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d
p
r
eo
p
er
ativ
e
p
r
o
g
n
o
s
is
[
1
8
]
.
T
h
is
b
ein
g
th
e
co
m
m
o
n
l
y
f
o
ll
o
w
ed
p
r
o
ce
d
u
r
e,
f
u
s
io
n
o
f
f
e
atu
r
e
ex
tr
ac
t
io
n
a
n
d
class
if
ica
tio
n
u
s
i
n
g
o
n
e
d
i
m
e
n
s
io
n
al
(
1
D
)
C
o
n
v
o
lu
tio
n
a
l
Neu
r
al
Net
w
o
r
k
(
C
N
N)
h
ad
b
ee
n
atte
m
p
ted
[
1
9
]
.
Usi
n
g
a
d
ed
icate
d
C
NN,
lo
n
g
E
C
G
d
ata
co
u
ld
b
e
class
i
f
ied
f
o
r
a
p
ar
ticu
lar
p
atien
t.
T
h
o
u
g
h
t
h
is
w
o
r
k
d
en
i
es
th
e
n
ec
es
s
it
y
o
f
h
an
d
-
cr
af
ted
m
an
u
al
f
ea
tu
r
e
s
,
it
is
a
p
atien
t
s
p
ec
i
f
ic
s
y
s
te
m
w
h
ic
h
w
o
u
ld
g
e
n
er
ate
an
ea
r
l
y
a
ler
t
u
s
i
n
g
a
lig
h
t
-
w
ei
g
h
t
w
ea
r
ab
le
d
ev
ice.
Als
o
,
class
i
f
ica
tio
n
p
er
f
o
r
m
a
n
ce
w
a
s
s
u
p
er
io
r
o
n
l
y
f
o
r
Ve
n
tr
i
cu
lar
E
cto
p
ic
B
ea
ts
(
VE
B
)
an
d
Su
p
r
av
en
tr
ic
u
lar
E
cto
p
ic
B
ea
ts
(
SVEB
)
w
h
en
test
ed
o
n
MI
T
-
B
I
H
ar
r
h
y
th
m
ia
b
e
n
ch
m
ar
k
d
atab
ase.
I
t
is
s
ee
n
th
at,
E
C
G
s
i
g
n
al
an
a
l
y
s
i
s
n
o
t
o
n
l
y
id
en
ti
f
ies
th
e
h
e
ar
t
d
is
o
r
d
e
r
s
b
u
t
also
b
r
ea
th
in
g
d
is
o
r
d
er
n
a
m
e
l
y
,
s
leep
ap
n
ea
[
2
0
]
.
T
h
is
w
o
r
k
u
tili
ze
d
v
ar
iatio
n
a
l
m
o
d
e
d
ec
o
m
p
o
s
itio
n
in
s
tead
o
f
u
s
in
g
ex
p
en
s
i
v
e
an
d
ti
m
e
co
n
s
u
m
in
g
g
o
ld
s
ta
n
d
ar
d
p
o
ly
s
o
m
n
o
g
r
a
m
.
T
h
e
ac
c
u
r
ac
y
o
b
tain
ed
f
o
r
b
o
th
o
n
li
n
e
a
n
d
o
f
f
li
n
e
p
r
o
ce
s
s
es
w
er
e
9
7
.
5
%
a
n
d
9
5
%
r
esp
ec
t
iv
el
y
b
y
u
tili
zi
n
g
e
n
er
g
y
a
n
d
R
-
R
i
n
ter
v
al
f
ea
tu
r
e
f
r
o
m
t
h
e
v
ar
iatio
n
al
m
o
d
e
f
u
n
ctio
n
s
.
Hea
l
th
y
a
n
d
ap
n
ea
s
u
b
j
ec
ts
w
er
e
clas
s
i
f
ied
u
s
i
n
g
SVM.
R
e
s
ea
r
ch
w
o
r
k
atte
m
p
t
u
s
ed
R
-
R
in
te
r
v
al
an
d
f
r
eq
u
en
c
y
d
o
m
ain
f
ea
tu
r
e
s
a
n
d
clas
s
i
f
ied
u
s
i
n
g
R
ad
ial
b
asis
f
u
n
ct
io
n
(
R
B
F)
a
n
d
Sp
li
n
e
A
ct
iv
ated
Feed
Fo
r
w
ar
d
Ne
u
r
al
Net
w
o
r
k
(
S
AFFNN)
.
Ho
w
e
v
er
,
t
h
ese
f
ea
tu
r
es
w
er
e
n
o
t
s
u
f
f
icien
t
a
n
d
it
co
u
ld
y
ield
o
n
l
y
9
0
.
8
5
% o
f
ac
cu
r
ac
y
i
n
class
if
y
in
g
t
h
e
ar
r
h
y
t
h
m
ia
i
n
MI
T
-
B
I
H
d
atab
ase
[
2
1
]
.
A
s
i
m
ilar
w
o
r
k
s
ee
n
to
b
e
d
o
n
e
b
ased
o
n
f
r
eq
u
e
n
c
y
d
o
m
ai
n
f
ea
tu
r
e.
I
n
th
e
ir
p
r
o
p
o
s
ed
w
o
r
k
,
A
r
ti
f
icial
B
ee
C
o
lo
n
y
(
A
B
C
)
o
p
tim
ized
L
ea
s
t
Sq
u
ar
e
s
S
u
p
p
o
r
t
Vec
to
r
Ma
ch
in
es
(
L
SS
V
M)
class
i
f
ier
u
s
i
n
g
R
B
F
w
as
p
r
o
p
o
s
ed
[
2
2
]
.
I
n
tr
in
s
ic
M
o
d
e
F
u
n
c
tio
n
s
(
I
MF)
w
er
e
u
s
ed
as
b
an
d
w
id
t
h
f
ea
tu
r
e.
Ho
w
e
v
er
t
h
e
ac
cu
r
ac
y
o
b
tain
ed
i
n
class
if
ic
atio
n
co
u
ld
n
o
t
e
x
ce
ed
9
4
.
6
1
%.
T
h
e
class
if
icatio
n
ac
c
u
r
ac
y
is
m
an
d
ato
r
y
n
o
t
o
n
l
y
i
n
ar
r
h
y
t
h
m
ia
d
etec
tio
n
b
u
t
ev
e
n
in
b
io
m
etr
ic
r
ec
o
g
n
iti
o
n
b
ased
o
n
E
C
G.
T
h
e
w
o
r
k
s
d
o
n
e
also
s
u
f
f
er
s
i
n
ac
cu
r
ac
y
,
t
h
o
u
g
h
t
h
e
y
u
s
ed
a
m
u
ltit
a
s
k
lear
n
i
n
g
ap
p
r
o
ac
h
f
o
r
f
ea
t
u
r
e
ex
tr
ac
t
io
n
a
n
d
class
i
f
ier
[
2
3
]
.
I
t
is
o
b
v
io
u
s
t
h
at
t
h
e
en
d
r
es
u
lt
i
s
d
ep
en
d
en
t
o
n
i
n
telli
g
ib
le
f
e
atu
r
e
an
d
s
u
f
f
icien
t
d
is
ta
n
ce
m
etr
ics
a
m
o
n
g
t
h
e
f
ea
t
u
r
e
m
a
g
n
it
u
d
es
.
Mo
s
t
o
f
t
h
e
w
o
r
k
s
co
u
ld
g
et
cla
s
s
i
f
icati
o
n
ac
cu
r
ac
y
ab
o
v
e
9
0
%,
b
u
t
t
h
e
to
tal
n
u
m
b
er
s
o
f
class
es
i
n
s
u
c
h
clai
m
s
ar
e
le
s
s
t
h
an
1
0
.
T
h
e
co
m
p
le
x
it
y
i
n
th
e
cla
s
s
i
f
icat
io
n
i
n
cr
ea
s
es
p
r
o
p
o
r
tio
n
al
to
th
e
n
u
m
b
er
o
f
clas
s
es.
T
h
is
s
ce
n
ar
io
is
w
i
tn
e
s
s
ed
in
[
2
4
]
.
My
o
ca
r
d
ial
i
n
f
ar
ctio
n
(
MI
)
,
Hea
r
t
Mu
s
cle
Di
s
ea
s
e
(
HM
D)
an
d
B
u
n
d
le
B
r
an
c
h
B
l
o
ck
(
B
B
B
)
w
er
e
t
h
e
t
h
r
ee
ca
s
es
clas
s
i
f
ied
u
s
i
n
g
C
o
m
p
le
x
W
av
elet
S
u
b
B
an
d
bi
-
s
p
ec
tr
u
m
(
C
W
SB
)
f
ea
t
u
r
es
f
r
o
m
1
2
-
lead
E
C
G.
E
x
p
er
i
m
en
tal
r
esu
lts
s
h
o
w
th
at
t
h
e
C
W
SB
f
ea
tu
r
es
o
f
1
2
-
lead
E
C
G
an
d
th
e
S
VM
clas
s
if
ier
y
ield
ed
th
e
i
n
d
iv
id
u
al
ac
cu
r
ac
y
v
alu
e
s
f
o
r
MI
,
HM
D
an
d
B
B
B
class
es
9
8
.
3
7
,
9
7
.
3
9
an
d
9
6
.
4
0
%,
r
esp
ec
tiv
el
y
,
u
s
in
g
SV
M
class
if
ier
an
d
R
B
F k
er
n
e
l f
u
n
ctio
n
.
Yet
an
o
th
er
r
esear
ch
[
2
5
]
h
ad
o
b
tain
ed
9
5
%
ac
cu
r
ac
y
in
class
i
f
y
in
g
ca
r
d
iac
ar
r
h
y
t
h
m
i
a
s
u
c
h
as
m
y
o
ca
r
d
ial
i
n
f
ar
ct
io
n
,
ca
r
d
io
m
y
o
p
at
h
y
,
a
n
d
m
y
o
ca
r
d
itis
.
I
t
is
s
ee
n
t
h
at
o
n
l
y
3
clas
s
es
ca
s
e
w
as
i
m
p
le
m
en
ted
u
s
i
n
g
Gen
er
al
R
e
g
r
ess
io
n
Neu
r
al
Net
w
o
r
k
(
GR
N
N)
.
L
o
n
g
ter
m
ac
cu
m
u
la
ted
p
atien
t
E
C
G
d
ata
co
u
ld
p
r
o
d
u
ce
8
8
%
ac
cu
r
ac
y
w
i
th
e
f
f
icien
c
y
i
m
p
r
o
v
e
m
e
n
t
in
t
h
e
o
r
d
er
o
f
4
5
0
tim
es.
Ho
w
ev
er
,
th
is
w
o
r
k
s
u
f
f
er
s
f
r
o
m
v
er
y
less
n
u
m
b
er
o
f
cla
s
s
es.
C
las
s
if
y
in
g
n
o
r
m
al
b
ea
t,
s
u
p
r
av
e
n
t
r
icu
lar
ec
to
p
ic
b
ea
t,
b
u
n
d
le
b
r
an
ch
ec
to
p
ic
b
ea
t,
v
en
tr
ic
u
lar
ec
to
p
ic
b
ea
t,
f
u
s
io
n
b
ea
t
an
d
u
n
k
n
o
w
n
b
ea
t
h
ad
b
ee
n
atte
m
p
ted
i
n
[
2
6
]
.
QR
S
co
m
p
le
x
es
o
f
t
h
e
E
C
G
w
a
v
e
f
o
r
m
h
ad
b
ee
n
co
n
v
er
ted
in
to
Fo
u
r
ier
s
p
ec
tr
u
m
an
d
p
o
w
er
v
ar
iatio
n
s
w
er
e
o
b
s
er
v
ed
w
it
h
i
n
0
-
2
0
Hz
s
p
ec
tr
u
m
.
Gr
e
y
R
elatio
n
a
l
An
al
y
s
is
(
G
R
A
)
w
a
s
p
er
f
o
r
m
ed
to
class
i
f
y
t
h
e
af
o
r
e
m
e
n
t
io
n
ed
ab
n
o
r
m
a
liti
es
b
ased
o
n
MI
T
-
B
I
H
ar
r
h
y
t
h
m
i
a
b
en
ch
m
ar
k
d
atab
ase.
Ho
w
e
v
er
,
th
i
s
n
o
n
i
n
v
a
s
iv
e
m
et
h
o
d
is
li
m
ited
o
n
l
y
to
6
class
es
i
n
clu
d
i
n
g
t
h
e
n
o
r
m
al
b
ea
t.
T
h
is
m
aj
o
r
d
r
aw
b
ac
k
is
d
u
e
t
h
e
f
ac
t
t
h
at
f
ea
t
u
r
e
u
s
ed
i
s
b
ased
o
n
l
y
o
n
th
e
p
o
w
er
s
p
ec
tr
u
m
o
f
t
h
e
f
r
eq
u
e
n
c
y
d
o
m
ain
s
i
g
n
al.
T
h
e
w
o
r
k
s
clai
m
ed
in
[
2
7
]
co
u
ld
class
i
f
y
7
ar
r
h
y
t
h
m
ia
(
P
VC
,
A
tr
ial
Fib
r
illatio
n
(
A
F),
C
o
m
p
lete
Hea
r
t
B
lo
ck
(
C
HB
)
,
L
e
f
t
B
u
n
d
le
B
r
an
c
h
B
lo
ck
(
L
B
B
B
)
,
No
r
m
al
Si
n
u
s
R
h
y
t
h
m
(
NSR
)
,
Ve
n
tr
icu
lar
Fib
r
illatio
n
(
VF)
an
d
Ven
tr
ic
u
lar
T
ac
h
y
ca
r
d
ia
(
VT
)
)
u
s
in
g
1
4
f
ea
tu
r
es
f
r
o
m
ti
m
e
d
o
m
ai
n
,
f
r
eq
u
en
c
y
d
o
m
ai
n
,
n
o
n
li
n
ea
r
an
d
c
h
ao
tic
f
ea
t
u
r
e
s
w
er
e
ex
tr
ac
ted
to
tr
ain
M
u
l
ti
-
L
a
y
er
P
er
ce
p
tr
o
n
(
ML
P
)
n
eu
r
al
n
et
w
o
r
k
s
a
f
ter
co
m
p
u
ti
n
g
Hea
r
t
R
ate
Var
ia
b
ilit
y
(
HR
V)
.
Gen
er
alize
d
Di
s
cr
i
m
i
n
ate
An
al
y
s
i
s
(
GD
A
)
h
as
b
ee
n
u
s
ed
as
a
d
i
m
en
s
io
n
r
ed
u
ct
io
n
m
et
h
o
d
p
r
io
r
to
tr
ain
th
e
n
e
u
r
al
n
e
t
w
o
r
k
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ased
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s
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t V
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to
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Ma
ch
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SV
M)
.
2.
RE
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E
ARCH
M
E
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H
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D
2
.
1
.
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it
-
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rr
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4
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if
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b
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g
4
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e
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3
].
T
ab
le
1
.
Data
s
ets Su
m
m
ar
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G
si
g
n
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n
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T
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mal
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t
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t
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6
A
t
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mat
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2
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9
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1
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5
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t
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b
e
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r
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t
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l
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mat
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1
5
0
75
75
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e
n
t
r
i
c
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r
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t
t
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r
(
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)
!
4
7
2
2
3
6
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3
6
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si
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t
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t
(
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1
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l
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t
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(UN)
Q
33
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m
m
ar
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ata
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n
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th
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etails
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E
C
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ai
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t
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th
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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l.
12
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1
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et,
i.e
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2
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et
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2
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2
.
L
y
a
pu
no
v
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po
nents
L
y
ap
u
n
o
v
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x
p
o
n
en
t
s
(
L
E
)
is
v
er
y
u
s
e
f
u
l
i
n
a
n
al
y
z
in
g
t
h
e
d
y
n
a
m
ical
s
y
s
te
m
s
.
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h
e
s
e
n
s
iti
v
it
y
o
f
d
iv
er
g
e
n
ce
o
r
co
n
v
er
g
en
ce
o
f
tr
aj
ec
t
o
r
ies
in
p
h
ase
s
p
ac
e
w
ith
r
esp
ec
t
to
th
e
in
itia
l
co
n
d
itio
n
s
is
m
ea
s
u
r
ed
th
r
o
u
g
h
L
y
ap
u
n
o
v
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x
p
o
n
e
n
t
s
.
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s
y
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te
m
w
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th
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lea
s
t
o
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e
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itiv
e
e
x
p
o
n
en
t
is
co
n
s
id
er
ed
to
b
e
in
c
h
ao
tic
r
eg
io
n
.
L
E
i
s
a
m
ea
s
u
r
e
o
f
h
o
w
d
i
v
er
s
e
th
e
lat
tices d
u
r
i
n
g
ea
ch
t
i
m
e
iter
atio
n
a
n
d
it is
g
iv
en
b
y
E
q
u
atio
n
(
1
)
.
(
)
|
(
)
|
(
1
)
W
h
er
e
r
ef
er
s
iter
atio
n
s
a
n
d
λ
(
i)
is
L
E
.
λ
(
i)
ar
e
ca
lc
u
late
d
f
r
o
m
t
h
e
E
i
g
e
n
v
al
u
es
(
)
o
f
as
g
iv
e
n
i
n
[
3
4
]
.
R
n
is
ca
lcu
lated
u
s
i
n
g
E
q
u
atio
n
(
2
)
f
r
o
m
th
e
i
n
it
ial
v
alu
e
s
o
f
t
h
e
lattice
s
f
r
o
m
t
h
e
co
n
s
tr
u
ctio
n
o
f
J
ac
o
b
ian
m
atr
i
x
J
n
as d
o
n
e
i
n
[
3
5
]
in
ea
ch
iter
atio
n
.
T
h
en
w
e
d
ef
in
e
.
∏
(
2)
Af
ter
ca
lcu
latin
g
t
h
e
L
E
v
alu
es,
th
o
s
e
lattice
s
h
a
v
e
p
o
s
itiv
e
v
alu
e
s
ar
e
u
n
d
er
s
to
o
d
to
b
e
in
ch
ao
ti
c
r
eg
io
n
.
I
n
th
i
s
w
o
r
k
,
la
ttice
v
alu
es
ar
e
n
o
th
in
g
b
u
t
th
e
ti
m
e
s
er
ies
v
alu
e
s
o
b
tain
ed
f
r
o
m
test
d
atab
ase.
T
h
e
s
u
m
o
f
L
y
ap
u
n
o
v
ex
p
o
n
e
n
t
s
r
ev
ea
ls
t
h
e
d
a
m
p
i
n
g
n
at
u
r
e
o
f
a
s
y
s
te
m
a
n
d
an
y
c
h
a
n
g
e
s
in
d
am
p
i
n
g
co
u
ld
b
e
m
o
n
ito
r
ed
w
i
th
L
E
.
C
alc
u
lat
i
o
n
o
f
L
E
i
s
d
o
n
e
in
m
an
y
m
e
th
o
d
s
;
t
h
e
o
n
e
g
i
v
en
i
n
E
q
u
a
t
io
n
(
3
)
is
r
elate
d
to
d
is
cr
ete
ti
m
e
s
y
s
te
m
.
Fe
w
o
t
h
er
ap
p
r
o
ac
h
es
to
ca
lcu
late
L
E
f
o
r
a
c
o
n
tin
u
o
u
s
ti
m
e
s
er
ies
ar
e
r
ep
o
r
ted
b
elo
w
.
C
o
m
p
u
tin
g
L
E
a
n
d
I
n
s
tan
ta
n
eo
u
s
L
y
ap
u
n
o
v
ex
p
o
n
e
n
ts
(
I
L
E
)
u
tili
ze
d
p
h
ase
s
p
ac
e
an
d
tan
g
e
n
t
s
p
ac
e
ap
p
r
o
ac
h
in
[
3
6
]
.
I
n
an
a
lg
o
r
i
th
m
d
ev
elo
p
ed
i
n
[
3
7
]
Sh
o
r
t
t
er
m
a
v
er
ag
ed
L
y
ap
u
n
o
v
E
x
p
o
n
en
t
s
(
S
L
E
)
w
er
e
in
tr
o
d
u
ce
d
.
T
h
is
is
n
ee
d
ed
w
h
en
th
e
ex
p
er
i
m
e
n
tal
d
ata
(
ti
m
e
s
er
ies)
g
iv
e
s
in
ac
c
u
r
ate
I
L
E
f
r
o
m
a
ti
m
e
s
er
ie
s
d
u
e
to
co
m
p
u
tat
io
n
al
er
r
o
r
s
.
A
s
i
m
ilar
co
n
ce
p
t
to
t
h
e
S
L
E
,
L
o
ca
l
L
y
ap
u
n
o
v
E
x
p
o
n
e
n
t
s
(
L
L
E
)
w
as
p
r
o
p
o
s
ed
in
[
3
8
]
.
I
t
is
co
n
v
en
ie
n
t
to
m
o
d
el
a
d
y
n
a
m
ical
co
n
t
in
u
o
u
s
ti
m
e
s
y
s
te
m
b
y
o
r
d
in
ar
y
d
if
f
er
e
n
tial
eq
u
at
io
n
s
w
h
ic
h
is
o
f
t
h
e
f
o
r
m
g
i
v
e
n
in
E
q
u
atio
n
(
3
)
an
d
E
q
u
atio
n
(
4
)
.
(
)
(
3)
*
+
(
4)
W
h
er
e,
x=
[
x
1
,x
2
, ..x
n
]
T
T
h
e
ab
o
v
e
eq
u
atio
n
g
iv
e
s
a
s
et
o
f
tr
aj
ec
to
r
ies
in
p
h
ase
s
p
ac
e.
T
h
e
ith
L
y
ap
u
n
o
v
E
x
p
o
n
en
t
i
s
ca
lcu
lated
as
g
iv
e
n
i
n
E
q
u
atio
n
(
5
)
.
(
)
(
)
(
5)
W
h
er
e,
th
e
E
i
g
e
n
v
a
lu
e
s
ar
e
o
r
d
er
e
d
f
r
o
m
lar
g
est
to
s
m
all
est.
Si
n
ce
t
h
e
i
n
te
g
r
atio
n
ti
m
e
is
o
f
i
n
f
i
n
ite,
it
i
s
p
r
ac
ticall
y
n
o
t
p
o
s
s
ib
le
f
o
r
in
f
in
ite
ti
m
e
s
er
ies.
He
n
ce
,
L
E
c
alcu
latio
n
b
ased
o
n
f
in
i
te
n
u
m
b
er
o
f
iter
atio
n
s
is
g
iv
e
n
b
elo
w
in
E
q
u
a
tio
n
(
6
)
.
(
)
(
)
(
6)
L
E
g
iv
e
s
a
b
etter
id
ea
o
n
h
o
w
t
h
e
n
ea
r
b
y
o
r
b
its
d
iv
er
g
e
d
u
e
to
in
itia
l
co
n
d
itio
n
s
.
T
h
e
m
et
h
o
d
o
f
ca
lcu
lati
n
g
L
y
ap
u
n
o
v
e
x
p
o
n
e
n
ts
h
av
e
b
ee
n
alr
ea
d
y
d
ea
lt in
a
l
m
o
s
t s
i
m
ilar
m
et
h
o
d
s
as
g
iv
en
in
[
3
9
-
42
].
2
.
3
.
K
o
l
m
o
g
o
ro
v
Sin
a
i En
t
ro
py
Densi
t
y
T
h
e
s
p
atio
tem
p
o
r
al
ch
ao
tic
s
y
s
te
m
o
f
th
e
p
r
o
p
o
s
ed
s
y
s
te
m
ca
n
b
e
co
n
s
id
er
ed
as
L
d
i
m
e
n
s
io
n
s
d
y
n
a
m
ics,
t
h
e
Ko
l
m
o
g
o
r
o
v
-
Si
n
ai
e
n
tr
o
p
y
(
KSE)
o
f
t
h
e
L
d
i
m
e
n
s
io
n
s
d
y
n
a
m
ics
is
t
h
e
s
u
m
o
f
p
o
s
i
tiv
e
L
E
s
.
W
ith
o
u
t
lo
s
s
o
f
g
e
n
er
alit
y
,
th
e
Ko
l
m
o
g
o
r
o
v
-
S
in
ai
en
tr
o
p
y
d
en
s
it
y
is
e
m
p
lo
y
ed
h
er
e
to
el
i
m
i
n
ate
t
h
e
e
f
f
ec
t
o
f
n
u
m
b
er
o
f
lattice
s
,
w
h
ic
h
is
p
r
esen
ted
i
n
E
q
u
atio
n
(
7
)
as f
o
ll
o
w
s
∑
(
)
(
7)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
A
r
r
h
yth
mia
C
la
s
s
ifica
tio
n
B
a
s
ed
o
n
C
o
mb
in
e
d
C
h
a
o
tic
a
n
d
S
ta
tis
tica
l F
ea
tu
r
e
E
xtra
ctio
n
(
G.
Ja
ya
g
o
p
i
)
131
W
h
er
e,
h
is
th
e
KSE
d
en
s
it
y
a
n
d
th
e
n
u
m
er
ato
r
is
th
e
s
u
m
o
f
p
o
s
itiv
e
v
al
u
es o
f
L
E
[
4
3
]
.
2
.
4
.
K
o
l
m
o
g
o
ro
v
-
Sin
a
i En
t
ro
py
Univ
er
s
a
lity
T
h
e
Ko
l
m
o
g
o
r
o
v
-
Si
n
ai
en
tr
o
p
y
d
en
s
it
y
in
d
icate
s
w
h
et
h
er
o
r
n
o
t
t
h
e
s
p
atio
te
m
p
o
r
al
c
h
ao
t
ic
s
y
s
te
m
is
in
c
h
ao
s
.
Ho
w
ev
er
,
KSE
d
e
n
s
it
y
ca
n
n
o
t p
r
esen
t c
h
ao
tic
m
aj
o
r
ity
o
f
L
lattices
s
i
n
ce
KSE
d
en
s
it
y
is
p
o
s
iti
v
e.
Her
e,
w
e
e
m
p
lo
y
ed
KSE
g
e
n
e
r
alit
y
(
o
r
u
n
i
v
er
s
ali
t
y
)
h
u
as
g
i
v
en
i
n
E
q
u
atio
n
(
8
)
.
(
8)
W
h
er
e,
h
u
is
t
h
e
KSE
g
e
n
er
alit
y
a
n
d
L
′
is
th
e
n
u
m
b
er
o
f
p
o
s
itiv
e
L
y
ap
u
n
o
v
ex
p
o
n
e
n
t
s
i
n
s
p
atio
te
m
p
o
r
al
ch
ao
tic
s
y
s
te
m
o
f
t
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
[
4
3
]
.
T
h
e
KSE
g
e
n
er
alit
y
is
th
e
p
er
ce
n
ta
g
e
o
f
lat
tic
es
i
n
c
h
ao
s
,
w
h
ic
h
ev
alu
a
tes t
h
e
s
p
ac
e
co
m
p
lex
it
y
in
L
d
i
m
en
s
io
n
s
o
f
d
y
n
a
m
ic
s
[
4
4
]
.
2
.
5
.
Sta
nd
a
rd
Dev
ia
t
io
n
Stan
d
ar
d
d
ev
iatio
n
i
s
a
m
ea
s
u
r
e
o
f
th
e
d
i
s
p
er
s
io
n
o
f
t
h
e
d
ata
f
r
o
m
its
m
ea
n
[
4
5
]
.
T
h
e
lo
w
er
th
e
s
tan
d
ar
d
d
ev
iatio
n
,
th
e
d
ata
p
o
in
ts
ten
d
to
b
e
m
o
r
e
clo
s
e
to
th
e
m
ea
n
an
d
v
ice
v
er
s
a.
T
h
e
f
o
r
m
u
la
f
o
r
th
e
s
tan
d
ar
d
d
ev
iatio
n
o
f
th
e
g
i
v
e
n
m
atr
i
x
is
;
√
(
)
(
)
(
(
)
∑
∑
(
)
(
∑
∑
(
)
)
)
(
9)
W
h
er
e
M=
1
,
n
u
m
b
er
o
f
r
o
w
s
an
d
N
is
th
e
n
u
m
b
er
o
f
co
lu
m
n
s
.
2
.
6
.
K
urt
o
s
is
Ku
r
to
s
is
i
s
a
m
ea
s
u
r
e
o
f
w
h
e
t
h
er
th
e
d
ata
ar
e
p
ea
k
ed
o
r
f
lat
r
elativ
e
to
a
n
o
r
m
al
d
is
tr
ib
u
tio
n
.
T
h
at
is
,
d
ata
s
ets
w
it
h
h
i
g
h
k
u
r
to
s
is
te
n
d
to
h
a
v
e
a
d
is
ti
n
ct
p
ea
k
n
ea
r
th
e
m
ea
n
,
d
ec
li
n
e
r
ath
er
r
ap
i
d
ly
an
d
h
av
e
h
ea
v
y
tails
.
Data
s
et
s
w
it
h
lo
w
k
u
r
t
o
s
is
te
n
d
to
h
a
v
e
a
f
la
t
to
p
n
ea
r
th
e
m
ea
n
r
ath
er
t
h
a
n
a
s
h
ar
p
p
ea
k
[
4
5
]
.
T
h
e
f
o
r
m
u
la
f
o
r
th
e
k
u
r
to
s
is
o
f
th
e
s
elec
ted
w
in
d
o
w
len
g
t
h
is
a
s
f
o
llo
w
s
.
{
(
)
(
)
(
)
(
)
(
)
∑
∑
(
(
)
̅
)
}
(
)
(
)
(
)
(
10)
W
h
er
e
M=
1
,
n
u
m
b
er
o
f
r
o
w
s
an
d
N
is
th
e
n
u
m
b
er
o
f
co
lu
m
n
s
.
2
.
7
.
S
k
ew
nes
s
Sk
e
w
n
es
s
is
a
m
ea
s
u
r
e
o
f
t
h
e
as
y
m
m
etr
y
o
f
th
e
d
ata.
Qu
a
l
itativ
el
y
,
a
n
e
g
ati
v
e
s
k
e
w
n
es
s
in
d
icate
s
th
at
t
h
e
tail
o
n
t
h
e
le
f
t
s
id
e
o
f
th
e
Gr
a
y
L
e
v
el
Hi
s
to
g
r
a
m
(
GL
H)
is
lo
n
g
er
t
h
a
n
th
e
r
i
g
h
t
s
i
d
e,
an
d
th
e
b
u
l
k
o
f
th
e
v
al
u
es
(
i
n
cl
u
d
in
g
t
h
e
m
ed
ian
)
lie
to
th
e
r
i
g
h
t
o
f
t
h
e
m
e
an
.
A
p
o
s
iti
v
e
s
k
e
w
n
e
s
s
i
n
d
ic
ates
t
h
at
t
h
e
tail
on
th
e
r
ig
h
t
s
id
e
is
lo
n
g
er
th
a
n
th
e
lef
t
s
id
e
an
d
th
e
b
u
l
k
o
f
th
e
v
al
u
es
l
ie
to
th
e
lef
t
o
f
th
e
m
ea
n
[
4
5
]
.
T
h
e
f
o
r
m
u
la
f
o
r
th
e
s
k
e
w
n
e
s
s
o
f
t
h
e
g
iv
e
n
m
atr
ix
i
s
(
)
(
)
∑
∑
(
(
)
̅
)
(
11)
W
h
er
e
M=
1
,
n
u
m
b
er
o
f
r
o
w
s
an
d
N
is
th
e
n
u
m
b
er
o
f
co
lu
m
n
s
.
2
.
8
.
R
-
R
I
nte
rv
a
l F
ea
t
ures
B
y
n
at
u
r
e,
th
e
p
r
o
ce
s
s
o
f
p
u
m
p
in
g
b
lo
o
d
is
n
o
t
s
y
n
c
h
r
o
n
ize
d
to
an
y
s
tan
d
ar
d
clo
ck
.
B
ased
o
n
th
e
b
io
clo
ck
o
f
an
y
in
d
i
v
id
u
al,
t
h
er
e
co
u
ld
b
e
v
ar
iatio
n
s
in
t
h
e
r
h
y
t
h
m
o
f
th
e
h
ea
r
t.
T
h
is
v
ar
iatio
n
is
u
s
u
a
ll
y
ca
u
g
h
t
f
r
o
m
t
h
e
R
-
R
i
n
ter
v
al
b
et
w
e
en
t
w
o
h
ea
r
t
b
ea
ts
an
d
th
i
s
f
ea
t
u
r
e
is
a
g
o
o
d
r
ep
r
esen
tativ
e
o
f
th
e
d
y
n
a
m
ic
ch
ar
ac
ter
is
tic
o
f
t
h
e
E
C
G
s
ig
n
als.
Fo
u
r
R
-
R
f
ea
tu
r
es
ar
e
c
o
m
p
u
ted
th
at
co
r
r
esp
o
n
d
to
th
e
p
atter
n
o
f
E
C
G
s
ig
n
al,
n
a
m
el
y
,
p
r
e
R
-
R
,
p
o
s
t
R
-
R
,
lo
ca
l
R
-
R
,
a
n
d
av
er
ag
e
R
-
R
i
n
ter
v
al.
I
n
t
h
i
s
p
ap
er
,
th
e
in
ter
v
al
b
et
w
ee
n
a
p
r
ev
io
u
s
R
-
p
ea
k
an
d
t
h
e
c
u
r
r
en
t
R
-
p
ea
k
is
co
m
p
u
ted
to
d
eter
m
i
n
e
th
e
p
r
e
R
-
R
f
ea
tu
r
e,
w
h
il
e
t
h
e
i
n
ter
v
a
l
b
et
w
ee
n
a
g
iv
e
n
R
-
p
ea
k
a
n
d
th
e
f
o
llo
w
ed
R
-
p
ea
k
is
co
m
p
u
ted
to
d
eter
m
in
e
t
h
e
p
o
s
t
R
-
R
f
ea
t
u
r
e.
T
h
e
co
m
b
i
n
atio
n
o
f
th
e
p
r
e
a
n
d
p
o
s
t
R
-
R
i
n
ter
v
al
f
ea
tu
r
e
o
f
th
e
E
C
G
s
i
g
n
al
co
r
r
esp
o
n
d
s
to
an
i
n
s
ta
n
ta
n
eo
u
s
r
h
y
t
h
m
c
h
ar
ac
ter
is
tic.
T
h
e
av
er
ag
e
R
-
R
i
n
ter
v
al
f
ea
t
u
r
e
is
d
er
iv
ed
b
y
av
er
ag
i
n
g
th
e
R
-
R
in
ter
v
als
o
f
t
h
e
p
ast
3
-
m
i
n
ep
is
o
d
e
o
f
a
p
ar
ticu
lar
ev
e
n
t.
L
i
k
e
w
i
s
e,
t
h
e
lo
ca
l
-
R
-
R
f
ea
tu
r
e
is
d
er
iv
ed
b
y
a
v
er
ag
in
g
all
th
e
R
-
R
in
ter
v
a
ls
o
f
th
e
p
ast
8
-
s
ep
is
o
d
e
o
f
a
p
ar
ticu
lar
ev
en
t.
T
h
e
lo
ca
l
an
d
av
er
ag
e
f
e
at
u
r
es
r
ep
r
esen
t
th
e
av
er
a
g
e
ch
ar
ac
ter
is
tic
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o
f
a
s
er
ie
s
o
f
E
C
G
s
ig
n
al
s
.
F
u
r
th
er
,
k
u
r
to
s
i
s
,
s
k
e
w
n
ess
an
d
s
ta
n
d
ar
d
d
ev
iatio
n
ar
e
ca
lc
u
lated
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–
136
132
f
r
o
m
th
e
o
b
tain
ed
R
-
R
i
n
ter
v
al.
Fin
all
y
,
all
t
h
ese
d
y
n
a
m
ic
f
ea
t
u
r
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ar
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n
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ated
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A
s
a
r
esu
lt,
1
3
h
y
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r
id
f
ea
t
u
r
es
(
i.e
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,
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o
u
r
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ter
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m
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m
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i
m
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er
ag
e
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d
ar
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K
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it
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ilter
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s
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ass
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it
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t
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Hz
to
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em
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ll
th
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ar
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atab
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to
o
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f
ec
ti
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el
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h
e
p
r
o
p
er
u
tili
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B
f
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ctio
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(
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o
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u
ilt
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u
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ef
in
ed
)
,
to
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o
x
es
s
u
ch
as
s
t
atis
tical
to
o
l
b
o
x
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Di
g
ital
Si
g
n
al
p
r
o
ce
s
s
i
n
g
to
o
l
b
o
x
,
m
at
h
e
m
atica
l
to
o
l
b
o
x
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etc.
,
ca
n
lead
to
w
o
r
k
w
it
h
E
C
G
s
i
g
n
als
f
o
r
p
r
o
ce
s
s
in
g
an
d
an
al
y
s
is
b
o
th
in
r
ea
l
ti
m
e
a
n
d
b
y
s
i
m
u
la
tio
n
w
it
h
g
r
ea
t
ac
c
u
r
ac
y
a
n
d
co
n
v
e
n
ie
n
ce
[
4
6
]
.
E
v
alu
atio
n
o
f
Stati
s
ti
ca
l
f
ea
t
u
r
es
h
a
s
b
ee
n
i
n
s
p
ir
ed
b
y
[
4
5
]
an
d
[
4
7
]
.
Ku
r
to
s
is
,
s
k
e
w
n
e
s
s
an
d
s
tan
d
ar
d
d
ev
iatio
n
i
s
ca
lc
u
lated
f
o
r
th
e
w
in
d
o
w
o
f
R
-
R
i
n
ter
v
a
l.
Sin
ce
a
n
a
u
to
m
atic
k
n
o
w
led
g
e
d
is
co
v
er
y
i
s
ess
e
n
tial
i
n
th
i
s
p
r
o
p
o
s
ed
ar
r
h
y
t
h
m
ia
class
if
ica
tio
n
,
ch
ao
tic
m
ap
alg
o
r
ith
m
i
s
p
r
o
p
o
s
ed
to
r
ec
o
g
n
ize
th
e
p
atter
n
s
b
ased
o
n
ch
ao
tic
m
etr
ic
s
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
T
h
is
ch
a
o
tic
m
ap
alg
o
r
it
h
m
s
u
cc
ee
d
s
in
e
f
f
icie
n
t
cla
s
s
i
f
ic
atio
n
o
f
n
o
r
m
al
an
d
ab
n
o
r
m
a
l
p
atter
n
s
w
ith
b
etter
s
e
n
s
it
iv
it
y
a
n
d
s
p
ec
i
f
icit
y
[
4
8
]
.
Ho
w
ev
er
,
p
ar
a
m
eter
s
s
u
ch
as K
SE
d
en
s
it
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n
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KSE
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n
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ad
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clas
s
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f
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p
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o
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Fig
u
r
e
1
.
B
lo
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a
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p
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m
3.
RE
SU
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D
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ts
a
m
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t
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b
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f
er
s
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th
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ate
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f
c
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ec
tly
c
lass
if
ied
e
v
en
t
s
i
n
all
d
etec
ted
ev
en
t
s
.
Usi
n
g
t
h
ese
d
ef
in
i
tio
n
s
,
s
en
s
iti
v
it
y
a
n
d
s
p
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i
f
icit
y
ca
n
b
e
d
ef
in
ed
as
,
(
12)
T
h
e
o
v
er
all
ac
cu
r
ac
y
an
d
er
r
o
r
r
ate
ca
n
b
e
d
ef
in
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as g
i
v
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n
i
n
E
q
u
atio
n
1
3
an
d
1
4
r
esp
ec
tiv
el
y
.
(
)
(
13)
(
)
(
14)
A
ll
th
e
s
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ab
o
v
e
-
m
e
n
tio
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p
ar
a
m
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d
h
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g
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t
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s
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m
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lat
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ca
r
r
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o
u
t u
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in
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MI
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-
B
I
H
d
atab
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Evaluation Warning : The document was created with Spire.PDF for Python.
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J
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A
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(
G.
Ja
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133
3
.
2
.
Resul
t
s
a
n
d Ana
ly
s
is
T
h
e
p
r
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p
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f
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0
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R
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p
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in
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s
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p
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i.e
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f
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a
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is
o
f
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s
ig
n
al
s
.
T
h
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ex
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m
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ts
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o
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t
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e
p
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p
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m
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h
o
d
o
lo
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r
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d
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t
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d
v
alid
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s
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m
ar
k
MI
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-
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I
H
ar
r
h
y
t
h
m
ia
d
atab
ase.
T
h
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SVM
clas
s
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f
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ain
ed
o
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t
h
e
tr
ai
n
in
g
d
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s
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m
en
tio
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ed
in
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ab
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1
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d
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p
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f
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ce
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s
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al.
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e
p
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ed
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f
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n
to
t
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s
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s
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t
ca
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ies
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s
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n
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th
e
p
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p
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m
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h
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d
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lo
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y
is
p
r
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e
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t
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f
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m
o
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c
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n
f
u
s
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m
atr
i
x
s
h
o
w
n
in
T
ab
le
2
.
No
r
m
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ll
y
i
n
all
class
i
f
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n
w
o
r
k
s
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if
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ai
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g
s
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in
cr
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e
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s
e
in
th
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u
m
b
e
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tr
ain
i
n
g
s
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g
n
als
w
il
l
lead
to
in
cr
ea
s
ed
class
if
icatio
n
ac
c
u
r
ac
y
.
I
n
[
3
7
]
,
f
iv
e
class
es
o
f
E
C
G
s
i
g
n
als
ar
e
class
i
f
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ac
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f
9
3
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n
t
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s
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th
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m
b
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test
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g
s
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n
als.
Ho
w
e
v
er
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th
e
ex
p
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m
e
n
ts
s
ee
n
in
[
3
7
]
ar
e
p
er
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o
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m
ed
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l
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el
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ted
r
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e
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s
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u
s
t
if
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b
y
th
e
r
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p
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tiv
e
r
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ch
er
s
.
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h
e
ad
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an
tag
e
o
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t
h
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p
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ed
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i.e
.
,
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ased
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R
-
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ter
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m
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ch
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t
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n
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t
ch
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ter
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th
e
E
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G
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n
a
l.
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h
e
co
m
b
in
a
tio
n
o
f
b
o
th
t
h
ese
co
m
b
i
n
ed
f
ea
tu
r
e
s
y
ield
ed
i
m
p
r
o
v
ed
class
i
f
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n
ac
cu
r
ac
y
.
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n
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h
eles
s
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e
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m
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al
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m
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lex
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e
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p
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s
ed
m
et
h
o
d
o
lo
g
y
n
ee
d
s
to
b
e
ev
alu
a
ted
f
o
r
r
ea
l
-
ti
m
e
ap
p
lica
tio
n
s
.
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n
ad
d
itio
n
,
t
h
e
p
r
o
p
o
s
ed
tech
n
iq
u
e
i
s
v
alid
ated
o
n
all
th
e
E
C
G
d
ata
(
i.e
.
,
w
it
h
o
u
t
ex
cl
u
d
in
g
an
y
s
e
g
m
en
t)
o
f
b
en
ch
m
ar
k
MI
T
-
B
I
H
ar
r
h
y
th
m
ia
d
atab
ase
w
ith
2
1
.
8
%
tr
ain
in
g
d
ata
lead
in
g
to
less
co
n
s
u
m
p
tio
n
o
f
tr
ai
n
in
g
t
i
m
e
an
d
m
e
m
o
r
y
o
n
t
h
e
h
ar
d
w
ar
e
(
is
v
al
id
,
b
ec
au
s
e,
r
u
n
n
i
n
g
ti
m
e
o
f
alg
o
r
ith
m
is
a
r
ea
l t
h
r
ea
t d
u
r
in
g
tr
ain
i
n
g
ten
u
r
e
).
3
.
3
.
Co
nfusi
o
n M
a
t
rix
f
o
r
P
r
o
po
s
ed
M
o
del
I
n
o
r
d
er
to
ex
p
lain
th
e
co
n
f
u
s
io
n
m
atr
ix
b
etter
,
an
e
x
a
m
p
le
is
p
r
ese
n
ted
b
y
tak
i
n
g
n
o
r
m
a
l
class
o
f
s
ig
n
al
s
an
d
r
elate
d
co
u
n
t
v
al
u
es f
o
r
an
e
x
a
m
p
le.
T
h
e
f
ir
s
t r
o
w
co
r
r
esp
o
n
d
s
to
th
e
n
o
r
m
al
c
ateg
o
r
y
an
d
i
m
p
lies
th
at
6
3
1
8
7
s
ig
n
als
ar
e
co
r
r
ec
tly
d
etec
ted
as
n
o
r
m
al
s
ig
n
al
s
b
y
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
a
m
o
n
g
6
3
7
6
4
ac
tu
al
n
u
m
b
er
s
o
f
n
o
r
m
al
s
i
g
n
al
s
an
d
th
e
r
est
o
f
th
e
n
o
r
m
al
s
i
g
n
als
ar
e
m
i
s
clas
s
if
ied
in
th
e
o
th
er
ca
teg
o
r
ies.
I
n
co
lu
m
n
1
,
6
3
3
2
8
n
o
r
m
al
s
i
g
n
als
ar
e
d
etec
ted
i
n
t
h
e
n
o
r
m
al
ca
teg
o
r
y
t
h
at
in
cl
u
d
es
s
i
g
n
als
f
r
o
m
th
e
o
th
er
ca
teg
o
r
ies,
i.e
.
,
6
3
1
8
7
n
o
r
m
al
s
ig
n
al
s
ar
e
co
r
r
ec
tl
y
clas
s
if
ied
a
n
d
th
e
s
i
g
n
al
s
f
r
o
m
o
th
er
class
e
s
ar
e
m
is
c
lass
if
ied
in
to
t
h
e
n
o
r
m
al
ca
teg
o
r
y
r
ep
r
esen
ti
n
g
a
to
ta
l
o
f
6
3
3
2
8
s
ig
n
al
s
.
I
n
t
h
e
s
a
m
e
p
r
o
ce
d
u
r
e,
t
h
e
class
i
f
icatio
n
r
es
u
lt
s
f
o
r
t
h
e
o
t
h
er
1
5
ca
teg
o
r
ies
o
f
E
C
G
s
i
g
n
als
ar
e
also
ca
lcu
la
ted
an
d
p
r
esen
ted
i
n
T
ab
le
3
.
M
o
r
eo
v
er
,
o
u
t
o
f
8
6
1
1
3
test
s
ig
n
a
ls
in
to
tal,
8
5
2
0
9
s
ig
n
als
ar
e
co
r
r
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tly
cla
s
s
i
f
ied
an
d
9
0
4
s
ig
n
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.
Ho
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in
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ea
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E
C
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Evaluation Warning : The document was created with Spire.PDF for Python.
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N:
2502
-
4752
A
r
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mia
C
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tio
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B
a
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ta
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G.
Ja
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g
o
p
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)
135
p
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o
ce
s
s
in
g
,
p
ar
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p
r
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ce
s
s
i
n
g
s
c
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es,
FP
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d
A
SIC
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th
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an
d
o
f
f
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n
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m
o
d
es.
RE
F
E
R
E
NC
E
S
[1
]
Ch
a
z
a
l
P
D,
O‟D
wy
e
r
M
,
Re
il
l
y
RB.
“
A
u
to
m
a
ti
c
c
las
si
f
ic
a
ti
o
n
o
f
h
e
a
rtb
e
a
ts
u
sin
g
ECG
m
o
rp
h
o
lo
g
y
a
n
d
h
e
a
rt
b
e
a
t
in
terv
a
l
f
e
a
tu
re
s
”
.
IEE
E
T
ra
n
s.
B
i
o
me
d
.
E
n
g
.
2
0
0
4
;
5
1
(
7
):
1
1
9
6
-
1
2
0
6
.
[2
]
Ch
a
z
a
l
P
D,
Re
il
ly
RB.
“
A
p
a
ti
e
n
t
-
a
d
a
p
ti
n
g
h
e
a
rt
b
e
a
t
c
las
sif
ier
u
sin
g
ECG
m
o
rp
h
o
l
o
g
y
a
n
d
h
e
a
rtb
e
a
t
in
terv
a
l
f
e
a
tu
re
s
”
.
IEE
E
T
ra
n
s.B
i
o
me
d
.
E
n
g
.
2
0
0
6
;
5
3
(1
2
):
2
5
3
5
-
2
5
4
3
.
[3
]
M
it
ra
S
,
M
it
ra
M
,
Ch
a
u
d
h
u
ri
B
B.
“
A
Ro
u
g
h
se
t
b
a
s
e
d
in
f
e
r
e
n
c
e
e
n
g
in
e
f
o
r
EC
G
c
las
si
f
ic
a
ti
o
n
”
.
IEE
E
T
ra
n
s.
In
stru
m.
M
e
a
s
.
2
0
0
6
;
5
5
(6
):
2
1
9
8
-
2
2
0
6
.
[4
]
Ra
j
S
,
M
a
u
ry
a
K,
Ra
y
KC.
“
A
k
n
o
w
led
g
e
-
b
a
se
d
re
a
l
ti
m
e
e
m
b
e
d
d
e
d
p
latf
o
rm
f
o
r
a
rrh
y
th
m
ia
b
e
a
t
c
las
si
f
ica
ti
o
n
.
Bio
m
e
d
”
.
En
g
.
L
e
tt
.
2
0
1
5
;
5
(4
)
:
2
7
1
-
2
8
0
.
[5
]
M
in
a
m
i
K,
Na
k
a
ji
m
a
H,
T
o
y
o
s
h
im
a
T
.
“
Re
a
l
-
ti
m
e
d
isc
ri
m
in
a
ti
o
n
o
f
v
e
n
tri
c
u
lar
tac
h
y
a
rrh
y
th
m
ia
w
it
h
F
o
u
rier
tran
sf
o
r
m
n
e
u
ra
l
n
e
tw
o
rk
”
.
IEE
E
T
ra
n
s.
Bi
o
me
d
.
En
g
.
1
9
9
9
;
4
6
(2
)
:
1
7
9
-
1
8
5
.
[6
]
In
c
e
T
,
Kira
n
y
a
z
S
,
Ga
b
b
o
u
j
M
.
“
A
g
e
n
e
ric
a
n
d
ro
b
u
st
sy
ste
m
f
o
r
a
u
to
m
a
ted
p
a
ti
e
n
t
-
sp
e
c
if
ic
c
las
si
f
ica
ti
o
n
o
f
EC
G
sig
n
a
ls
”
.
IEE
E
T
ra
n
s.B
i
o
me
d
.
E
n
g
.
2
0
0
9
;
5
6
(
5
):
1
4
1
5
-
1
4
2
6
.
[7
]
Ba
n
e
rjee
S
,
M
i
tra
M
.
“
A
p
p
li
c
a
ti
o
n
o
f
c
ro
ss
w
a
v
e
let
tran
sf
o
r
m
f
o
r
ECG
p
a
tt
e
rn
a
n
a
ly
sis
a
n
d
c
las
sif
ica
ti
o
n
”
.
IEE
E
T
ra
n
s.
I
n
stru
m.
M
e
a
s
.
2
0
1
4
;
6
3
(2
):
3
2
6
-
3
3
3
.
[8
]
Ra
j
S
,
Ch
a
n
d
G
S
S
P
,
Ra
y
KC.
“
A
RM
b
a
se
d
a
rrh
y
th
m
ia
b
e
a
t
m
o
n
it
o
ri
n
g
s
y
ste
m
,
M
icro
p
ro
c
e
ss
”
.
M
icr
o
sy
st
.
2
0
1
5
;
3
9
(
7
):
5
0
4
-
5
1
1
.
[9
]
M
e
lg
a
n
i
F
,
Ba
z
i
Y.
“
Clas
si
f
ic
a
ti
o
n
o
f
e
lec
tro
c
a
rd
io
g
ra
m
sig
n
a
ls
w
it
h
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s
a
n
d
p
a
rti
c
le
sw
a
r
m
o
p
ti
m
iza
ti
o
n
”
.
IEE
E
T
ra
n
s.
I
n
f.
T
e
c
h
n
o
l.
Bi
o
me
d
.
2
0
0
8
;
1
2
(
5
):
6
6
7
-
6
7
7
.
[1
0
]
Os
o
w
s
k
i
S
,
Ho
a
i
L
T
,
M
a
rk
i
e
wic
z
T
.
“
S
u
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
b
a
se
d
e
x
p
e
rt
s
y
ste
m
f
o
r
re
li
a
b
le
h
e
a
rt
b
e
a
t
re
c
o
g
n
it
io
n
”
.
IEE
E
T
ra
n
s.B
io
me
d
.
En
g
.
2
0
0
4
;
5
1
(4
):
5
8
2
–
5
8
9
.
[1
1
]
Ra
j
S
,
L
u
th
ra
S
,
Ra
y
KC.
“
De
v
e
lo
p
m
e
n
t
o
f
h
a
n
d
h
e
l
d
c
a
rd
iac
e
v
e
n
t
m
o
n
it
o
ri
n
g
sy
ste
m
”
.
IFA
C
-
Pa
p
e
rs
On
L
i
n
e
.
2
0
1
5
;
4
8
(
4
):
7
1
-
7
6
.
[1
2
]
Ye
C,
Ku
m
a
r
BV
KV
,
C
o
im
b
ra
M
T
.
“
He
a
rt
b
e
a
t
c
las
si
f
ica
ti
o
n
u
s
in
g
m
o
rp
h
o
l
o
g
ica
l
a
n
d
d
y
n
a
m
ic
f
e
a
tu
re
s
o
f
ECG
sig
n
a
ls
”
.
IEE
E
T
ra
n
s.
Bi
o
me
d
.
En
g
.
2
0
1
2
;
5
9
(
1
0
)
:
2
9
3
0
-
2
9
4
1
.
[1
3
]
Ra
j
S
,
Ra
y
KC,
S
h
a
n
k
a
r
O.
“
Ca
rd
iac
a
rrh
y
th
m
ia
b
e
a
t
c
las
si
f
ica
ti
o
n
u
sin
g
DO
S
T
a
n
d
P
S
O
t
u
n
e
d
S
V
M
”
.
Co
m
p
u
t
.
M
e
th
o
d
s P
ro
g
ra
ms
Bi
o
me
d
.
2
0
1
6
;
1
3
6
:
1
6
3
-
1
7
7
.
[1
4
]
L
in
h
T
H,
Os
o
w
s
k
i
S
,
S
to
d
o
lsk
i
M
.
“
On
-
li
n
e
h
e
a
rt
b
e
a
t
re
c
o
g
n
it
i
o
n
u
sin
g
He
rm
it
e
p
o
ly
n
o
m
ial
s
a
n
d
n
e
u
ro
-
f
u
z
z
y
n
e
tw
o
rk
”
.
IEE
E
T
ra
n
s.
In
stru
m
.
M
e
a
s
.
2
0
0
3
;
5
2
(
4
):
1
2
2
4
-
1
2
3
1
.
[1
5
]
L
la
m
e
d
o
M
,
M
a
rt
in
e
z
J
P
.
“
He
a
rt
b
e
a
t
c
las
sif
ica
ti
o
n
u
sin
g
f
e
a
t
u
re
se
lec
ti
o
n
d
r
iv
e
n
b
y
d
a
tab
a
se
g
e
n
e
ra
li
z
a
ti
o
n
c
rit
e
ria
”
.
IEE
E
T
ra
n
s.B
i
o
me
d
.
E
n
g
.
2
0
1
1
;
5
8
(
3
):
6
1
6
-
6
2
5
.
[1
6
]
F
ra
se
r
G
D,
Ch
a
n
AD
C,
G
re
e
n
JR
,
M
a
c
isa
a
c
D
T
.
“
A
u
to
m
a
ted
b
io
sig
n
a
l
q
u
a
li
ty
a
n
a
l
y
sis
f
o
r
e
l
e
c
tro
m
y
o
g
ra
p
h
y
u
sin
g
a
o
n
e
c
las
s su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
”
.
IEE
E
T
ra
n
s.
I
n
stru
m.
M
e
a
s
.
2
0
1
4
;
6
3
(1
2
):
2
9
1
9
-
2
9
3
0
.
[1
7
]
Ra
j
S
,
Ra
y
KC.
“
A
c
o
m
p
a
ra
ti
v
e
stu
d
y
o
f
m
u
lt
iv
a
riate
a
p
p
ro
a
c
h
w
it
h
n
e
u
ra
l
n
e
tw
o
rk
s
a
n
d
su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s
f
o
r
a
rrh
y
th
m
ia
c
l
a
ss
i
f
ica
ti
o
n
”
.
Pro
c
.
In
t
.
Co
n
f.
E
n
e
rg
y
,
P
o
we
r
En
v
iro
n
.
,
T
o
w
a
rd
s
S
u
sta
in
.
Gr
o
wth
(
ICEP
E)
,
2
0
1
5
;
1
-
6.
[1
8
]
Ha
i
m
a
n
D,
Ya
n
g
B,
S
u
ip
i
n
g
Z,
Ho
n
g
ru
i
W
,
X
iu
li
n
g
L
.
“
A
No
v
e
l
M
e
th
o
d
f
o
r
Dia
g
n
o
sin
g
P
re
m
a
tu
re
V
e
n
tri
c
u
lar
Co
n
trac
ti
o
n
Be
a
t
Ba
se
d
o
n
Ch
a
o
s
T
h
e
o
r
y
”
.
Pro
c
e
e
d
in
g
s
o
f
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1
t
h
In
ter
n
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t
io
n
a
l
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n
fer
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n
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e
o
n
Fu
z
zy
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ste
ms
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n
d
Kn
o
wled
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e
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ry
(
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.
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m
e
n
,
Ch
in
a
.
2
0
1
4
;
4
9
7
-
5
0
1
.
[1
9
]
Kira
n
y
a
z
S
,
In
c
e
T
.
G
a
b
b
o
u
j
M
.
“
Re
a
l
-
T
i
m
e
P
a
ti
e
n
t
-
S
p
e
c
if
ic
ECG
Clas
si
f
ica
ti
o
n
b
y
1
D
Co
n
v
o
lu
ti
o
n
a
l
Ne
u
ra
l
Ne
tw
o
rk
s
”
.
IEE
E
T
ra
n
s
.
Bi
o
me
d
.
En
g
.
2
0
1
6
;
6
3
(
3
):
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6
4
–
6
7
5
.
[2
0
]
S
m
ru
th
y
A
,
S
u
c
h
e
th
a
M
.
“
Re
a
l
-
T
i
m
e
Clas
sif
i
c
a
ti
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n
o
f
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a
lt
h
y
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n
d
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p
n
e
a
S
u
b
jec
ts
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si
n
g
EC
G
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ig
n
a
ls
w
it
h
V
a
riatio
n
a
l
M
o
d
e
De
c
o
m
p
o
siti
o
n
”
.
IEE
E
S
e
n
so
rs
J
o
u
rn
a
l
.
2
0
1
7
;
1
7
(1
0
):
3
0
9
2
-
3
0
9
9
.
[2
1
]
G
a
n
e
sh
KR,
Ku
m
a
ra
s
wa
m
y
YS.
“
S
p
li
n
e
A
c
ti
v
a
ted
Ne
u
ra
l
Ne
t
w
o
rk
F
o
r
Clas
sify
in
g
Ca
rd
iac
A
r
rh
y
t
h
m
ia
”
.
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
S
c
ien
c
e
.
2
0
1
4
;
1
0
(
8
):
1
5
8
2
-
1
5
9
0
.
[2
2
]
Ja
in
S
,
Ba
jaj
V
,
Ku
m
a
r
A
.
“
E
ff
ic
ien
t
a
lg
o
rit
h
m
f
o
r
c
las
si
f
ica
ti
o
n
o
f
e
le
c
tro
c
a
rd
io
g
ra
m
b
e
a
ts
b
a
se
d
o
n
a
rti
f
icia
l
b
e
e
c
o
lo
n
y
-
b
a
se
d
lea
st
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sq
u
a
re
s su
p
p
o
rt
v
e
c
to
r
m
a
c
h
in
e
s cla
ss
if
ier
”
.
IET
El
e
c
tr
o
n
ics
L
e
t
ter
s
.
2
0
1
6
;
5
2
(1
4
):
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1
9
8
-
1
2
0
0
.
[2
3
]
S
a
n
d
e
e
p
G
,
Ch
e
n
g
Q.
“
Jo
in
t
F
e
a
t
u
re
Ex
trac
ti
o
n
a
n
d
Clas
sif
ier D
e
si
g
n
f
o
r
EC
G
B
a
se
d
Bio
m
e
tri
c
R
e
c
o
g
n
it
io
n
”
.
IEE
E
J
o
u
rn
a
l
o
f
Bi
o
me
d
ica
l
a
n
d
He
a
l
t
h
In
f
o
rm
a
ti
c
s
.
2
0
1
6
;
2
0
(2
):
4
6
0
-
4
6
8
.
[2
4
]
T
rip
a
th
y
RK,
Da
n
d
a
p
a
t
S
.
“
A
u
to
m
a
ted
d
e
tec
ti
o
n
o
f
h
e
a
rt
a
il
m
e
n
ts
f
ro
m
1
2
-
lea
d
ECG
u
sin
g
c
o
m
p
lex
w
a
v
e
let
su
b
-
b
a
n
d
b
i
-
sp
e
c
tr
u
m
f
e
a
tu
re
s
”
.
IET
He
a
lt
h
c
a
re
T
e
c
h
n
o
l
o
g
y
L
e
tt
e
rs
.
2
0
1
7
;
4
(
2
):
5
7
-
6
3
.
[2
5
]
P
e
n
g
f
e
i
L
,
W
a
n
g
Y,
Jia
n
g
c
h
u
n
H,
W
a
n
g
L
,
T
ian
Y,
Zh
o
u
T
,
T
ian
c
h
a
n
g
L
,
Jin
g
-
so
n
g
L
.
“
H
ig
h
P
e
rf
o
rm
a
n
c
e
P
e
rs
o
n
a
li
z
e
d
He
a
rt
b
e
a
t
Clas
sif
ica
ti
o
n
M
o
d
e
l
f
o
r
L
o
n
g
-
T
e
r
m
EC
G
S
ig
n
a
l
”
.
IEE
E
T
ra
n
s.
Bi
o
me
d
.
E
n
g
.
2
0
1
7
;
6
4
(1
)
:
78
-
86.
[2
6
]
“
F
re
q
u
e
n
c
y
-
d
o
m
a
in
f
e
a
tu
re
s f
o
r
ECG
b
e
a
t
d
isc
ri
m
in
a
ti
o
n
u
sin
g
g
re
y
re
latio
n
a
l
a
n
a
l
y
sis
-
b
a
se
d
c
las
sif
ier
”
.
Co
mp
u
ter
s
a
n
d
M
a
th
e
ma
ti
c
s wit
h
Ap
p
li
c
a
t
io
n
s
.
2
0
0
8
;
5
5
:
6
8
0
-
6
9
0
.
[2
7
]
M
o
d
j
tab
a
R,
Re
z
a
S
.
“
Ne
u
ra
l
Ne
tw
o
rk
s b
a
s
e
d
Dia
g
n
o
sis o
f
h
e
a
rt
a
r
rh
y
th
m
ias
u
sin
g
c
h
a
o
ti
c
a
n
d
n
o
n
li
n
e
a
r
f
e
a
tu
re
s o
f
HRV
sig
n
a
ls
”
.
Pro
c
e
e
d
in
g
s
o
f
In
ter
n
a
t
io
n
a
l
Asso
c
i
a
ti
o
n
o
f
Co
mp
u
ter
S
c
ien
c
e
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
-
S
in
g
a
p
o
re
.
2
0
0
9
;
5
4
5
-
5
4
9
.
[2
8
]
Ub
e
y
li
ED.
“
Re
c
u
rre
n
t
n
e
u
ra
l
n
e
tw
o
rk
s
e
m
p
lo
y
in
g
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
ts
f
o
r
a
n
a
ly
sis
o
f
EC
G
s
ig
n
a
ls
”
.
Exp
e
rt
S
y
ste
ms
wit
h
Ap
p
li
c
a
ti
o
n
s
.
2
0
1
0
;
3
7
:
1
1
9
2
–
1
1
9
9
.
[2
9
]
Ra
j
S
,
Ra
y
KC.
“
EC
G
S
ig
n
a
l
A
n
a
ly
sis Us
in
g
DCT
-
B
a
se
d
DO
S
T
a
n
d
P
S
O O
p
ti
m
ize
d
S
V
M
”.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
12
,
No
.
1
,
Octo
b
er
2
0
1
8
:
1
2
7
–
136
136
[3
0
]
S
a
b
a
r
S
,
D
jan
g
g
a
n
S
,
S
e
t
y
a
w
a
n
P
S
,
A
n
d
a
rin
i
S
.
“
T
h
e
P
e
a
k
o
f
th
e
P
QRST
a
n
d
th
e
T
ra
je
c
to
r
y
P
a
th
o
f
Eac
h
C
y
c
le
o
f
th
e
ECG
1
2
-
L
e
a
d
W
a
v
e
”
.
In
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
(
IJ
EE
CS
)
.
2
0
1
6
;
4
(1
):
1
6
9
-
1
7
5
.
[3
1
]
S
riL
a
k
sh
m
i
P
,
L
o
k
e
sh
Ra
ju
V
.
“
ECG
De
-
n
o
isin
g
u
sin
g
Hy
b
rid
L
in
e
a
riza
ti
o
n
M
e
th
o
d
”
.
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
.
2
0
1
5
;
1
5
(
3
):
5
0
4
-
5
0
8
.
[3
2
]
S
a
rth
a
k
P
,
M
i
h
ir
NM.
“
Im
p
u
lsiv
e
No
ise
C
a
n
c
e
ll
a
ti
o
n
f
ro
m
ECG
S
ig
n
a
l
u
sin
g
A
d
a
p
ti
v
e
F
il
ters
a
n
d
th
e
ir
Co
m
p
a
riso
n
”
.
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
i
n
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
(
IJ
EE
CS
)
.
2
0
1
6
;
3
(2
):
3
6
9
-
3
7
6
.
[3
3
]
M
o
o
d
y
G
B,
M
a
rk
R
G
.
“
T
h
e
i
m
p
a
c
t
o
f
th
e
M
IT
-
BIH
a
rrh
y
th
m
ia
d
a
tab
a
se
”
.
IEE
E
En
g
.
M
e
d
.
Bi
o
l
.
M
a
g
.
2
0
0
1
;
2
0
(3
)
:
45
-
50.
[3
4
]
S
h
ib
a
ta H.
“
KS
e
n
tr
o
p
y
a
n
d
m
e
a
n
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
t
f
o
r
c
o
u
p
led
m
a
p
latti
c
e
s
”
.
P
h
y
sic
a
A
.
2
0
0
1
;
2
9
2
:
1
8
2
-
1
9
2
.
[3
5
]
Ho
ld
e
n
A
V
,
Zh
a
n
g
H.
“
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
t
sp
e
c
tru
m
f
o
r
a
g
e
n
e
ra
li
z
e
d
c
o
u
p
led
m
a
p
latti
c
e
”
.
Ch
a
o
s
S
o
li
t
o
n
s
Fra
c
ta
ls
.
1
9
9
2
;
2
:
1
5
5
-
1
6
4
.
[3
6
]
W
o
lf
F
A
,
S
w
i
f
t
JB,
S
w
in
n
e
y
H
L,
V
a
sta
n
o
JA
.
“
De
ter
m
in
in
g
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
ts
f
ro
m
a
ti
m
e
s
e
r
ies
”
.
Ph
y
sic
a
D
.
1
9
8
5
;
1
6
:
2
8
5
-
3
1
7
.
[3
7
]
S
a
n
o
M
,
S
a
w
a
n
a
Y.
“
M
e
a
su
re
m
e
n
t
o
f
th
e
Ly
a
p
u
n
o
v
sp
e
c
tru
m
f
ro
m
a
c
h
a
o
ti
c
ti
m
e
se
ri
e
s
”
.
Ph
y
sic
a
l
Rev
iew
L
e
tt
e
rs
.
1
9
8
5
;
5
5
:
1
0
8
2
-
1
0
8
5
.
[3
8
]
A
b
a
rb
a
n
e
l
HD
I,
Bro
w
n
R,
K
e
n
n
e
l
M
B.
“
V
i
b
ra
ti
o
n
o
f
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
ts
o
n
a
stra
n
g
e
a
tt
ra
c
to
r
”
.
J
o
u
rn
a
l
o
f
No
n
li
n
e
a
r
S
c
ien
c
e
.
1
9
9
1
;
1
:
1
7
5
-
1
9
9
.
[3
9
]
Bro
w
n
R,
Bry
a
n
t
P
,
A
b
a
rb
a
n
e
l
H
DI.
“
Co
m
p
u
ti
n
g
th
e
L
y
a
p
u
n
o
v
sp
e
c
tru
m
o
f
a
d
y
n
a
m
i
c
a
l
s
y
st
e
m
f
r
o
m
a
n
o
b
se
rv
e
d
ti
m
e
s
e
ries
”
.
Ph
y
s.
Rev
.
A
.
1
9
9
1
;
4
3
:
2
7
8
7
-
2
8
0
6
.
[4
0
]
Ro
se
n
ste
in
M
T
,
Co
ll
i
n
s
JJ
,
De
L
u
c
a
CJ.
“
A
p
ra
c
ti
c
a
l
m
e
t
h
o
d
f
o
r
c
a
lcu
latin
g
larg
e
st
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
ts
f
ro
m
s
m
a
ll
d
a
ta se
ts
”
.
Ph
y
s.
D,
N
o
n
li
n
e
a
r P
h
e
n
o
m
.
1
9
9
3
;
6
5
:
1
1
7
-
1
3
4
.
[4
1
]
S
e
k
h
a
v
a
t
P
,
S
e
p
e
h
ri
N,
W
u
Q.
“
Ca
lcu
latio
n
o
f
Ly
a
p
u
n
o
v
e
x
p
o
n
e
n
ts
u
si
n
g
n
o
n
sta
n
d
a
rd
f
in
it
e
d
if
f
e
re
n
c
e
d
isc
re
ti
z
a
ti
o
n
sc
h
e
m
e
:
a
c
a
se
stu
d
y
”
.
J
.
Diff
e
r.
Eq
u
.
A
p
p
l
.
2
0
0
4
;
1
0
(
4
):
3
6
9
-
3
7
8
.
[4
2
]
W
u
Q,
S
e
k
h
a
v
a
t
P
,
S
e
p
e
h
ri
N,
P
e
les
S
.
“
On
d
e
sig
n
o
f
c
o
n
ti
n
u
o
u
s
Ly
a
p
u
n
o
v
‟s
f
e
e
d
b
a
c
k
c
o
n
tro
l
”
.
J
.
Fra
n
k
li
n
In
st
.
En
g
.
Ap
p
l.
M
a
th
.
2
0
0
5
;
3
4
2
(6
):
7
0
2
-
7
2
3
.
[4
3
]
Yin
g
-
Qia
n
Z,
X
i
n
g
-
Yu
a
n
W
.
“
S
p
a
ti
o
tem
p
o
ra
l
c
h
a
o
s
in
A
rn
o
ld
c
o
u
p
led
lo
g
isti
c
m
a
p
latti
c
e
,
No
n
li
n
e
a
r
A
n
a
l
y
sis:
M
o
d
e
li
n
g
a
n
d
Co
n
tro
l
”
.
2
0
1
3
;
1
8
(4
):
5
2
6
-
5
4
1
.
[4
4
]
Zh
a
n
g
Y,
W
a
n
g
X
.
“
S
p
a
ti
o
tem
p
o
ra
l
c
h
a
o
s
in
m
ix
e
d
li
n
e
a
r
-
n
o
n
li
n
e
a
r
c
o
u
p
led
lo
g
isti
c
m
a
p
latti
c
e
”
.
Ph
y
sic
a
A
.
2
0
1
4
;
4
0
2
:
1
0
4
-
1
1
8
.
[4
5
]
Yu
n
f
e
i
W
,
Qiz
h
e
n
g
Y,
X
in
g
w
a
n
g
L
,
Da
n
T
.
“
Clas
si
f
ica
ti
o
n
o
f
D
iele
c
tri
c
Ba
rrier
Disc
h
a
rg
e
s
Us
in
g
Dig
it
a
l
I
m
a
g
e
P
r
o
c
e
ss
in
g
T
e
c
h
n
o
lo
g
y
”
.
IEE
E
T
ra
n
sa
c
ti
o
n
s O
n
Pl
a
sm
a
S
c
ien
c
e
.
2
0
1
2
;
4
0
(
5
):
5
.
[4
6
]
“
S
tu
d
y
a
n
d
A
n
a
l
y
sis
o
f
EC
G
S
ig
n
a
l
Us
in
g
M
ATLA
B
&
LA
BV
IEW
a
s
E
ff
e
c
ti
v
e
T
o
o
ls
”
.
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
a
n
d
El
e
c
trica
l
En
g
in
e
e
rin
g
.
2
0
1
2
;
4
(
3
).
[4
7
]
Ja
v
ier
G
,
S
é
b
a
stien
M
,
Ju
li
a
n
F
.
“
I
m
a
g
e
Qu
a
li
ty
A
ss
e
ss
m
e
n
t
f
o
r
F
a
k
e
Bio
m
e
tri
c
De
te
c
ti
o
n
:
A
p
p
li
c
a
ti
o
n
to
Iris,
F
in
g
e
rp
rin
t,
a
n
d
F
a
c
e
Re
c
o
g
n
it
io
n
”
.
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Im
a
g
e
Pro
c
e
ss
in
g
.
2
0
1
4
;
2
3
(2
):
7
1
0
-
7
2
4
.
[4
8
]
Ro
b
e
rto
B
,
M
a
rc
e
ll
o
C,
F
ra
n
c
e
sc
o
DC,
“
A
Ch
a
o
ti
c
M
a
p
A
lg
o
rit
h
m
f
o
r
Kn
o
w
led
g
e
Dis
c
o
v
e
r
y
in
T
i
m
e
S
e
rie
s:
A
Ca
se
S
tu
d
y
o
n
Bi
o
m
e
d
ica
l
S
ig
n
a
ls
”
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Nu
c
lea
r S
c
ien
c
e
.
2
0
0
4
;
5
1
(3
)
.
[4
9
]
L
i
H,
Hu
a
n
L
,
Ch
u
n
ji
a
o
M
,
L
u
C,
X
i
u
li
F
,
Ch
u
n
x
iao
T
,
L
i
E.
“
No
v
e
l
EC
G
sig
n
a
l
c
las
si
f
ic
a
ti
o
n
b
a
se
d
o
n
KICA
n
o
n
li
n
e
a
r
f
e
a
tu
re
e
x
trac
ti
o
n
”
.
Circ
u
it
s,
S
y
st.
,
S
i
g
n
a
l
Pro
c
e
ss
.
2
0
1
6
;
3
5
(
4
):
1
1
8
7
-
1
1
9
7
.
[5
0
]
Ro
d
rig
u
e
z
J,
G
o
n
i
A
,
Ill
a
rra
m
e
n
d
i
A
.
“
Re
a
l
-
ti
m
e
c
la
ss
i
f
ica
ti
o
n
o
f
EC
G
s
o
n
a
P
DA
”
.
IEE
E
T
ra
n
s.
In
f.
T
e
c
h
n
o
l.
Bi
o
me
d
.
2
0
0
5
;
9
(
1
):
2
3
-
34.
[5
1
]
M
a
rti
s
RJ,
A
c
h
a
r
y
a
UR,
M
a
n
d
a
n
a
KM,
Ra
y
A
K,
Ch
a
k
ra
b
o
rty
C.
“
Ca
rd
iac
d
e
c
isio
n
m
a
k
in
g
u
sin
g
h
ig
h
e
r
o
r
d
e
r
sp
e
c
tra
”
.
Bi
o
me
d
.
S
ig
n
a
l
Pro
c
e
ss
.
Co
n
tr
o
l
.
2
0
1
3
;
8
(
2
):
1
9
3
-
2
0
3
.
[5
2
]
Ko
h
li
S
S
,
M
a
k
w
a
n
a
N,
M
ish
ra
N,
S
a
g
a
r
B.
Hilb
e
rt
.
“
T
ra
n
sf
o
r
m
Ba
se
d
A
d
a
p
ti
v
e
ECG
R
-
P
e
a
k
De
tec
t
io
n
T
e
c
h
n
iq
u
e
”
.
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
.
2
0
1
2
;
2
(5
):
6
3
9
-
6
4
3
.
[5
3
]
P
a
d
m
a
v
a
th
i
K,
Ra
m
a
k
rish
n
a
KS.
“
De
tec
ti
o
n
o
f
A
tri
a
l
F
ib
ril
latio
n
u
sin
g
A
u
to
re
g
re
ss
iv
e
m
o
d
e
ll
in
g
”
.
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
t
e
r E
n
g
i
n
e
e
rin
g
(
IJ
ECE
)
.
2
0
1
5
;
5
(
1
):
6
4
-
7
0
.
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