ri
n
g
L
e
t
t
e
rs
,
v
o
l
.
6
,
n
o
.
2
,
p
p
.
6
6
–
7
3
,
M
a
y
2
0
1
6
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
3
5
3
4
-
0
1
6
-
0
2
1
8
-
2.
[
2
4
]
E.
P
e
r
e
d
a
,
M
.
G
a
r
c
í
a
-
T
o
r
r
e
s,
B
.
M
e
l
i
á
n
-
B
a
t
i
st
a
,
S
.
M
a
ñ
a
s,
L
.
M
é
n
d
e
z
,
a
n
d
J.
J.
G
o
n
z
á
l
e
z
,
“
T
h
e
b
l
e
s
si
n
g
o
f
d
i
me
n
si
o
n
a
l
i
t
y
:
f
e
a
t
u
r
e
se
l
e
c
t
i
o
n
o
u
t
p
e
r
f
o
r
ms
f
u
n
c
t
i
o
n
a
l
c
o
n
n
e
c
t
i
v
i
t
y
-
b
a
se
d
f
e
a
t
u
r
e
t
r
a
n
sf
o
r
mat
i
o
n
t
o
c
l
a
ss
i
f
y
A
D
H
D
s
u
b
j
e
c
t
s
f
r
o
m
EEG
p
a
t
t
e
r
n
s
o
f
p
h
a
se
s
y
n
c
h
r
o
n
i
s
a
t
i
o
n
,
”
PL
o
S
O
N
E
,
v
o
l
.
1
3
,
n
o
.
8
,
p
.
e
0
2
0
1
6
6
0
,
A
u
g
.
2
0
1
8
,
d
o
i
:
1
0
.
1
3
7
1
/
j
o
u
r
n
a
l
.
p
o
n
e
.
0
2
0
1
6
6
0
.
[
2
5
]
I
.
D
a
u
b
e
c
h
i
e
s
,
T
e
n
L
e
c
t
u
r
e
s
o
n
W
a
v
e
l
e
t
s
.
S
o
c
i
e
t
y
f
o
r
I
n
d
u
s
t
r
i
a
l
a
n
d
A
p
p
l
i
e
d
M
a
t
h
e
m
a
t
i
c
s,
1
9
9
2
.
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:
2
5
0
2
-
4
7
52
A
tten
tio
n
d
eficit
a
n
d
h
yp
era
cti
vity
d
is
o
r
d
er c
la
s
s
if
ica
tio
n
…
(
S
yifa
n
i I
h
fa
d
z
a
A
liya
h
)
1587
[
2
6
]
S
.
M
a
l
l
a
t
,
A
W
a
v
e
l
e
t
T
o
u
r
o
f
S
i
g
n
a
l
P
ro
c
e
ssi
n
g
,
2
n
d
.
1
9
9
9
.
[
2
7
]
F
.
P
e
d
r
e
g
o
s
a
e
t
a
l
.
,
“
S
c
i
k
i
t
-
l
e
a
r
n
:
ma
c
h
i
n
e
l
e
a
r
n
i
n
g
i
n
p
y
t
h
o
n
,
”
J
o
u
r
n
a
l
o
f
M
a
c
h
i
n
e
L
e
a
rn
i
n
g
Re
s
e
a
r
c
h
,
v
o
l
.
1
2
,
p
p
.
2
8
2
5
–
2
8
3
0
,
2
0
1
1
,
[
O
n
l
i
n
e
]
.
A
v
a
i
l
a
b
l
e
:
h
t
t
p
:
/
/
j
m
l
r
.
c
sai
l
.
m
i
t
.
e
d
u
/
p
a
p
e
r
s/
v
1
2
/
p
e
d
r
e
g
o
sa
1
1
a
.
h
t
ml
%
5
C
n
h
t
t
p
:
/
/
a
r
x
i
v
.
o
r
g
/
a
b
s/
1
2
0
1
.
0
4
9
0
.
[
2
8
]
H
.
F
.
K
a
i
ser,
“
O
n
c
l
i
f
f
’
s
f
o
r
m
u
l
a
,
t
h
e
k
a
i
ser
-
g
u
t
t
ma
n
r
u
l
e
,
a
n
d
t
h
e
n
u
mb
e
r
o
f
f
a
c
t
o
r
s
,
”
Pe
r
c
e
p
t
u
a
l
a
n
d
M
o
t
o
r S
k
i
l
l
s
,
v
o
l
.
7
4
,
n
o
.
2
,
p
p
.
5
9
5
–
5
9
8
,
A
p
r
.
1
9
9
2
,
d
o
i
:
1
0
.
2
4
6
6
/
p
ms
.
1
9
9
2
.
7
4
.
2
.
5
9
5
.
[
2
9
]
M
.
M
o
h
r
i
,
A
.
R
o
s
t
a
m
i
z
a
d
e
h
,
a
n
d
A
.
T
a
l
w
a
l
k
a
r
,
F
o
u
n
d
a
t
i
o
n
s
o
f
M
a
c
h
i
n
e
L
e
a
rn
i
n
g
,
2
n
d
.
2
0
1
8
.
[
3
0
]
H
.
A
b
b
a
s,
M
.
H
u
ssa
i
n
,
N
.
S
h
a
h
i
d
,
a
n
d
S
.
R
a
z
a
,
“
A
D
H
D
d
e
t
e
c
t
i
o
n
a
n
d
c
l
a
s
si
f
i
c
a
t
i
o
n
u
s
i
n
g
K
N
N
a
n
d
S
V
M
b
a
s
e
d
o
n
EEG
si
g
n
a
l
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
Ap
p
l
i
c
a
t
i
o
n
s,
9
(
1
0
)
,
v
o
l
.
9
,
n
o
.
1
0
,
p
p
.
4
9
1
–
4
9
7
,
2
0
1
8
.
[
3
1
]
T.
H
a
s
t
i
e
,
R
.
Ti
b
s
h
i
r
a
n
i
,
a
n
d
J
.
F
r
i
e
d
man
,
T
h
e
E
l
e
m
e
n
t
s
o
f
S
t
a
t
i
s
t
i
c
a
l
L
e
a
r
n
i
n
g
:
D
a
t
a
M
i
n
i
n
g
,
I
n
f
e
re
n
c
e
,
a
n
d
Pr
e
d
i
c
t
i
o
n
.
2
0
0
9
.
[
3
2
]
C
.
C
o
r
t
e
s
a
n
d
V
.
V
a
p
n
i
k
,
“
S
u
p
p
o
r
t
-
v
e
c
t
o
r
n
e
t
w
o
r
k
s,”
M
a
c
h
i
n
e
L
e
a
r
n
i
n
g
,
v
o
l
.
2
0
,
n
o
.
3
,
p
p
.
2
7
3
–
2
9
7
,
S
e
p
.
1
9
9
5
,
d
o
i
:
1
0
.
1
0
0
7
/
b
f
0
0
9
9
4
0
1
8
.
[
3
3
]
N
.
A
r
o
r
a
,
S
.
S
r
i
v
a
s
t
a
v
a
,
R
.
A
g
a
r
w
a
l
,
V
.
M
e
h
n
d
i
r
a
t
t
a
,
a
n
d
A
.
Tr
i
p
a
t
h
i
,
“
D
i
a
b
e
t
e
s m
e
l
l
i
t
u
s
p
r
e
d
i
c
t
i
o
n
u
s
i
n
g
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
w
i
t
h
i
n
t
h
e
s
c
o
p
e
o
f
a
g
e
n
e
r
i
c
f
r
a
m
e
w
o
r
k
,
”
I
n
d
o
n
e
s
i
a
n
J
o
u
rn
a
l
o
f
El
e
c
t
r
i
c
a
l
E
n
g
i
n
e
e
ri
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
3
2
,
n
o
.
3
,
p
p
.
1
7
2
4
–
1
7
3
5
,
D
e
c
.
2
0
2
3
,
d
o
i
:
1
0
.
1
1
5
9
1
/
I
JEEC
S
.
V
3
2
.
I
3
.
P
P
1
7
2
4
-
1
7
3
5
.
[
3
4
]
A
.
E.
A
l
c
h
a
l
a
b
i
,
S
.
S
h
i
r
m
o
h
a
mm
a
d
i
,
A
.
N
.
E
d
d
i
n
,
a
n
d
M
.
E
l
s
h
a
r
n
o
u
b
y
,
“
F
O
C
U
S
:
d
e
t
e
c
t
i
n
g
A
D
H
D
p
a
t
i
e
n
t
s
b
y
a
n
EEG
-
b
a
se
d
seri
o
u
s
g
a
m
e
,
”
I
EEE
T
ra
n
s
a
c
t
i
o
n
s
o
n
I
n
st
ru
m
e
n
t
a
t
i
o
n
a
n
d
M
e
a
s
u
r
e
m
e
n
t
,
v
o
l
.
6
7
,
n
o
.
7
,
p
p
.
1
5
1
2
–
1
5
2
0
,
Ju
l
.
2
0
1
8
,
d
o
i
:
1
0
.
1
1
0
9
/
TI
M
.
2
0
1
8
.
2
8
3
8
1
5
8
.
[
3
5
]
J.
Y
a
n
g
,
W
.
Li
,
S
.
W
a
n
g
,
J.
Lu
,
a
n
d
L
.
Z
o
u
,
“
C
l
a
ssi
f
i
c
a
t
i
o
n
o
f
c
h
i
l
d
r
e
n
w
i
t
h
a
t
t
e
n
t
i
o
n
d
e
f
i
c
i
t
h
y
p
e
r
a
c
t
i
v
i
t
y
d
i
s
o
r
d
e
r
u
si
n
g
P
C
A
a
n
d
k
-
n
e
a
r
e
s
t
n
e
i
g
h
b
o
r
s
d
u
r
i
n
g
i
n
t
e
r
f
e
r
e
n
c
e
c
o
n
t
r
o
l
t
a
s
k
,
”
2
0
1
6
,
p
p
.
4
4
7
–
4
5
3
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
S
y
ifa
n
i
Ihf
a
d
z
a
Aliy
a
h
i
s
a
re
se
a
rc
h
e
r
a
t
Bio
m
e
d
ica
l
In
stru
m
e
n
tatio
n
Re
se
a
rc
h
G
ro
u
p
o
f
Un
i
v
e
rsitas
In
d
o
n
e
sia
.
S
h
e
re
c
e
iv
e
d
h
e
r
b
a
c
h
e
lo
r’s
d
e
g
re
e
in
p
h
y
sic
s
with
th
e
sp
e
c
ializa
ti
o
n
i
n
s
y
ste
m
s
a
n
d
in
str
u
m
e
n
tatio
n
p
h
y
sic
s.
He
r
re
se
a
rc
h
a
re
a
s
a
re
sig
n
a
l
p
ro
c
e
ss
in
g
,
e
lec
tro
e
n
c
e
p
h
a
l
o
g
ra
p
h
y
,
a
n
d
c
las
sifica
ti
o
n
p
r
o
g
ra
m
wi
th
su
p
e
rv
ise
d
lea
rn
i
n
g
.
S
h
e
is
a
re
c
ip
ien
t
o
f
se
v
e
ra
l
a
c
a
d
e
m
ic
s’
a
wa
rd
s
su
c
h
a
s
IIS
M
A
S
c
h
o
lar
sh
ip
2
0
2
2
sp
o
n
so
re
d
b
y
th
e
M
in
istr
y
o
f
E
d
u
c
a
ti
o
n
,
C
u
lt
u
re
,
Re
se
a
rc
h
,
a
n
d
Tec
h
n
o
l
o
g
y
o
f
t
h
e
Re
p
u
b
l
ic
o
f
In
d
o
n
e
sia
to
Wes
tern
Un
iv
e
rsity
,
Ca
n
a
d
a
.
Also
,
sh
e
re
c
e
iv
e
d
a
P
UTI
Q3
2
0
2
3
re
se
a
rc
h
g
ra
n
t
fro
m
Un
iv
e
rsitas
In
d
o
n
e
sia
to
re
se
a
rc
h
o
n
AD
HD
c
las
sifica
ti
o
n
fro
m
q
EE
G
sig
n
a
l
u
sin
g
m
a
c
h
in
e
lea
rn
in
g
.
He
r
re
se
a
rc
h
in
tere
st
i
n
c
lu
d
e
s
si
g
n
a
l
p
ro
c
e
ss
in
g
,
a
n
a
ly
sis,
a
n
d
c
las
sifica
ti
o
n
o
f
q
EE
G
da
ta.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
th
ro
u
g
h
h
e
r
e
m
a
il
:
sy
i
fa
n
i.
i
h
fa
d
z
a
@u
i.
a
c
.
id
.
Dr
.
S
a
str
a
K
u
sum
a
Wij
a
y
a
e
a
rn
e
d
h
is
Do
c
to
ra
te
fro
m
Ok
a
y
a
m
a
Un
iv
e
rsity
,
Ja
p
a
n
,
sp
e
c
ializin
g
in
Na
tu
ra
l
S
c
ien
c
e
s
a
n
d
En
g
in
e
e
rin
g
.
His
a
c
a
d
e
m
ic
d
e
d
ica
ti
o
n
sp
a
n
s
d
iv
e
rse
d
o
m
a
in
s,
with
c
u
rre
n
t
i
n
tere
sts
in
P
h
y
sic
s
In
str
u
m
e
n
tat
io
n
,
d
a
ta
a
c
q
u
isit
i
o
n
,
a
n
d
b
io
si
g
n
a
l
p
r
o
c
e
ss
in
g
.
Dr.
Wi
jay
a
is
a
p
io
n
e
e
r
in
d
e
v
e
lo
p
in
g
m
e
a
su
re
m
e
n
t
to
o
ls,
u
ti
l
izin
g
c
u
tt
in
g
-
e
d
g
e
tec
h
n
o
lo
g
ies
f
o
r
p
r
e
c
isio
n
,
a
n
d
b
r
o
a
d
e
n
i
n
g
e
x
p
e
rime
n
tal
m
e
th
o
d
o
l
o
g
ies
.
His
p
iv
o
tal
c
o
n
tr
ib
u
ti
o
n
s
to
d
a
ta
a
c
q
u
isit
io
n
in
v
o
lv
e
in
n
o
v
a
ti
v
e
a
p
p
ro
a
c
h
e
s
in
c
a
p
tu
r
in
g
,
p
ro
c
e
ss
in
g
,
a
n
d
i
n
terp
re
ti
n
g
d
a
ta
a
c
ro
ss
sc
ien
ti
fic
d
o
m
a
in
s.
P
a
rti
c
u
larly
fo
c
u
se
d
o
n
b
i
o
sig
n
a
l
p
ro
c
e
ss
in
g
,
e
sp
e
c
ially
i
n
b
i
o
m
e
d
ica
l
sig
n
a
ls,
h
e
e
m
p
lo
y
s
a
d
v
a
n
c
e
d
tec
h
n
iq
u
e
s
to
u
n
ra
v
e
l
c
o
m
p
lex
it
ies
in
b
io
l
o
g
ica
l
sy
ste
m
s.
He
lea
d
s
r
e
se
a
r
c
h
in
teg
ra
ti
n
g
m
a
c
h
in
e
lea
rn
in
g
in
to
b
io
si
g
n
a
l
p
r
o
c
e
ss
in
g
,
e
n
h
a
n
c
in
g
a
c
c
u
ra
c
y
,
a
n
d
e
fficie
n
c
y
in
b
io
m
e
d
ica
l
d
a
ta
a
n
a
ly
sis.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
s
k
wijay
a
@
sc
i.
u
i.
a
c
.
id
.
Dr
.
Ye
tty
R
a
m
l
i
re
c
e
iv
e
d
h
e
r
Do
c
to
ra
te
in
Bi
o
m
e
d
icin
e
fro
m
In
sti
tu
t
P
e
rtan
ia
n
Bo
g
o
r
,
In
d
o
n
e
sia
.
S
h
e
h
a
s b
e
e
n
s
e
rv
in
g
a
t
th
e
Un
i
v
e
rsity
o
f
I
n
d
o
n
e
sia
'
s Ne
u
ro
lo
g
y
De
p
a
rtme
n
t
sin
c
e
2
0
0
3
.
Dr.
Ra
m
li
h
o
ld
s
th
e
e
ste
e
m
e
d
p
o
siti
o
n
o
f
C
o
n
su
l
t
a
n
t
in
P
e
d
iatr
ic
Ne
u
ro
lo
g
y
,
m
a
k
in
g
sig
n
if
ica
n
t
c
o
n
tri
b
u
ti
o
n
s
to
p
a
ti
e
n
t
c
a
re
a
t
Ci
p
to
M
a
n
g
u
n
k
u
su
m
o
Ho
sp
it
a
l,
a
p
ro
m
in
e
n
t
g
o
v
e
rn
m
e
n
t
i
n
stit
u
ti
o
n
.
He
r
a
c
ti
v
e
p
a
rti
c
i
p
a
ti
o
n
i
n
v
a
rio
u
s
c
o
n
fe
re
n
c
e
s
a
n
d
e
v
e
n
ts,
in
c
lu
d
in
g
p
re
se
n
tatio
n
s
a
t
p
re
sti
g
io
u
s
p
latf
o
rm
s
li
k
e
Ne
u
r
o
ra
d
io
lo
g
y
Au
stra
li
a
a
n
d
th
e
Bra
in
In
ju
r
y
T
h
ir
d
T
h
re
e
Co
n
ti
n
e
n
ts
Co
n
fe
re
n
c
e
i
n
S
in
g
a
p
o
re
,
u
n
d
e
r
sc
o
re
s
h
e
r
c
o
m
m
it
m
e
n
t
t
o
a
d
d
re
ss
in
g
c
h
a
ll
e
n
g
e
s
in
p
e
d
iat
ric
n
e
u
ro
l
o
g
y
.
He
r
re
se
a
rc
h
in
tere
st
in
c
lu
d
e
s
n
e
u
ro
lo
g
y
,
n
e
u
ro
l
o
g
ica
l
th
e
ra
p
h
y
,
b
ra
in
i
n
ju
r
y
,
a
n
d
ste
m
c
e
ll
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
e
tt
y
ra
m
li
@y
a
h
o
o
.
c
o
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.