I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
,
p
p
.
8
2
9
~
8
3
8
I
SS
N:
2
5
02
-
4
7
5
2
,
DOI
: 1
0
.
1
1
5
9
1
/i
j
ee
cs.v
2
1
.i
2
.
p
p
8
2
9
-
8
3
8
829
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
ee
cs.ia
esco
r
e.
co
m
Rev
ea
ling
and ev
a
lua
ting the
in
flu
ence
o
f
filt
ers po
s
ition in
ca
sca
ded f
ilt
er:
a
pplica
tion o
n t
he
ECG
d
e
-
no
ising
p
erfor
m
a
nce
dispa
rity
Abdeno
ur
Al
la
li
1
,
Arr
es B
a
rt
il
2
,
L
a
hcene
Z
iet
3
,
A
m
a
r
H
e
bib
i
4
1,
3,
4
De
p
a
rtm
e
n
t
o
f
El
e
c
tro
n
ics
E
n
g
in
e
e
rin
g
,
F
a
c
u
lt
y
o
f
T
e
c
h
n
o
lo
g
y
,
F
e
rh
a
t
A
b
b
a
s Un
iv
e
rsity
,
S
e
ti
f
,
A
l
g
e
ria
2
L
a
b
o
ra
to
ry
o
f
S
c
ien
ti
f
i
c
I
n
stru
m
e
n
tatio
n
,
De
p
a
rtm
e
n
t
o
f
El
e
c
tro
n
ic
s,
F
a
c
u
lt
y
o
f
Tec
h
n
o
lo
g
y
,
F
e
rh
a
t
A
b
b
a
s Un
iv
e
rsit
y
,
S
e
ti
f
,
A
l
g
e
ria
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
1
5
,
2
0
2
0
R
ev
i
s
ed
A
u
g
7
,
2
0
2
0
A
cc
ep
ted
A
u
g
2
1
,
2
0
2
0
In
th
is
p
a
p
e
r,
a
n
e
w
o
p
ti
m
iza
ti
o
n
o
n
w
in
d
o
w
in
g
tec
h
n
iq
u
e
b
a
se
d
o
n
f
in
it
e
im
p
u
lse
re
sp
o
n
se
(F
IR)
f
il
ters
is
p
ro
p
o
se
d
f
o
r
r
e
v
e
a
li
n
g
a
n
d
e
v
a
lu
a
ti
n
g
t
h
e
In
f
lu
e
n
c
e
o
f
f
il
ters
p
o
siti
o
n
in
c
a
sc
a
d
e
d
f
il
ter
tes
ted
o
n
th
e
ECG
sig
n
a
l
d
e
-
n
o
isi
n
g
.
b
a
se
li
n
e
w
a
n
d
e
r
(BL
W
)
,
p
o
w
e
r
li
n
e
in
terf
e
re
n
c
e
(P
L
I)
a
n
d
e
lec
tro
m
y
o
g
ra
p
h
y
(EM
G
)
n
o
ise
s
a
re
g
e
tt
in
g
re
m
o
v
e
d
.
T
h
e
p
e
rf
o
r
m
a
n
c
e
o
f
th
e
a
d
o
p
te
d
m
e
th
o
d
is
e
v
a
lu
a
ted
o
n
th
e
P
T
B
d
iag
n
o
stic
d
a
tab
a
se
.
S
u
b
se
q
u
e
n
tl
y
,
th
e
c
o
m
p
a
riso
n
s
a
re
b
a
se
d
o
n
si
g
n
a
l
to
n
o
ise
ra
ti
o
(S
NR)
im
p
ro
v
e
m
e
n
t
a
n
d
m
e
a
n
sq
u
a
re
e
rro
r
(M
S
E)
m
in
imiz
a
ti
o
n
.
W
h
e
re
th
e
Re
c
tan
g
u
lar,
a
n
d
Ka
ise
r
w
in
d
o
w
s
h
a
v
e
b
e
e
n
u
se
d
f
o
r
th
e
m
o
re
p
o
ten
t
p
e
rf
o
r
m
a
n
c
e
s.
T
h
e
d
isp
a
rit
y
a
v
e
ra
g
e
(D
A
)
o
f
S
NR
v
a
lu
e
s
is
d
e
tec
ted
;
in
b
o
th
Ka
ise
r
a
n
d
R
e
c
tan
g
u
lar
w
in
d
o
w
s
a
re
a
ss
e
s
se
d
b
y
±
0
.
3
8
0
4
6
d
B
a
n
d
±
0
.
7
0
2
7
8
d
B
re
sp
e
c
ti
v
e
l
y
,
w
h
il
e
th
e
M
S
E
v
a
lu
e
s
w
e
re
c
o
n
sta
n
t.
T
h
e
e
x
c
e
ll
e
n
t
c
o
n
f
ig
u
ra
ti
o
n
o
r
f
il
ters
p
o
siti
o
n
(H
-
B
-
L
)
o
f
th
e
f
il
tratio
n
sy
ste
m
i
s
se
lec
te
d
a
c
c
o
rd
in
g
to
h
ig
h
m
e
a
su
re
m
e
n
ts
o
f
S
NR
a
n
d
lo
w
M
S
E
to
o
,
t
o
d
e
-
n
o
ise
th
e
ECG
sig
n
a
ls.
F
irst
o
f
a
ll
,
th
is
a
p
p
li
e
d
a
p
p
r
o
a
c
h
h
a
s
led
t
o
3
1
.
3
0
d
B
S
NR
im
p
ro
v
e
m
e
n
t
w
it
h
M
S
E
m
in
i
m
iza
ti
o
n
o
f
2
6
.
4
3
%
.
T
h
is
m
e
a
n
s
th
a
t
th
e
re
is
a
si
g
n
if
ica
n
t
c
o
n
tri
b
u
ti
o
n
to
im
p
ro
v
in
g
th
e
f
ield
o
f
f
il
tratio
n
.
K
ey
w
o
r
d
s
:
C
ascad
e
d
FIR
f
ilter
Diag
n
o
s
tic
d
is
ea
s
e
Dis
p
ar
it
y
E
C
G
MSE
No
is
e
SNR
T
h
is
is
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
:
A
b
d
en
o
u
r
Allali
Dep
ar
t
m
en
t o
f
E
lectr
o
n
ic
E
n
g
in
ee
r
in
g
Facu
lt
y
o
f
T
ec
h
n
o
lo
g
y
Un
i
v
er
s
it
y
Fer
h
a
t
A
b
b
as
Setif
-
1
-
,
1
9
0
0
0
,
A
lg
er
ia
E
m
ail: a
b
d
en
o
u
r
alla
li@
h
o
t
m
a
il.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
E
tio
lo
g
y
o
f
e
lectr
o
ca
r
d
io
g
r
am
(
E
C
G)
s
ig
n
al
a
llo
w
s
p
h
y
s
icia
n
s
to
u
n
d
er
s
tan
d
p
h
y
s
ical
a
n
d
p
ath
o
lo
g
ical
co
n
d
it
io
n
s
o
f
t
h
e
E
C
G
d
iag
n
o
s
is
.
Gen
er
all
y
,
th
e
n
o
is
e
s
ca
n
h
id
e
an
d
co
r
r
u
p
t
i
m
p
o
r
tan
t
in
f
o
r
m
atio
n
f
r
o
m
t
h
e
b
e
g
in
n
i
n
g
o
f
h
ea
r
tb
ea
t
m
o
n
i
to
r
in
g
r
e
co
r
d
s
.
So
m
e
f
r
eq
u
en
c
y
n
o
is
e
s
h
a
v
e
e
x
is
ted
in
th
e
f
r
eq
u
en
c
y
b
a
n
d
o
f
t
h
e
E
C
G
s
i
g
n
al,
an
d
th
e
E
C
G
s
ig
n
al
w
ill
o
f
te
n
g
et
d
i
s
to
r
ted
,
w
h
ic
h
li
m
it
t
h
e
e
x
tr
ac
tio
n
o
f
u
s
e
f
u
l
in
f
o
r
m
atio
n
f
r
o
m
it.
T
h
e
in
s
tr
u
m
e
n
tatio
n
n
o
is
e
r
ef
er
r
ed
to
th
e
n
o
is
e
o
r
ig
in
a
ted
in
th
e
d
ata
co
llectio
n
d
ev
ice,
th
e
elec
tr
o
n
ic
n
o
is
e
w
h
ic
h
is
a
s
p
ec
i
f
ic
k
i
n
d
o
f
t
h
e
in
s
tr
u
m
e
n
tatio
n
n
o
is
e.
T
h
is
k
i
n
d
o
f
n
o
is
e
i
s
r
ef
er
r
ed
to
as
f
lick
er
n
o
is
e
wh
ich
o
v
er
lap
s
i
n
th
e
f
r
eq
u
e
n
c
y
d
o
m
a
in
w
it
h
elec
tr
o
m
y
o
g
r
a
p
h
y
(
E
MG
)
n
o
is
e.
T
h
er
ef
o
r
e,
f
ilter
in
g
t
h
e
E
M
G
n
o
is
e
w
i
ll,
i
n
t
u
r
n
,
r
ed
u
ce
th
e
s
e
f
lick
er
s
[
1
]
.
Oth
er
n
o
is
e
s
o
u
r
ce
s
a
f
f
ec
ti
n
g
th
e
E
C
G
s
i
g
n
a
l s
u
ch
as c
h
a
n
n
el
n
o
is
e,
elec
tr
o
d
e
co
n
tact
n
o
is
e,
m
o
tio
n
ar
te
f
ac
ts
’
….
etc.
T
h
e
m
ai
n
n
o
i
s
es
ad
d
r
ess
ed
i
n
th
i
s
p
ap
er
ar
e
class
if
ied
in
to
th
r
ee
m
ai
n
t
y
p
es:
elec
tr
o
m
y
o
g
r
ap
h
y
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
lecE
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
8
2
9
-
838
830
n
o
is
e,
p
o
w
er
lin
e
i
n
ter
f
er
e
n
ce
(
P
L
I
)
an
d
B
aselin
e
W
an
d
er
(
B
L
W
)
,
w
h
ic
h
th
e
m
s
elv
e
s
o
cc
u
p
y
th
r
ee
f
r
eq
u
en
c
y
b
an
d
s
: h
i
g
h
,
m
ed
i
u
m
a
n
d
lo
w
f
r
eq
u
en
c
y
r
esp
ec
ti
v
el
y
:
a)
T
h
e
elec
tr
o
m
y
o
g
r
ap
h
y
(
E
MG
)
n
o
is
e;
w
h
ic
h
e
m
er
g
es
b
ec
a
u
s
e
o
f
t
h
e
co
n
tr
ac
tio
n
o
f
m
u
s
c
les
o
th
er
t
h
an
ca
r
d
iac
m
u
s
cle
s
[
2
]
an
d
is
as
s
u
m
ed
to
b
e
tr
an
s
ie
n
t
b
u
r
s
t
s
o
f
ze
r
o
m
ea
n
b
an
d
s
l
i
m
ited
Gau
s
s
ia
n
No
is
e
[
3
]
.
I
t
is
o
v
er
lap
p
ed
w
it
h
t
h
e
E
C
G
s
ig
n
al
i
n
t
h
e
m
o
m
en
t
o
f
h
ea
r
t
elec
tr
ical
ac
tiv
i
t
y
r
ec
o
r
d
in
g
,
in
c
lu
d
i
n
g
th
e
a
m
p
l
itu
d
e
o
f
t
h
i
s
k
i
n
d
o
f
n
o
is
e;
it
i
s
r
an
d
o
m
a
n
d
co
u
ld
b
e
r
ea
s
o
n
ab
l
y
ap
p
r
o
x
i
m
ated
b
y
a
Gau
s
s
ia
n
f
u
n
ctio
n
i
n
t
h
e
r
a
n
g
e
o
f
0
to
1
0
0
m
V.
Hen
ce
th
e
E
C
G
s
i
g
n
al
’
s
a
m
p
lit
u
d
e
r
an
g
es
f
r
o
m
0
.
1
to
5
m
V.
T
h
er
ef
o
r
e,
E
MG
n
o
is
e
a
n
d
E
C
G
s
ig
n
al
s
p
ar
ticip
ate
in
t
h
e
f
r
eq
u
en
c
y
s
p
ec
tr
u
m
w
i
th
s
i
g
n
if
ica
n
t
p
ar
ts
o
f
en
er
g
y
[
1
]
.
So
,
th
e
E
MG
n
o
is
e
ca
n
b
e
r
em
o
v
ed
b
y
u
s
in
g
a
lo
w
p
as
s
f
il
ter
(
L
P
F).
b)
T
h
e
p
o
w
er
lin
e
in
ter
f
er
en
ce
(
P
L
I
)
;
m
o
s
tl
y
h
ap
p
en
ed
d
u
e
to
u
n
s
u
itab
le
g
r
o
u
n
d
in
g
o
f
th
e
E
C
G
d
ev
ice.
T
h
is
latter
af
f
ec
t
s
th
e
q
u
ali
t
y
a
n
d
d
etailed
f
ea
tu
r
es
o
f
t
h
e
s
i
g
n
al
w
h
ic
h
ca
n
b
e
cr
itical
f
o
r
s
ig
n
a
l
p
r
o
ce
s
s
in
g
b
ec
au
s
e
th
e
s
e
f
ea
t
u
r
es
ar
e
r
ich
s
o
u
r
ce
s
o
f
i
n
f
o
r
m
atio
n
.
I
t
o
p
er
ates
i
n
m
ed
i
u
m
f
r
eq
u
en
c
y
,
i.e
.
(
5
0
Hz
/
6
0
Hz)
.
T
h
is
n
o
is
e
ca
n
b
e
s
u
p
p
r
ess
ed
b
y
a
b
an
d
s
t
o
p
f
ilter
(
B
SF
)
.
c)
T
h
e
last
n
o
is
e
is
B
aselin
e
wan
d
er
s
(
B
L
W
)
;
b
o
d
y
ac
tio
n
s
,
r
esp
ir
atio
n
,
s
w
ea
t,
an
d
i
m
p
r
o
p
er
elec
tr
o
d
e
co
n
n
ec
tio
n
s
ar
e
th
e
m
ain
s
o
u
r
ce
s
o
f
t
h
is
n
o
is
e.
A
cc
o
r
d
in
g
to
N
y
q
u
i
s
t
’
s
r
u
le,
it
s
f
r
eq
u
en
c
y
r
an
g
e
i
s
u
s
u
all
y
b
et
w
ee
n
(
0
.
1
Hz
-
0
.
5
Hz)
,
its
lo
w
f
r
eq
u
en
c
y
,
ca
n
b
e
eli
m
i
n
ated
u
s
in
g
h
ig
h
p
ass
f
ilter
(
HP
F).
As
E
C
G
a
n
d
s
o
m
e
n
o
is
es
s
h
ar
e
th
e
s
a
m
e
f
r
eq
u
en
c
y
,
t
h
e
b
est
d
e
-
n
o
is
i
n
g
tec
h
n
iq
u
e
i
s
t
h
e
o
n
e
th
a
t
p
r
o
v
id
es
th
e
b
est
tr
ad
e
-
o
f
f
i
n
ter
m
s
o
f
m
i
n
i
m
al
w
asta
g
e
o
f
in
f
o
r
m
atio
n
an
d
i
n
ter
esti
n
g
le
v
el
o
f
n
o
i
s
e
eli
m
i
n
atio
n
[
4
]
.
I
n
th
e
co
n
te
x
t
o
f
th
i
s
is
s
u
e,
s
tu
d
ies
co
v
er
in
g
E
C
G
s
ig
n
al
s
ar
e
lar
g
el
y
lis
t
ed
in
v
ar
io
u
s
s
tate
s
o
f
ar
t,
s
p
ec
if
ic
liter
atu
r
e
an
d
in
s
ev
er
al
m
eth
o
d
o
lo
g
ies
o
f
d
e
-
n
o
i
s
in
g
E
C
G
s
i
g
n
als.
A
lo
t
o
f
alg
o
r
ith
m
s
h
av
e
b
ee
n
p
r
o
p
o
s
ed
f
o
r
E
C
G
s
i
g
n
a
l
d
e
-
n
o
is
i
n
g
.
So
m
e
o
f
t
h
e
m
ar
e
d
er
iv
ed
f
r
o
m
u
s
in
g
Sav
itzk
y
-
Go
la
y
f
ilter
f
o
r
t
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
s
tag
e
s
u
c
h
as
i
n
[
5
]
,
d
is
cr
ete
w
a
v
elet
tr
an
s
f
o
r
m
s
(
DW
T
)
in
[
6
-
8
]
,
ad
ap
tiv
e
f
il
ter
i
n
[
3
,
9
-
1
5
]
,
an
d
d
ig
i
tal
f
ilter
in
[
2
,
1
6
-
2
0
]
.
W
h
er
e,
in
[
2
1
]
,
Ge
W
an
g
et
al.
p
r
o
p
o
s
ed
a
n
o
v
el
E
C
G
s
i
g
n
al
d
e
-
n
o
is
i
n
g
alg
o
r
ith
m
b
ased
o
n
th
e
d
ee
p
f
ac
to
r
an
al
y
s
is
f
o
r
eli
m
i
n
ati
n
g
a
Ga
u
s
s
ian
-
d
is
tr
ib
u
tio
n
n
o
is
e
s
ig
n
al.
I
n
[
2
2
]
,
s
y
s
te
m
a
tic
r
ev
ie
w
s
o
f
d
ee
p
le
ar
n
in
g
(
d
ee
p
n
e
u
r
al
n
et
w
o
r
k
)
m
et
h
o
d
s
h
a
v
e
b
ee
n
u
s
ed
i
n
v
a
r
io
u
s
E
C
G
a
n
al
y
tics
task
s
ar
e
p
r
esen
ted
,
b
y
an
al
y
zin
g
th
e
p
ap
er
s
th
at
w
er
e
p
u
b
lis
h
ed
s
i
n
ce
1
0
y
ea
r
s
ag
o
.
A
.
k
.
Ver
m
a
et
al.
p
r
o
p
o
s
ed
th
e
ale
x
a
n
d
er
f
r
ac
tio
n
al
d
i
f
f
er
en
tial
w
i
n
d
o
w
(
A
FD
W
)
f
ilter
f
o
r
E
C
G
s
ig
n
al
d
e
-
n
o
is
in
g
a
n
d
ac
h
iev
ed
b
etter
n
o
is
e
r
ed
u
ctio
n
r
esu
lts
[
2
3
]
.
T
o
s
u
m
u
p
,
it
ca
n
b
e
s
aid
th
at
ea
ch
o
f
t
h
ese
al
g
o
r
ith
m
s
o
r
tech
n
iq
u
e
s
f
o
cu
s
o
n
d
eletin
g
u
n
w
a
n
ted
s
i
g
n
a
ls
an
d
i
m
p
r
o
v
i
n
g
th
e
E
C
G
s
i
g
n
a
l
q
u
alit
y
.
He
n
ce
,
f
ilter
i
n
g
i
s
th
e
f
ir
s
t
s
tep
in
ter
m
s
o
f
th
e
E
C
G
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
,
i.e
.
n
o
s
tep
ca
n
b
e
in
itiated
b
ef
o
r
e
p
ass
in
g
t
h
r
o
u
g
h
t
h
i
s
s
ta
g
e.
T
o
b
e
co
n
s
is
te
n
t
w
it
h
d
is
cu
s
s
io
n
s
ab
o
u
t
t
h
e
s
u
m
m
ar
y
o
f
r
elev
an
t
w
o
r
k
s
,
an
d
ad
d
r
ess
in
g
o
f
t
h
e
ca
s
ca
d
ed
d
ig
ital
FIR
f
i
lter
is
g
iv
e
n
f
o
r
eli
m
i
n
ati
n
g
m
u
lti
-
le
v
els
o
f
n
o
is
e
s
,
as
p
r
esen
ted
in
th
e
[
2
]
,
a
ca
s
ca
d
ed
th
r
ee
s
et
s
o
f
t
h
e
Kai
s
er
-
w
in
d
o
w
f
u
n
ctio
n
b
ased
FIR
f
ilter
s
w
er
e
d
es
ig
n
ed
f
o
r
s
u
p
p
r
ess
in
g
th
e
B
L
W
,
5
0
/6
0
Hz
an
d
E
MG
n
o
is
e
s
f
r
o
m
a
n
o
i
s
y
E
C
G
s
i
g
n
al.
Hen
ce
,
d
i
f
f
e
r
en
t
E
C
G
s
ig
n
al
s
f
r
o
m
MI
T
-
B
I
H
n
o
r
m
a
l
s
i
n
u
s
r
h
y
t
h
m
(
NS
R
)
,
E
C
G
I
D
d
atab
ases
ar
e
co
n
s
id
er
ed
f
o
r
t
h
e
s
i
m
u
latio
n
.
T
h
e
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es
ar
e
r
elate
d
to
SNR
,
M
SE
a
n
d
p
o
w
er
s
p
ec
tr
al
d
en
s
i
t
y
(
P
SD)
.
W
h
er
e,
in
[
1
9
]
,
b
y
co
n
s
id
er
in
g
t
h
e
b
e
s
t
SNR
r
e
s
u
lted
f
r
o
m
d
if
f
er
e
n
t
w
i
n
d
o
w
i
n
g
tech
n
iq
u
es,
ca
s
ca
d
ed
FIR
f
ilter
s
h
a
v
e
b
ee
n
ca
r
r
ied
o
u
t
as
FIR
L
P
F
h
a
m
m
i
n
g
,
FIR
HP
F
R
ec
tan
g
u
lar
a
n
d
FIR
No
tch
R
ec
tan
g
u
lar
co
m
b
i
n
atio
n
f
o
r
r
e
m
o
v
i
n
g
t
h
e
s
a
m
e
n
o
is
e
s
f
r
o
m
E
C
G
s
ig
n
al.
T
h
e
E
C
G
s
a
m
p
les
h
a
v
e
b
ee
n
e
x
tr
ac
ted
f
r
o
m
t
h
e
MI
T
-
B
I
H
d
atab
ase.
T
h
e
au
th
o
r
s
i
n
[
2
4
]
p
r
o
p
o
s
ed
f
o
u
r
co
m
b
i
n
atio
n
s
o
f
ca
s
ca
d
ed
f
il
te
r
s
w
er
e
u
s
ed
f
o
r
r
e
m
o
v
i
n
g
t
h
e
u
n
d
esire
d
f
r
eq
u
e
n
cie
s
f
r
o
m
a
n
o
is
y
E
C
G
s
i
g
n
al.
W
h
er
e
FIR
HP
F
w
a
s
d
esi
g
n
e
d
b
y
B
lac
k
m
a
n
w
i
n
d
o
w
,
A
d
ap
tiv
e
Fil
ter
w
as
d
esi
g
n
ed
b
y
NL
M
S
alg
o
r
it
h
m
,
No
tch
F
ilter
(
5
0
Hz)
an
d
lo
w
p
ass
I
I
R
f
il
ter
w
as
d
esi
g
n
ed
b
y
t
h
e
E
llip
tic
ap
p
r
o
x
i
m
atio
n
m
et
h
o
d
.
T
h
e
E
C
G
s
a
m
p
les
h
a
v
e
b
ee
n
ac
ce
s
s
ed
f
r
o
m
t
h
e
MI
T
-
B
I
H
A
r
r
h
y
t
h
m
i
a
Data
b
ase.
T
h
e
h
ig
h
p
er
f
o
r
m
an
ce
r
esu
lted
f
r
o
m
SNR
a
n
d
P
SD p
ar
am
eter
s
h
a
v
e
b
ee
n
co
m
p
ar
ed
w
it
h
th
e
r
es
u
lts
o
b
tain
ed
in
[
2
]
,
as sh
o
w
n
i
n
th
e
T
ab
le
3
.
As
b
r
u
t
E
C
G
s
i
g
n
al
co
n
ta
m
i
n
atin
g
b
y
m
u
lti
-
le
v
els
o
f
n
o
i
s
e
s
,
a
ca
s
ca
d
ed
f
ilter
ca
n
r
e
m
o
v
e
d
if
f
er
e
n
t
n
o
is
es
d
ep
en
d
in
g
o
n
d
esire
d
f
r
eq
u
en
cie
s
,
w
h
ic
h
in
v
o
lv
e
s
m
a
n
y
s
tep
s
s
u
ch
as
s
h
o
w
n
in
th
e
F
ig
u
r
e
1
.
T
h
er
ef
o
r
e,
an
FIR
f
ilter
ca
n
b
e
d
esig
n
ed
w
it
h
d
i
f
f
er
en
t
w
i
n
d
o
w
in
g
m
et
h
o
d
s
.
B
u
t
th
er
e
is
an
i
m
p
o
r
tan
t
r
e
m
ar
k
to
th
e
p
o
in
t
w
h
ic
h
co
n
ce
r
n
s
t
h
e
f
ac
t
t
h
at
t
h
e
ar
r
an
g
e
m
e
n
t
o
f
f
ilter
s
p
o
s
it
io
n
h
as
n
o
t
b
ee
n
c
o
v
er
ed
in
th
e
ab
o
v
e
m
en
tio
n
ed
al
g
o
r
ith
m
s
,
t
h
is
p
r
o
b
lem
t
h
at
w
e
ch
ec
k
e
d
a
n
d
an
al
y
ze
d
i
n
t
h
i
s
s
tu
d
y
t
h
r
o
u
g
h
th
e
f
e
w
p
ap
er
s
o
f
th
i
s
m
an
u
s
cr
ip
t.
Ho
w
e
v
er
,
t
h
e
f
o
llo
w
i
n
g
s
ec
t
io
n
s
w
ill
b
e
s
h
o
w
i
n
g
th
e
d
etail
o
f
th
e
id
ea
o
f
r
ev
ea
li
n
g
a
n
d
ev
alu
a
tin
g
th
e
I
n
f
l
u
e
n
ce
o
f
f
il
ter
s
p
o
s
itio
n
in
th
e
ca
s
ca
d
ed
f
ilter
,
in
w
h
ic
h
th
at
'
s
te
s
ted
o
n
t
h
e
E
C
G
s
i
g
n
a
l
d
e
-
n
o
is
i
n
g
w
it
h
t
h
r
ee
le
v
els
o
f
c
o
u
r
an
t
n
o
is
e
s
.
T
h
e
u
s
e
o
f
th
is
is
s
u
e
s
h
o
u
ld
n
o
t
b
e
r
an
d
o
m
d
u
e
to
its
i
m
p
ac
t
o
n
th
e
q
u
alit
y
o
f
t
h
e
r
es
u
lti
n
g
s
i
g
n
al.
As
a
r
eso
lu
tio
n
,
th
e
f
ilt
er
s
’
ar
r
an
g
e
m
e
n
t
i
n
th
e
ca
s
ca
d
ed
f
ilter
h
as
b
ee
n
af
f
ec
ted
b
y
t
h
e
p
ar
a
m
eter
s
a
n
d
th
e
co
m
b
i
n
atio
n
s
o
f
th
e
f
iltr
atio
n
s
y
s
te
m
s
i.e
.
t
h
e
f
ilter
s
a
r
r
an
g
e
m
en
t
p
la
y
s
a
v
ital
r
o
le
s
ig
n
i
f
ica
n
tl
y
i
m
p
r
o
v
in
g
t
h
e
ca
s
ca
d
ed
f
ilter
p
er
f
o
r
m
an
ce
i
n
t
h
e
tas
k
o
f
s
i
g
n
a
ls
f
iltra
tio
n
.
T
h
is
latter
id
ea
co
n
s
tit
u
te
s
t
h
e
o
r
ig
i
n
ali
t
y
in
th
i
s
co
n
tr
ib
u
t
io
n
.
So
,
t
h
is
s
tu
d
y
a
i
m
ed
to
ch
ar
ac
ter
ize
ea
c
h
t
y
p
e
o
f
w
i
n
d
o
w
s
(
ad
j
u
s
tab
le
o
r
f
ix
ed
w
in
d
o
w
)
s
u
c
h
a
s
Kai
s
er
an
d
R
ec
ta
n
g
u
l
ar
w
i
n
d
o
w
s
o
f
v
ar
io
u
s
SN
R
l
ev
els
(
0
to
1
0
d
B
)
an
d
to
ass
ess
t
h
e
e
f
f
ec
t
s
o
f
th
e
f
il
ter
p
o
s
itio
n
ar
r
an
g
e
m
en
t
o
n
v
ar
io
u
s
E
C
G
s
i
g
n
al
s
.
Nev
er
th
ele
s
s
,
t
h
is
co
n
tr
ib
u
tio
n
p
r
o
v
id
ed
i
m
p
o
r
ta
n
t e
f
f
ec
ti
v
e
n
es
s
an
d
s
u
p
er
b
p
o
ten
tial
i
n
t
h
e
d
esig
n
o
f
t
h
e
f
ilte
r
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lecE
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
ev
ea
lin
g
a
n
d
ev
a
l
u
a
tin
g
t
h
e
I
n
flu
en
ce
o
f filter
s
p
o
s
itio
n
in
c
a
s
ca
d
ed
filt
er
....
(
A
b
d
en
o
u
r
A
l
la
li)
831
Fig
u
r
e
1
.
Dif
f
er
en
t t
y
p
e
s
o
f
n
o
is
es c
o
m
p
atib
le
w
it
h
v
ar
io
u
s
f
r
eq
u
en
c
y
b
a
n
d
s
2.
M
E
T
H
O
D
O
L
O
G
Y
Fin
ite
i
m
p
u
l
s
e
r
esp
o
n
s
e
(
FI
R
)
f
ilter
s
ar
e
t
h
e
f
o
r
e
m
o
s
t
b
as
i
c
d
ig
ital
s
i
g
n
al
p
r
o
ce
s
s
in
g
s
y
s
te
m
p
ar
ts
.
I
t’
s
,
at
a
n
y
r
ate,
th
e
f
r
eq
u
e
n
c
y
w
it
h
a
s
tr
ictl
y
li
n
ea
r
p
h
ase.
T
h
er
e
is
n
o
i
n
p
u
t
to
o
u
tp
u
t
f
ee
d
b
ac
k
t
h
at
co
u
ld
b
e
a
s
tab
le
s
y
s
te
m
.
O
n
th
e
o
th
er
h
a
n
d
,
th
e
c
h
ar
ac
ter
is
tic
s
o
f
d
is
tr
i
b
u
ted
ar
ith
m
et
ic
(
DA
)
alg
o
r
it
h
m
ar
e
p
r
ef
er
r
ed
as
a
r
esu
lt
o
f
g
r
ea
tl
y
s
ca
led
b
ac
k
h
ar
d
w
ar
e
s
ize
u
tili
za
t
io
n
w
h
i
ch
en
d
s
u
p
i
n
to
h
ig
h
s
p
ee
d
ex
ec
u
tio
n
[
1
7
]
.
Fin
ite
i
m
p
u
l
s
e
r
esp
o
n
s
e
f
ilter
s
ar
e
a
ls
o
r
ec
o
g
n
ized
as
n
o
n
-
r
ec
u
r
s
i
v
e
d
ig
i
tal
f
i
lter
s
;
th
e
s
e
f
ilter
s
ar
e
o
f
ten
u
s
ed
i
n
d
ig
ital
s
i
g
n
al
p
r
o
ce
s
s
i
n
g
o
w
i
n
g
to
its
f
lex
ib
ili
t
y
.
Ho
w
ev
er
,
t
h
er
e
ar
e
t
h
r
ee
m
ai
n
m
et
h
o
d
s
f
o
r
FIR
f
ilter
d
e
s
ig
n
n
a
m
e
l
y
:
a)
Op
ti
m
al
f
ilter
d
esi
g
n
m
et
h
o
d
.
b)
T
h
e
f
r
eq
u
en
c
y
s
a
m
p
l
in
g
tec
h
n
iq
u
e.
c)
T
h
e
w
in
d
o
w
i
n
g
m
et
h
o
d
.
T
h
e
FIR
f
i
lter
ca
n
b
e
d
esig
n
ed
b
y
d
if
f
er
en
t
w
i
n
d
o
w
i
n
g
m
et
h
o
d
.
W
h
er
e,
th
er
e
ar
e
t
w
o
w
i
n
d
o
w
k
i
n
d
s
,
n
a
m
e
l
y
:
f
ix
ed
a
n
d
ad
j
u
s
tab
le
w
i
n
d
o
w
s
.
T
h
er
e
ar
e
m
a
n
y
o
th
er
m
et
h
o
d
s
u
s
ed
f
o
r
d
esi
g
n
in
g
FI
R
f
ilter
s
u
c
h
a
s
E
q
u
ir
ip
p
le,
least
s
q
u
ar
e,
m
ax
i
m
all
y
f
lat
in
s
tead
o
f
th
e
w
i
n
d
o
w
i
n
g
m
et
h
o
d
,
r
eg
ar
d
in
g
t
h
e
E
C
G
d
e
-
n
o
is
i
n
g
p
r
o
b
lem
[
1
6
]
,
m
an
y
s
t
u
d
ies
h
av
e
b
ee
n
m
ad
e
to
p
r
ep
ar
e
th
e
co
m
b
i
n
atio
n
o
f
v
ar
io
u
s
t
y
p
e
s
o
f
d
ig
ital
f
ilter
s
,
s
u
c
h
as s
h
o
w
n
i
n
t
h
e
Fi
g
u
r
e1
.
T
h
e
Fig
u
r
e
1
,
S
h
o
w
s
t
h
e
d
if
f
er
en
t
t
y
p
es
o
f
n
o
is
es
co
m
p
at
i
b
le
w
it
h
v
ar
io
u
s
ap
p
r
o
p
r
iate
f
r
eq
u
en
c
y
b
an
d
s
b
ased
o
n
d
i
g
ital
f
il
ter
t
y
p
es
r
e
s
p
ec
tiv
el
y
.
Mo
r
eo
v
e
r
,
m
o
s
t
o
f
t
h
e
p
r
ev
io
u
s
w
o
r
k
s
h
a
v
e
ad
o
p
ted
a
w
i
n
d
o
w
m
e
th
o
d
to
d
esig
n
t
h
e
ca
s
ca
d
in
g
FIR
f
il
ter
s
.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
w
led
g
e,
th
er
e
is
n
o
s
in
g
le
s
tu
d
y
th
at
p
r
o
v
es o
r
d
is
ap
p
r
o
v
es th
e
i
m
p
ac
t o
f
f
ilter
s
p
o
s
itio
n
o
n
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
ca
s
ca
d
i
n
g
f
i
lter
.
T
o
a
d
d
r
ess
th
is
is
s
u
e,
a
n
e
w
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
to
p
r
o
v
e
th
e
i
m
p
ac
t
o
f
th
e
f
i
lter
s
ar
r
an
g
e
m
en
t
w
it
h
e
m
p
lo
y
e
t
h
e
Kaiser
an
d
R
ec
ta
n
g
u
lar
w
i
n
d
o
w
d
i
s
t
r
ib
u
ted
o
n
t
h
e
ca
s
ca
d
in
g
FI
R
f
il
ter
,
i.
e.
s
elec
t
th
e
ac
cu
r
ac
y
co
n
f
i
g
u
r
atio
n
t
h
at
g
iv
e
s
t
h
e
b
est o
u
tp
u
t
s
p
er
f
o
r
m
an
ce
s
o
f
SNR
a
n
d
MSE
f
o
r
ea
ch
f
i
lter
p
o
s
itio
n
i
n
ea
ch
w
i
n
d
o
w
.
T
h
e
Kaiser
an
d
R
ec
tan
g
u
lar
w
i
n
d
o
w
s
s
elec
ted
f
r
o
m
FD
A
T
o
o
ls
,
s
in
ce
th
e
y
’
r
e
lar
g
el
y
u
s
ed
in
m
o
r
e
th
an
o
n
e
w
o
r
k
r
elate
d
to
t
h
e
f
i
lter
in
g
o
f
t
h
e
E
C
G
s
ig
n
al
a
n
d
th
eir
p
er
f
o
r
m
a
n
ce
as
it
w
a
s
ap
p
ea
r
in
g
i
n
[
2
]
an
d
[
2
5
]
.
T
h
is
p
r
o
ce
d
u
r
e
w
i
ll
b
e
a
p
p
ly
i
n
g
it
to
ea
c
h
t
y
p
e
o
f
d
ig
i
tal
FIR
f
ilter
s
u
c
h
a
s
s
h
o
w
n
i
n
F
ig
u
r
e
2
.
O
n
o
n
e
h
an
d
,
th
e
S
NR
a
n
d
MSE
p
er
f
o
r
m
an
ce
s
o
f
ea
ch
co
n
f
i
g
u
r
atio
n
p
r
o
d
u
ce
d
b
y
ch
a
n
g
i
n
g
th
e
f
i
lter
p
o
s
itio
n
s
,
w
h
ic
h
ar
e
u
s
ed
to
f
o
r
m
t
h
e
ca
s
ca
d
in
g
FIR
f
ilter
,
w
e
ar
e
co
m
p
ar
ed
w
it
h
t
h
e
r
esu
l
ts
o
f
R
e
ctan
g
u
lar
a
n
d
Kaiser
w
i
n
d
o
w
s
p
er
f
o
r
m
a
n
ce
s
.
Fig
u
r
e
2
.
T
h
e
k
aiser
an
d
r
ec
tan
g
u
lar
p
er
f
o
r
m
a
n
ce
s
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
lecE
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
8
2
9
-
838
832
T
h
e
ca
s
ca
d
ed
f
ilter
s
ar
e
ta
k
e
n
w
it
h
a
f
i
x
ed
o
r
d
er
o
f
(
3
6
0
)
,
b
ec
au
s
e
th
i
s
ap
p
r
o
ac
h
is
li
m
i
ted
to
s
tu
d
y
in
g
th
e
c
h
an
g
e
in
t
h
e
s
y
s
te
m
p
er
f
o
r
m
a
n
ce
b
y
ch
an
g
i
n
g
th
e
p
o
s
itio
n
s
o
f
its
co
n
s
tit
u
en
t
f
ilter
s
,
an
d
th
i
s
is
w
h
at
m
ak
e
s
th
e
o
b
s
er
v
ed
s
ta
t
io
n
ar
y
m
ea
s
u
r
e
m
e
n
ts
i
n
all
p
o
s
s
ib
le
atte
m
p
t
s
.
Ho
w
e
v
er
,
th
e
m
eth
o
d
b
ased
o
n
esti
m
ati
n
g
t
h
e
d
is
p
ar
it
y
o
f
th
e
ch
an
g
ed
v
al
u
e
in
SNR
a
n
d
MSE
p
er
f
o
r
m
an
ce
s
,
r
esu
lt
in
g
f
r
o
m
th
e
o
u
tp
u
t
o
f
ca
s
ca
d
e
f
ilter
b
lo
c
d
esig
n
,
w
il
l
b
e
p
r
o
d
u
ce
d
s
ix
p
o
s
s
ib
le
p
atter
n
s
o
f
ca
s
ca
d
e
f
ilter
,
i.e
.
it’
s
r
ep
r
esen
ted
th
e
s
i
x
ca
s
ca
d
ed
f
ilter
co
n
f
ig
u
r
atio
n
s
f
o
r
ea
ch
m
en
t
io
n
ed
w
i
n
d
o
w
as
s
h
o
w
n
i
n
t
h
e
T
ab
le
1
,
i.e
.
T
h
e
t
w
o
w
in
d
o
w
s
f
r
o
m
FD
A
T
o
o
ls
,
ar
e
test
ed
w
i
th
th
e
f
il
ter
s
,
an
d
th
e
s
ix
ca
s
ca
d
ed
f
ilter
co
n
f
i
g
u
r
atio
n
s
ar
e
o
b
tain
ed
.
A
cc
o
r
d
in
g
to
t
h
e
co
n
d
itio
n
o
f
SN
R
a
n
d
MSE
m
ea
s
u
r
e
m
en
t
s
,
th
e
d
is
p
ar
it
y
a
v
er
ag
e
(
D
A
)
is
d
etec
ted
f
r
o
m
c
h
an
g
i
n
g
v
a
lu
e
s
o
f
f
il
t
er
s
p
o
s
itio
n
p
atter
n
s
;
w
i
ll
b
e
in
v
esti
g
ati
n
g
t
h
e
b
est
f
ilter
s
p
o
s
itio
n
a
n
d
th
e
co
n
v
e
n
ien
t
w
i
n
d
o
w
,
ap
p
lied
to
ac
h
iev
e
t
h
e
tas
k
o
f
f
ilter
i
n
g
th
e
n
o
is
y
E
C
G
s
ig
n
al
s
in
d
if
f
er
en
t
ca
s
e
s
.
T
h
e
p
o
s
itio
n
o
f
th
e
th
r
ee
f
il
ter
s
t
h
a
t j
u
s
ti
f
ied
t
h
e
b
est co
n
f
ig
u
r
ati
o
n
h
ad
to
ac
h
ie
v
e
a
m
o
r
e
p
o
ten
t
i
n
t
h
e
q
u
alit
y
a
n
d
ac
cu
r
ac
y
ap
p
ea
r
an
ce
o
f
th
e
E
C
G
s
i
g
n
al.
On
t
h
e
o
th
er
h
a
n
d
,
m
o
r
e
t
h
an
1
0
r
ec
o
r
d
ed
s
am
p
les
u
s
ed
i
n
ad
v
an
ce
,
b
y
tak
i
n
g
in
to
ac
co
u
n
t
m
o
r
e
t
h
a
n
o
n
e
d
iag
n
o
s
i
s
o
f
d
if
f
er
e
n
t
h
ea
r
t
d
is
ea
s
es
[
2
6
]
.
Hen
ce
,
th
e
r
esu
lt
s
ar
e
in
s
er
ted
in
t
h
e
T
ab
le
2
in
w
h
ic
h
s
h
o
w
i
n
g
th
e
es
ti
m
atio
n
o
f
t
h
e
SN
R
an
d
MSE
o
b
tain
ed
f
r
o
m
th
e
f
i
n
al
p
h
a
s
e
o
f
eli
m
i
n
atin
g
th
e
p
r
ed
o
m
i
n
an
t
in
ter
f
er
e
n
ce
s
o
f
th
e
E
C
G
s
i
g
n
al
b
e
f
o
r
e
an
d
af
ter
f
iltra
t
io
n
.
Fig
u
r
e
3
il
lu
s
tr
ates
a
d
etailed
w
o
r
k
p
la
n
t
h
at
lis
t
s
t
h
e
s
tep
s
t
h
at
h
a
v
e
b
ee
n
id
en
ti
f
ied
to
r
ea
ch
t
h
e
d
esire
d
r
esu
lts
.
Fo
r
clar
if
icati
o
n
o
n
l
y
,
h
er
e
w
e
f
i
n
d
th
e
ter
m
m
o
v
i
n
g
f
ilter
(
ca
s
ca
d
ed
FIR
f
ilter
)
,
w
h
ich
m
ea
n
s
th
at
t
h
e
f
i
lter
s
ar
e
r
ep
o
s
itio
n
ed
at
th
e
s
a
m
e
t
i
m
e
i
n
s
id
e
t
h
e
ca
s
ca
d
ed
f
ilter
e
v
er
y
ti
m
e
f
r
o
m
m
ak
i
n
g
t
h
e
n
ec
es
s
ar
y
m
ea
s
u
r
e
m
e
n
ts
.
T
h
e
f
o
llo
w
in
g
s
tep
s
ill
u
s
tr
ate
t
h
e
a
lg
o
r
ith
m
p
r
o
ce
d
u
r
e.
Fig
u
r
e
3
.
Diag
r
a
m
o
f
th
e
p
r
o
p
o
s
ed
m
e
th
o
d
Alg
o
rit
h
m
P
r
e
-
P
r
o
ce
s
s
in
g
C
a
p
tu
r
ed
E
C
G
s
i
g
n
al
I
np
ut:
No
is
y
E
C
G
s
i
g
n
al
E
I
n
p
u
t.
O
utput
:
De
-
n
o
is
i
n
g
E
C
G
E
O
u
tp
u
t.
I
s
s
ue:
a
n
i
nv
estig
a
t
io
n
into
t
he
cha
ng
e
v
a
lue
o
f
SNR
end
M
SE
perf
o
r
m
a
nces
in
t
er
ms
o
f
cha
ng
ing
t
he
po
s
it
io
n o
f
t
he
f
ilte
rs.
1
: L
o
ad
E
I
n
p
u
t.
2
: I
n
itializatio
n
o
f
ea
ch
f
ilter
b
y
ch
o
o
s
i
n
g
o
r
d
er
s
,
cu
t
-
o
f
f
f
r
eq
u
en
c
y
an
d
s
i
m
p
le
f
r
eq
u
e
n
c
y
i
n
FD
A
T
o
o
ls
.
3
:
Fil
ter
in
g
E
I
n
p
u
t
s
i
g
n
al
b
y
u
s
i
n
g
(
Kaiser
w
in
d
o
w
,
th
e
n
R
ec
tan
g
u
lar
w
i
n
d
o
w
)
av
a
ilab
le
in
FD
A
T
o
o
ls
,
an
d
ch
an
g
i
n
g
t
h
e
p
o
s
itio
n
o
f
th
e
f
i
lter
s
in
ea
c
h
ti
m
e.
4
:
C
o
m
p
u
ti
n
g
an
d
co
m
p
ar
in
g
th
e
E
Ou
tp
u
t
ac
co
r
d
in
g
to
S
NR
an
d
MSE
p
ar
a
m
eter
s
f
o
r
t
h
e
p
o
s
itio
n
o
f
ea
ch
ca
s
ca
d
ed
f
ilter
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lecE
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
ev
ea
lin
g
a
n
d
ev
a
l
u
a
tin
g
t
h
e
I
n
flu
en
ce
o
f filter
s
p
o
s
itio
n
in
c
a
s
ca
d
ed
filt
er
....
(
A
b
d
en
o
u
r
A
l
la
li)
833
5
:
Dis
p
ar
it
y
Av
er
ag
e
e
s
ti
m
ati
o
n
o
f
an
y
c
h
an
g
e
i
n
t
h
e
SN
R
o
r
MSE
v
al
u
es
a
n
d
co
m
p
a
r
in
g
t
h
e
m
f
o
r
b
o
th
w
i
n
d
o
w
s
.
6
: U
s
in
g
th
e
b
es
t p
o
s
itio
n
o
f
f
i
lter
s
an
d
th
e
b
est
w
i
n
d
o
w
to
im
p
r
o
v
in
g
t
h
e
E
C
G
s
ig
n
al
i
n
al
l c
ases
in
g
e
n
er
al.
No
r
m
a
l
an
d
ab
n
o
r
m
al
E
C
G
s
ig
n
al
s
ar
e
co
llected
f
r
o
m
th
e
P
h
y
s
io
b
an
k
d
atab
ase,
w
h
ic
h
is
f
r
ee
l
y
av
ailab
le
o
n
-
lin
e
at
[
2
6
]
,
w
h
er
e
co
u
ld
f
in
d
m
a
n
y
p
r
ev
io
u
s
l
y
r
ec
o
r
d
e
d
E
C
G
p
atter
n
s
s
u
c
h
as
a
tex
t
h
ea
d
er
f
ile,
b
in
ar
y
a
n
n
o
tated
f
ile,
a
n
d
b
in
ar
y
d
ata
s
i
g
n
al
f
i
le.
T
h
e
r
ec
o
r
d
ed
s
ig
n
al
s
h
av
e
b
ee
n
r
etr
iev
e
d
f
r
o
m
P
h
y
s
io
b
an
k
A
T
M
-
ex
p
o
r
t
s
ig
n
a
ls
as
(
m
at)
f
o
r
m
at
to
tak
en
e
x
p
er
i
m
e
n
t,
m
an
ip
u
latio
n
an
d
i
m
p
le
m
en
t
u
s
in
g
M
A
T
L
A
B
an
d
SIM
UL
I
NK
e
n
v
ir
o
n
m
en
ts
.
As
a
p
r
o
o
f
o
f
co
n
ce
p
t
o
f
th
is
s
tu
d
y
,
t
h
e
f
o
u
r
f
o
llo
w
i
n
g
s
u
b
s
ec
tio
n
s
i
n
cl
u
d
ed
in
co
m
p
let
in
g
t
h
e
r
esu
l
ts
o
f
t
h
e
e
x
p
er
i
m
e
n
tatio
n
s
.
2
.
1
.
E
v
a
lua
t
i
o
n o
f
P
T
B
dia
g
no
s
is
E
CG
da
t
a
ba
s
e
W
ith
M
A
T
L
A
B
,
SIM
U
L
I
NK
v
er
s
io
n
8
.
2
.
0
.
7
0
1
(
2
0
1
3
b
)
6
4
b
it
f
o
r
w
in
d
o
w
s
7
,
o
p
er
atin
g
s
y
s
te
m
O
S
6
4
b
its
;
th
e
p
r
o
j
ec
t
h
as
b
ee
n
ac
h
iev
ed
b
y
r
etr
iev
i
n
g
th
e
r
a
w
E
C
G
s
ig
n
al
f
r
o
m
a
p
h
y
s
io
b
an
k
(
P
T
B
d
iag
n
o
s
is
E
C
G
d
atab
ase)
.
T
h
e
r
ec
o
r
d
s
w
er
e
d
ig
itized
at
1
0
0
0
s
am
p
l
es
p
er
s
ec
o
n
d
p
er
ch
an
n
el
w
it
h
1
6
b
its
r
eso
lu
tio
n
(
1
4
b
its
f
o
r
E
C
G
s
,
0
1
b
it
f
o
r
r
esp
ir
atio
n
e
f
f
ec
t
an
d
0
1
b
it
f
o
r
lin
e
v
o
lta
g
e
e
f
f
ec
t)
o
v
er
±
1
6
m
V
r
a
n
g
ed
o
f
(
0
to
6
5
5
3
5
)
[
2
6
]
,
i.e
.
3
2
7
6
8
w
h
ic
h
is
t
h
e
m
id
p
o
in
t
o
f
r
eso
l
u
tio
n
th
at
i
s
w
o
r
th
0
m
V.
T
h
e
E
C
G
s
a
m
p
le
s
d
ata
f
i
le
f
r
o
m
t
h
e
P
T
B
d
atab
ase
is
ex
tr
ac
ted
an
d
co
n
s
id
er
ed
as
o
r
ig
in
al
E
C
G
s
ig
n
al
w
it
h
lo
w
an
d
Me
d
iu
m
f
r
eq
u
e
n
c
y
n
o
is
es.
E
x
p
er
i
m
e
n
tall
y
t
h
e
s
a
m
e
s
a
m
p
les
d
ata
e
m
p
lo
y
ee
s
f
o
r
id
en
tify
i
n
g
th
e
p
er
f
o
r
m
a
n
c
e
lev
el
s
o
f
p
r
o
p
o
s
ed
w
o
r
k
,
s
u
c
h
as s
h
o
w
n
i
n
T
ab
le
2.
2
.
2
.
SNR
a
nd
M
SE
pa
ra
m
et
er
s
E
C
G
Si
g
n
al
d
e
-
n
o
is
i
n
g
ap
p
r
o
ac
h
es
ar
e
u
s
u
all
y
es
ti
m
ated
b
y
t
h
e
s
i
g
n
al
to
n
o
is
e
r
atio
(
S
NR
)
o
n
d
B
an
d
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MSE
)
p
ar
am
eter
s
.
F
u
r
t
h
er
m
o
r
e,
th
e
s
e
p
ar
am
e
ter
s
h
a
v
e
t
h
e
ab
ilit
y
to
k
n
o
w
h
o
w
clo
s
e
th
e
d
e
-
n
o
is
ed
s
i
g
n
a
l is i
n
th
e
o
r
ig
in
a
l si
g
n
al
a
s
s
e
s
s
m
en
t.
2
1
10
2
1
()
1
0
l
o
g
(
1
)
(
(
)
(
)
)
N
n
N
n
xn
S
N
R
y
n
x
n
(
1
)
2
1
1
(
(
)
(
)
)
(
2
)
N
n
M
S
E
y
n
x
n
N
(
2
)
As
s
h
o
w
n
in
(
2
)
an
d
(
3
)
ar
e
u
s
ed
to
ca
lcu
late
th
e
SN
R
o
n
d
B
an
d
MSE
o
f
th
e
f
ilte
r
ed
s
ig
n
a
l
r
esp
ec
tiv
el
y
,
w
h
er
e,
x
(
n
)
is
t
h
e
o
r
ig
in
al
E
C
G
i
n
p
u
t
s
i
g
n
al,
y
(
n
)
is
th
e
o
u
tp
u
t
d
e
-
n
o
i
s
ed
E
C
G
s
ig
n
al
o
f
d
ig
ita
l
f
ilter
s
,
a
n
d
N
is
t
h
e
s
a
m
p
li
n
g
p
o
in
ts
o
f
E
C
G
s
i
g
n
als
[
2
7
]
.
Hen
ce
th
e
b
etter
d
e
-
n
o
is
in
g
m
et
h
o
d
s
h
o
u
ld
h
a
v
e
a
h
ig
h
er
SN
R
an
d
a
lo
w
er
MSE
.
2
.
3
.
Dig
it
a
l F
I
R
f
ilte
r
T
h
e
r
esp
o
n
s
e
o
f
s
u
ch
a
f
il
ter
to
an
i
m
p
u
ls
e
i
s
co
m
p
o
s
ed
o
f
a
f
in
ite
s
eq
u
e
n
ce
o
f
M+
1
s
a
m
p
le,
w
h
er
e
M
is
th
e
f
il
ter
o
r
d
er
.
Hen
ce
,
th
e
o
u
tp
u
t
Y(
m
)
o
f
an
FIR
f
ilt
er
is
a
f
u
n
ct
io
n
o
n
l
y
o
f
th
e
i
n
p
u
t
s
ig
n
al
X(
m
)
a
n
d
b
k
ar
e
th
e
f
i
lter
co
ef
f
icien
ts
[
2
8
]
.
T
h
e
i
m
p
u
l
s
e
r
esp
o
n
s
e
o
f
a
lin
ea
r
-
p
h
ase
FI
R
f
ilter
h
as
ev
e
n
o
r
o
d
d
s
y
m
m
etr
y
,
w
h
ic
h
ca
n
b
e
ex
p
lo
ited
to
r
ed
u
ce
th
e
n
u
m
b
er
o
f
m
u
ltip
lier
s
[
2
9
]
.
Fo
r
d
ig
ita
l
FIR
f
i
lter
s
,
a
(
co
m
p
iler
5
.
0
b
lo
ck
s
et)
ar
e
u
s
e
d
s
u
ch
a
s
d
ig
ital
f
ilter
s
i
n
o
u
r
s
i
m
u
latio
n
ex
p
er
i
m
e
n
ts
,
t
h
e
f
il
ter
s
ar
e
ap
p
lied
w
it
h
a
f
i
x
ed
o
r
d
er
,
a
s
am
p
lin
g
f
r
eq
u
e
n
c
y
o
f
(
3
6
0
Hz
>=
2
*
(
o
r
ig
in
a
l
E
C
G
s
ig
n
al)
)
an
d
cu
t
-
o
f
f
f
r
eq
u
en
c
y
s
elec
ted
ac
co
r
d
in
g
to
th
e
u
n
d
es
ir
ed
n
o
is
e
f
r
eq
u
e
n
c
y
.
S
o
,
th
ese
m
ater
ial
s
,
eq
u
ip
m
e
n
t
a
n
d
Si
m
u
latio
n
m
eth
o
d
s
ar
e
co
m
b
i
n
ed
to
ac
h
ie
v
e
o
u
r
m
ai
n
ai
m
s
,
a
n
d
f
i
ll
i
n
th
e
r
es
u
lt
s
o
f
t
h
e
ex
p
er
i
m
e
n
t
n
o
ted
in
t
h
e
f
o
llo
w
i
n
g
tab
les,
i
n
t
h
e
n
ex
t
s
ec
tio
n
.
A
d
ig
ital
FI
R
f
ilter
o
f
M
o
r
d
er
h
as
t
h
e
tr
an
s
f
er
f
u
n
ctio
n
ca
n
b
e
d
escr
ib
ed
b
y
:
0
(
)
(
)
(
3
)
M
k
k
Y
m
b
x
m
k
(
3
)
2
.
4
.
F
DA
T
o
o
ls
a
nd
w
ind
o
w
f
un
ct
io
n
T
h
e
Fil
ter
Desig
n
an
d
A
n
a
l
y
s
is
(
FD
A
)
is
a
v
er
y
i
m
p
o
r
ta
n
t
to
o
l
to
cr
ea
te
f
ilter
tr
an
s
a
ctio
n
s
.
T
h
e
o
p
tio
n
s
av
ailab
le
d
ep
en
d
o
n
th
e
s
p
ec
if
ic
f
il
ter
d
e
s
ig
n
m
et
h
o
d
[
3
0
]
.
T
h
er
e
a
r
e
t
w
o
t
y
p
es
o
f
w
in
d
o
w
f
u
n
ctio
n
s
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
lecE
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
8
2
9
-
838
834
d
escr
ib
ed
b
y
an
ad
j
u
s
tab
le
w
i
n
d
o
w
a
n
d
f
i
x
ed
w
in
d
o
w
[
2
9
]
.
T
h
e
ad
j
u
s
tab
le
w
i
n
d
o
w
h
a
s
b
ee
n
s
et
u
p
w
i
th
o
n
e
o
r
m
o
r
e
p
ar
a
m
eter
s
,
in
w
h
ic
h
,
th
e
Kai
s
er
w
in
d
o
w
w
as
a
d
o
p
ted
w
it
h
“
B
eta”
p
ar
am
e
t
er
β
=
0
.
5
in
t
h
is
ap
p
r
o
ac
h
.
On
th
e
o
th
er
h
a
n
d
,
th
e
FIR
E
q
u
ir
ip
p
le
an
d
FIR
w
i
n
d
o
w
d
esig
n
m
et
h
o
d
s
h
a
v
e
s
ettab
le
o
p
tio
n
s
.
Fo
r
FIR
E
q
u
ir
ip
p
le,
th
e
o
p
tio
n
is
a
d
en
s
it
y
f
ac
to
r
.
Fo
r
FIR
w
i
n
d
o
w
t
h
e
o
p
tio
n
s
ar
e
Scale
P
ass
-
b
an
d
,
w
i
n
d
o
w
s
elec
tio
n
,
a
n
d
f
o
r
th
e
f
o
llo
w
i
n
g
w
i
n
d
o
w
s
,
a
s
e
ttab
le
p
ar
a
m
et
er
[
3
0
]
.
3.
RE
SU
L
T
S
E
V
AL
U
AT
I
O
N
AND
DIS
CUSS
I
O
N
3
.
1
.
E
CG
De
-
no
is
ing
perf
o
rm
a
nce
ba
s
ed
o
n f
ilte
rs po
s
it
i
o
ns
w
it
h k
a
is
er
a
nd
re
ct
a
ng
ula
r
w
ind
o
w
s
W
ith
in
t
h
e
s
co
p
e
o
f
k
n
o
w
led
g
e
f
r
o
m
o
u
r
p
r
o
p
o
s
ed
m
et
h
o
d
ex
p
er
i
m
en
ts
,
in
c
lu
d
i
n
g
t
h
at
t
h
e
p
r
o
ce
s
s
o
f
n
o
is
e
s
d
is
p
o
s
al,
d
ep
en
d
s
o
n
s
ev
er
al
f
ac
to
r
s
t
h
at
d
ir
ec
t
l
y
a
f
f
ec
t
th
e
f
il
ter
r
esu
l
ts
a
n
d
th
e
q
u
alit
y
o
f
th
e
ac
co
m
p
a
n
y
in
g
s
ig
n
al
s
.
T
h
ese
is
s
u
es
ca
n
b
e
ex
p
lai
n
ed
i
n
th
e
f
o
llo
w
in
g
p
o
in
ts
:
Ho
w
to
s
el
ec
t
th
e
p
o
s
itio
n
o
f
ea
ch
f
ilter
i
n
t
h
e
co
m
b
i
n
atio
n
o
f
ca
s
ca
d
ed
f
ilter
?
I
.
e.
w
h
i
ch
o
n
e
a
m
o
n
g
t
h
ese
f
ilter
s
s
h
o
u
ld
b
e
t
h
e
f
ir
s
t,
in
ter
m
ed
iate
o
r
last
to
co
m
b
i
n
e
th
e
m
?
T
h
en
,
h
o
w
to
d
eter
m
in
e
th
e
c
u
t
-
o
f
f
f
r
eq
u
en
c
y
b
an
d
s
w
it
h
ap
p
r
o
p
r
iate
o
r
d
er
f
o
r
ea
ch
f
ilter
?
A
n
d
th
e
s
o
r
ts
o
f
w
in
d
o
w
s
w
h
ic
h
ar
e
u
s
ed
f
o
r
co
n
f
ig
u
r
i
n
g
t
h
e
f
ilter
o
r
an
y
o
th
er
tech
n
iq
u
e
f
o
r
f
ilter
i
n
g
t
h
e
s
i
g
n
als?
T
ab
le
1
s
h
o
w
s
t
h
e
r
esu
lts
o
b
tai
n
ed
f
r
o
m
t
h
e
f
i
n
al
p
h
a
s
e
o
f
eli
m
i
n
ati
n
g
t
h
e
p
r
ed
o
m
in
a
n
t
i
n
te
r
f
er
en
ce
s
o
f
t
h
e
E
C
G
s
ig
n
al.
Ho
w
ev
er
,
b
y
ap
p
l
y
i
n
g
al
l
p
o
s
s
ib
le
ex
p
er
i
m
en
ts
o
n
t
h
e
th
r
ee
f
i
l
ter
s
p
o
s
itio
n
s
w
h
ic
h
ar
e
u
s
ed
f
o
r
t
h
e
s
er
ial
s
y
s
te
m
r
ep
r
esen
ti
n
g
t
h
e
ca
s
ca
d
ed
FIR
f
ilt
er
.
T
h
e
o
u
tp
u
t
s
S
NR
an
d
MS
E
p
ar
a
m
eter
s
g
i
v
e
n
b
y
ea
c
h
co
n
f
i
g
u
r
atio
n
(
p
o
s
itio
n
)
ar
e
co
m
p
ar
ed
in
Kaiser
th
e
n
R
ec
ta
n
g
u
lar
w
in
d
o
w
.
Hen
ce
,
th
e
test
o
f
v
ar
io
u
s
SNR
le
v
els
ar
e
c
o
n
ce
n
tr
at
ed
(
2
to
6
d
B
)
,
w
h
er
e
t
h
e
b
est
SN
R
le
v
els
ar
e
t
r
ap
p
ed
n
ea
r
ly
li
k
e
3
.
4
0
<
SNR
<3
.
9
0
d
B
in
Kaiser
w
i
n
d
o
w
a
n
d
4
.
9
5
<
SNR
<
5
.
7
5
in
R
ec
ta
n
g
u
lar
w
i
n
d
o
w
.
T
ab
le
1
.
T
h
e
r
esu
lts
o
f
t
h
e
d
e
-
n
o
is
i
n
g
p
er
f
o
r
m
an
ce
o
f
s
ev
er
a
l p
o
s
itio
n
s
u
s
i
n
g
SNR
a
n
d
MS
E
p
ar
am
eter
s
F
i
l
t
e
r
s
p
o
si
t
i
o
n
s
K
a
i
se
r
R
e
c
t
a
n
g
u
l
a
r
S
N
R
M
S
E
S
N
R
M
S
E
L
-
B
-
H
0
3
.
4
3
5
1
0
.
0
1
0
0
0
4
.
6
8
3
0
0
.
0
0
9
8
H
-
L
-
B
0
3
.
6
2
3
9
0
.
0
1
0
0
0
4
.
9
4
3
3
0
.
0
0
9
8
B
-
H
-
L
0
2
.
9
7
9
8
0
.
0
1
0
0
0
4
.
0
4
3
2
0
.
0
0
9
8
L
-
H
-
B
0
3
.
1
5
5
3
0
.
0
1
0
0
0
4
.
3
9
8
1
0
.
0
0
9
8
B
-
L
-
H
0
3
.
1
8
9
1
0
.
0
1
0
0
0
4
.
4
2
6
8
0
.
0
0
9
8
H
-
B
-
L
0
3
.
8
9
9
6
0
.
0
1
0
0
0
5
.
7
2
2
3
0
.
0
0
9
8
DA
±
0
.
3
8
0
4
6
0
±
0
.
7
0
2
7
8
0
T
h
e
d
is
p
ar
ity
a
v
er
ag
e
o
f
SN
R
v
alu
e
s
i
n
Kaiser
a
n
d
R
ec
ta
n
g
u
lar
w
i
n
d
o
w
s
ar
e
r
esp
ec
tiv
el
y
esti
m
ated
b
y
±
0
.
3
8
0
4
6
d
B
an
d
±
0
.
7
0
2
7
8
d
B
.
T
h
ese
v
alu
e
s
h
a
v
e
b
ee
n
ex
tr
ac
ted
to
id
en
ti
f
y
th
e
d
i
f
f
e
r
en
ce
b
et
w
ee
n
ea
c
h
th
e
s
e
w
i
n
d
o
w
s
.
Mo
r
eo
v
er
,
w
h
ich
g
et
s
u
p
t
h
e
tas
k
o
f
c
h
o
o
s
in
g
th
e
r
i
g
h
t
w
in
d
o
w
is
p
o
s
s
ib
le.
T
h
ese
v
al
u
es
m
a
y
b
e
s
m
all
b
u
t
ca
n
a
f
f
ec
t
t
h
e
q
u
al
it
y
o
f
th
e
p
r
o
ce
s
s
an
d
th
e
ap
p
ea
r
an
ce
o
f
t
h
e
m
o
r
p
h
o
lo
g
y
o
f
E
C
G
s
ig
n
al
s
as
s
h
o
w
n
i
n
th
e
F
i
g
u
r
e
4
an
d
5
.
Ho
w
ev
er
,
th
e
MSE
p
er
f
o
r
m
a
n
ce
s
w
er
e
r
e
m
ain
ed
s
tab
le
in
b
o
th
w
i
n
d
o
w
s
,
as
d
er
iv
ed
f
r
o
m
i
m
p
r
o
v
ed
co
n
f
ig
u
r
at
io
n
s
r
e
s
p
ec
tiv
el
y
.
T
h
u
s
,
o
b
s
er
v
atio
n
s
ar
e
ad
m
itted
th
e
(
HP
F
-
B
SF
-
L
P
F)
co
n
f
i
g
u
r
atio
n
o
r
H
-
B
-
L
p
o
s
iti
o
n
as
th
e
b
est
a
m
o
n
g
all
p
o
s
iti
o
n
s
;
m
o
r
eo
v
er
,
th
i
s
s
tr
u
ct
u
r
e
is
ac
h
ie
v
in
g
t
h
e
d
e
s
ir
ed
ac
cu
r
ac
y
to
i
m
p
r
o
v
e
t
h
e
q
u
alit
y
o
f
E
C
G
s
i
g
n
al
s
,
t
h
r
o
u
g
h
t
h
e
ab
o
v
e
-
m
en
tio
n
ed
r
u
le
s
o
f
m
a
x
i
m
u
m
SNR
an
d
m
in
i
m
u
m
MSE
.
H
o
w
e
v
er
,
s
u
c
h
as
f
o
u
n
d
i
n
th
e
f
o
llo
w
in
g
tab
le
th
e
r
esu
lt
s
ac
h
ie
v
ed
f
r
o
m
t
h
is
co
m
p
o
s
itio
n
a
f
ter
g
en
er
aliz
in
g
t
h
e
v
a
lid
it
y
o
f
it
s
p
er
f
o
r
m
a
n
ce
o
n
m
a
n
y
s
i
g
n
a
ls
w
i
th
d
if
f
er
e
n
t so
u
r
ce
s
an
d
p
atter
n
s
.
Yo
u
ca
n
lo
o
k
at
t
h
e
p
o
in
ts
r
ep
r
esen
ted
in
t
h
e
g
r
ap
h
ical
cu
r
v
e
o
f
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
u
s
ed
w
i
n
d
o
w
s
.
B
y
c
h
ec
k
i
n
g
w
h
a
t
i
s
s
h
o
w
n
,
t
h
e
v
al
u
es
o
f
SN
R
a
r
e
co
n
s
tan
t
l
y
c
h
an
g
i
n
g
in
ea
c
h
co
n
f
ig
u
r
atio
n
th
at
ha
s
b
ee
n
ap
p
lied
.
As
t
h
e
v
al
u
es
w
er
e
f
air
l
y
co
n
s
is
te
n
t,
e
x
ce
p
t
th
at
t
h
e
R
ec
tan
g
u
lar
as
a
f
i
x
ed
w
i
n
d
o
w
,
i
t
ac
h
iev
ed
a
n
o
ticea
b
le
r
is
e
in
th
e
H
-
B
-
L
co
n
f
i
g
u
r
atio
n
,
an
d
th
is
s
u
p
p
o
r
ts
w
h
at
w
a
s
s
tat
ed
in
th
e
co
m
m
e
n
t
ab
o
u
t
T
ab
le
1
.
Fro
m
h
er
e
it
w
il
l
b
e
b
etter
to
co
n
tain
t
h
e
v
ar
iab
les
t
h
at
ca
n
b
e
e
x
a
m
in
e
d
th
r
o
u
g
h
A
NO
V
A
ap
p
licatio
n
.
3
.
2
.
T
he
ANO
VA
re
s
ults
T
o
ch
ec
k
th
e
v
a
lid
it
y
o
f
r
es
u
lt
s
,
th
e
o
n
e
-
w
a
y
a
n
al
y
s
i
s
o
f
v
ar
ian
ce
(
A
NOV
A
)
w
a
s
also
e
m
p
lo
y
ed
f
o
r
an
al
y
z
in
g
t
h
e
e
f
f
ec
ts
o
f
f
il
ter
s
p
o
s
itio
n
w
i
th
th
e
to
w
s
elec
te
d
w
i
n
d
o
w
t
y
p
e
s
at
v
ar
io
u
s
S
NR
v
al
u
es
o
n
E
C
G
de
-
n
o
is
i
n
g
s
y
s
te
m
.
T
h
e
A
N
OV
A
r
esu
lt
s
(
F
=
2
2
.
9
9
3
,
P
=
0
.
0
0
0
)
in
d
icate
th
at
th
e
f
ilter
s
p
o
s
itio
n
s
ar
r
an
g
e
m
en
t
o
f
th
e
ca
s
ca
d
ed
s
y
s
te
m
at
v
ar
io
u
s
E
C
G
s
i
g
n
als
h
as s
ig
n
i
f
ica
n
t d
if
f
er
en
ce
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lecE
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
ev
ea
lin
g
a
n
d
ev
a
l
u
a
tin
g
t
h
e
I
n
flu
en
ce
o
f filter
s
p
o
s
itio
n
in
c
a
s
ca
d
ed
filt
er
....
(
A
b
d
en
o
u
r
A
l
la
li)
835
3
.
3
.
E
CG
De
-
no
is
ing
ba
s
ed
o
n H
-
B
-
L
co
nfig
ura
t
i
o
n
T
h
e
F
ig
u
r
e
4
s
h
o
w
s
t
h
e
s
tep
s
o
f
th
e
ca
s
ca
d
ed
FIR
f
i
lter
o
f
o
u
r
p
r
o
p
o
s
ed
m
e
t
h
o
d
,
w
h
ic
h
th
e
s
i
g
n
al
p
ass
es
d
u
r
in
g
n
o
is
e
s
ca
n
ce
lli
n
g
.
F
u
r
t
h
er
m
o
r
e,
t
h
e
P
T
B
d
ata
b
ase
r
ec
o
r
d
s
co
n
tain
clea
n
E
C
G
s
ig
n
al
s
g
r
o
u
p
ed
w
it
h
r
esp
ir
atio
n
an
d
5
0
/6
0
Hz
ef
f
ec
t
s
,
t
h
e
W
GN
h
a
s
b
ee
n
a
d
d
ed
to
f
o
r
m
a
n
o
i
s
y
E
C
G
s
ig
n
al
b
y
t
h
r
ee
m
aj
o
r
n
o
is
es
s
u
c
h
as s
h
o
w
n
in
t
h
e
F
i
g
u
r
e
5
.
Fig
u
r
e
4
.
Step
s
o
f
d
e
-
n
o
is
i
n
g
E
C
G
s
i
g
n
a
l u
s
in
g
t
h
e
H
-
B
-
L
c
o
n
f
i
g
u
r
atio
n
Fig
u
r
e
5
.
T
h
e
o
u
tp
u
t sig
n
als r
e
s
u
lted
f
r
o
m
t
h
e
ca
s
ca
d
ed
f
ilter
w
it
h
d
if
f
er
e
n
t c
o
n
f
ig
u
r
atio
n
s
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
lecE
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
8
2
9
-
838
836
T
h
e
H
-
B
-
L
co
n
f
i
g
u
r
atio
n
to
b
e
p
r
o
v
ed
a
s
u
cc
ess
f
u
l
n
o
i
s
e
r
e
m
o
v
al
p
r
o
ce
d
u
r
e
w
h
ile
p
r
es
er
v
in
g
t
h
e
m
o
r
p
h
o
lo
g
y
o
f
th
e
E
C
G
s
i
g
n
a
ls
.
Ho
w
ev
er
,
y
o
u
ca
n
ch
ec
k
th
e
s
i
g
n
al
as
it
g
o
es
t
h
r
o
u
g
h
t
h
e
f
o
llo
w
in
g
f
il
ter
in
g
s
tep
s
.
W
h
er
e,
a)
R
a
w
(
o
r
ig
in
al)
E
C
G
s
ig
n
al
w
as
r
ec
o
r
d
ed
f
o
r
P
atien
t
N
o
2
7
1
in
th
e
PTB
d
atab
ase;
th
is
in
d
icatio
n
i
s
ac
co
m
p
a
n
ied
b
y
B
L
W
an
d
P
L
I
n
o
is
es,
wh
ich
h
a
v
e
b
ee
n
d
iag
n
o
s
ed
w
it
h
t
h
e
d
is
ea
s
e
o
f
M
y
o
ca
r
d
itis
,
an
d
th
at
’
s
d
u
e
t
o
ad
d
itio
n
al
d
iag
n
o
s
es
o
f
A
r
t
er
ial
Hy
p
er
te
n
s
io
n
[
2
6
]
.
b
)
W
GN
co
r
r
u
p
ted
r
a
w
E
C
G
s
ig
n
al
at
an
SN
R
o
f
-
2
7
.
4
2
d
B
ad
j
u
s
ted
to
ac
h
iev
e
SNR
lev
el
s
,
it
is
u
s
ed
as
th
e
m
u
s
cle
s
’
co
n
tr
ac
tio
n
ef
f
ec
t
s
o
u
r
ce
,
i.e
.
t
h
e
e
f
f
ec
t
o
f
E
MG
n
o
is
e
[
3
1
]
.
c)
T
h
e
r
aw
s
ig
n
al
m
i
x
ed
w
it
h
W
GN
(
f
u
ll
n
o
is
y
E
C
G
s
i
g
n
al)
,
w
h
er
e
it
p
ass
e
s
t
h
r
o
u
g
h
th
e
n
ex
t
t
h
r
ee
p
h
a
s
es
o
f
f
i
lt
r
atio
n
i
n
ta
g
s
as
s
h
o
w
n
in
t
h
e
f
o
llo
w
i
n
g
s
tep
s
.
d
,
e
a
n
d
f
.
d
)
T
h
e
s
ig
n
al
r
es
u
lt
in
g
f
r
o
m
t
h
e
f
ir
s
t
f
ilter
HP
F
i
s
f
r
ee
f
r
o
m
B
L
W
n
o
is
e,
w
h
er
e
it
b
ec
a
m
e
in
ac
co
r
d
an
ce
w
it
h
th
e
ax
is
li
n
e
0
.
e)
T
h
e
s
ig
n
al
r
esu
lti
n
g
f
r
o
m
t
h
e
s
ec
o
n
d
f
ilter
B
SF
is
f
r
ee
o
f
B
L
W
an
d
P
L
I
(
5
0
/6
0
Hz)
f
r
eq
u
en
c
y
n
o
is
e
s
,
w
h
er
e
it
s
h
o
u
ld
b
e
as
in
p
u
t
f
o
r
t
h
e
n
e
x
t
s
tag
e
o
f
t
h
e
d
e
-
n
o
is
i
n
g
tas
k
.
f
)
T
h
e
s
ig
n
al
r
e
s
u
l
ted
f
r
o
m
t
h
e
th
ir
d
f
ilter
L
P
F a
n
d
th
e
E
MG
n
o
is
e
r
e
m
o
v
ed
w
it
h
t
h
e
o
th
er
n
o
is
e
s
(
d
e
-
n
o
is
ed
E
C
G
s
ig
n
al)
.
3
.
4
.
E
CG
De
-
no
is
ing
ba
s
ed
t
he
co
m
pa
riso
n t
he
m
o
rpho
lo
g
ies o
f
def
er
ent
co
nfig
ura
t
io
ns
B
y
ap
p
l
y
i
n
g
th
e
d
ef
er
e
n
t
co
n
f
i
g
u
r
atio
n
s
,
in
w
h
ic
h
a
n
o
tice
ab
le
d
if
f
er
en
ce
cr
ea
ted
o
u
t
o
n
th
e
E
C
G
s
ig
n
al
m
o
r
p
h
o
lo
g
y
.
T
h
e
s
ig
n
als
g
e
n
er
ated
b
y
th
is
w
o
r
k
s
h
o
w
th
e
d
if
f
er
e
n
ce
b
et
w
ee
n
th
e
ca
s
es.
W
h
er
e
th
e
r
es
u
lted
E
C
G
s
ig
n
al
w
a
s
p
r
es
en
ted
t
h
e
r
ea
l
s
i
g
n
al
w
a
v
e
f
o
r
m
.
He
n
ce
,
a)
,
b
)
,
c)
,
d
)
,
e)
an
d
f
)
ar
e
illu
s
tr
ati
n
g
th
e
m
o
r
p
h
o
lo
g
y
o
f
t
h
e
ca
r
d
iac
c
y
cles;
w
it
h
d
if
f
er
e
n
t
t
h
ick
n
e
s
s
in
ea
ch
o
f
t
h
e
m
,
b
u
t
f
)
I
t
is
t
h
e
b
est
d
u
e
to
t
h
e
ex
ce
lle
n
t
co
n
f
i
g
u
r
atio
n
(
H
-
B
-
L
)
.
Ho
w
ev
er
,
t
h
e
r
es
u
lti
n
g
s
i
g
n
al
p
r
eser
v
es
t
h
e
E
C
G
m
o
r
p
h
o
lo
g
y
.
He
n
ce
,
i
n
th
is
t
y
p
e
o
f
s
tu
d
ied
d
ata,
w
e
f
o
u
n
d
t
h
at
t
h
e
P
w
a
v
e
w
a
s
in
v
er
ted
(
n
e
g
ati
v
e)
[
3
2
]
an
d
th
i
s
i
s
d
u
e
to
th
e
d
iag
n
o
s
t
ic
s
tat
u
s
o
f
t
h
e
p
atie
n
t
f
r
o
m
w
h
ich
t
h
e
d
ata
ar
e
d
r
a
w
n
.
3
.
5
.
Co
nfir
m
a
t
io
n o
n o
t
her
E
C
G
s
dia
g
no
s
t
ic
cla
s
s
T
ab
le
2
s
h
o
w
s
t
h
e
r
esu
l
ts
o
b
tain
ed
f
r
o
m
t
h
e
co
n
f
o
r
m
atio
n
s
o
f
o
u
r
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
u
s
in
g
t
h
e
(
H
-
B
-
L
)
co
n
f
i
g
u
r
atio
n
w
it
h
R
ec
tan
g
u
lar
w
i
n
d
o
w
.
T
h
e
r
ec
o
r
d
s
s
h
o
w
n
in
th
is
tab
le
e
x
tr
ac
t
ed
f
r
o
m
t
h
e
P
T
B
d
iag
n
o
s
t
ic
d
atab
ase,
w
ith
in
t
h
e
h
ea
d
er
(
.
h
ea
)
f
ile
s
o
f
m
o
s
t
o
f
th
e
s
e
E
C
G
r
ec
o
r
d
s
co
n
tai
n
a
d
etailed
clin
ical
s
u
m
m
ar
y
,
i
n
cl
u
d
in
g
d
iag
n
o
s
is
,
ag
e,
g
e
n
d
er
,
an
d
d
ata
o
n
m
ed
ical
h
is
to
r
y
,
h
o
s
p
ita
l
m
ed
icatio
n
a
n
d
in
ter
v
e
n
tio
n
s
[
2
6
]
.
T
h
e
SNR
1
an
d
MSE
1
r
ep
r
esen
t
t
h
e
e
s
ti
m
atio
n
s
o
f
E
C
G
’
s
r
ec
o
r
d
s
b
ef
o
r
e
f
iltra
tio
n
an
d
T
h
e
SNR
2
an
d
MSE
2
r
ep
r
esen
t t
h
e
esti
m
atio
n
s
af
ter
f
iltra
tio
n
.
T
ab
le
2
.
C
o
n
f
ir
m
at
io
n
e
f
f
ec
t
s
o
f
th
e
d
e
-
n
o
is
i
n
g
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
m
et
h
o
d
f
o
r
d
if
f
er
en
t d
is
ea
s
es i
n
PT
B
d
iag
n
o
s
tic
d
atab
ase
D
i
a
g
n
o
st
i
c
c
l
a
ss
P
T
B
d
b
S
N
R
1
S
N
R
2
M
S
E
1
M
S
E
2
Ef
f
e
c
t
o
n
w
a
v
e
f
o
r
m
M
y
o
c
a
r
d
i
a
l
i
n
f
a
r
c
t
i
o
n
S
0
1
7
5
_
r
e
m
5
.
1
8
0
6
1
9
.
8
6
2
1
0
.
0
0
9
2
0
.
0
0
7
2
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
0
1
0
_
r
e
m
-
5
.
8
1
5
9
2
5
.
4
4
1
4
0
.
0
0
8
7
0
.
0
0
6
4
I
mp
r
o
v
e
d
si
g
n
a
l
C
a
r
d
i
o
my
o
p
a
t
h
y
S
0
3
9
2
l
r
e
m
-
3
.
0
6
4
3
1
3
.
0
6
8
6
0
.
0
1
0
0
0
.
0
0
8
7
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
2
0
0
_
r
e
m
-
2
.
3
9
3
2
0
5
.
6
7
5
1
0
.
0
0
9
7
0
.
0
0
9
2
I
mp
r
o
v
e
d
si
g
n
a
l
H
e
a
r
t
f
a
i
l
u
r
e
S
0
0
2
3
_
r
e
m
-
1
0
.
1
7
7
6
0
6
.
7
7
9
6
0
.
0
0
9
9
0
.
0
0
9
3
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
1
8
3
_
r
e
m
-
0
.
3
1
4
6
1
2
.
3
1
2
1
0
.
0
1
0
1
0
.
0
0
8
9
I
mp
r
o
v
e
d
si
g
n
a
l
B
u
n
d
l
e
b
r
a
n
c
h
b
l
o
c
k
S
0
4
4
1
_
r
e
m
-
7
.
1
8
6
0
2
3
.
0
8
5
9
0
.
0
1
1
1
0
.
0
0
8
8
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
4
2
9
_
r
e
m
-
1
.
9
1
2
7
0
9
.
3
0
0
6
0
.
0
1
0
7
0
.
0
0
9
8
I
mp
r
o
v
e
d
si
g
n
a
l
D
y
s
r
h
y
t
h
mi
a
S
0
0
1
8
_
r
e
m
-
3
.
3
7
1
6
1
1
.
2
2
0
5
0
.
0
1
0
5
0
.
0
0
9
4
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
1
6
9
_
r
e
m
-
3
.
1
7
6
7
0
3
.
4
7
6
9
0
.
0
0
9
7
0
.
0
0
9
3
I
mp
r
o
v
e
d
si
g
n
a
l
M
y
o
c
a
r
d
i
a
l
h
y
p
e
r
t
r
o
p
h
y
S
0
3
9
0
_
r
e
m
-
3
.
2
8
6
7
0
6
.
7
6
7
9
0
.
0
0
9
7
0
.
0
0
9
8
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
4
3
4
_
r
e
m
-
1
1
.
3
8
3
0
0
7
.
7
7
1
0
0
.
0
0
9
5
0
.
0
0
8
8
I
mp
r
o
v
e
d
si
g
n
a
l
V
a
l
v
u
l
a
r
h
e
a
r
t
d
i
se
a
se
S
0
0
3
0
_
r
e
m
-
8
.
7
2
4
1
1
8
.
8
8
7
2
0
.
0
1
0
3
0
.
0
0
8
4
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
1
9
9
_
r
e
m
-
8
.
4
3
6
3
1
0
.
4
2
4
8
0
.
0
0
9
3
0
.
0
0
8
3
I
mp
r
o
v
e
d
si
g
n
a
l
M
y
o
c
a
r
d
i
t
i
s
S
0
5
0
9
_
r
e
m
0
1
.
5
0
5
1
0
5
.
7
2
2
3
0
.
0
1
0
3
0
.
0
0
9
8
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
5
1
0
_
r
e
m
-
5
.
6
6
8
3
0
4
.
4
1
2
3
0
.
0
1
0
2
0
.
0
0
9
8
I
mp
r
o
v
e
d
si
g
n
a
l
H
e
a
l
t
h
y
c
o
n
t
r
o
l
s
S
0
5
4
5
_
r
e
m
-
5
.
4
6
9
6
0
1
.
9
3
3
7
0
.
0
1
0
4
0
.
0
1
0
3
I
mp
r
o
v
e
d
si
g
n
a
l
S
0
5
0
0
_
r
e
m
0
1
.
4
5
1
7
0
1
.
8
1
6
9
0
.
0
1
0
7
0
.
0
1
0
5
I
mp
r
o
v
e
d
si
g
n
a
l
W
ith
th
i
s
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
ap
p
lied
b
y
tes
tin
g
t
h
e
w
i
n
d
o
w
s
w
i
th
t
h
e
f
ilter
s
,
u
n
d
er
th
e
co
n
d
itio
n
o
f
m
ax
i
m
u
m
SNR
a
n
d
m
in
i
m
u
m
MSE
,
th
e
b
est
p
er
f
o
r
m
a
n
c
e
is
ac
h
ie
v
ed
i
n
ea
c
h
d
iag
n
o
s
t
ic
r
ec
o
r
d
class
a
n
d
d
etec
t
i
m
p
r
o
v
ed
E
C
G
s
i
g
n
als.
I
n
t
h
e
co
n
te
x
t
o
f
t
h
is
ap
p
r
o
ac
h
,
t
h
e
ex
p
er
i
m
en
ts
h
a
v
e
b
ee
n
r
ec
o
g
n
izi
n
g
th
a
t
i
t
e
li
m
i
n
ate
s
th
e
p
r
o
b
le
m
o
f
d
eter
m
in
in
g
th
e
ap
p
r
o
p
r
iaten
ess
o
f
an
y
f
ilter
to
g
et
r
ea
d
th
e
u
n
d
esire
d
f
r
eq
u
en
cie
s
f
r
o
m
a
n
y
r
a
w
s
ig
n
al.
T
h
er
ef
o
r
e,
th
e
s
elec
tio
n
o
f
f
il
ter
s
in
ca
s
ca
d
ed
FIR
f
ilter
co
m
b
i
n
a
tio
n
s
h
o
u
ld
n
o
t
b
e
r
an
d
o
m
,
d
u
e
to
it
s
i
m
p
ac
t
o
n
t
h
e
q
u
a
lit
y
o
f
t
h
e
r
es
u
lt
in
g
s
i
g
n
al.
E
ac
h
co
m
b
i
n
atio
n
o
f
ca
s
c
ad
ed
FIR
f
ilter
h
as
its
p
er
f
o
r
m
a
n
ce
.
T
h
is
is
n
o
t
li
m
ited
to
i
m
p
r
o
v
i
n
g
th
e
q
u
alit
y
o
f
th
e
E
C
G
s
i
g
n
al
s
o
lel
y
,
b
u
t
r
ath
er
to
ap
p
l
y
t
h
i
s
tech
n
iq
u
e
to
an
o
t
h
er
t
y
p
e
o
f
s
i
g
n
al
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lecE
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
R
ev
ea
lin
g
a
n
d
ev
a
l
u
a
tin
g
t
h
e
I
n
flu
en
ce
o
f filter
s
p
o
s
itio
n
in
c
a
s
ca
d
ed
filt
er
....
(
A
b
d
en
o
u
r
A
l
la
li)
83
7
3
.
6
.
Co
m
pa
ri
s
o
n o
f
t
he
pro
po
s
ed
a
pp
ro
a
ch
w
it
h e
x
is
t
ing
w
o
rk
s
T
h
e
h
ig
h
p
er
f
o
r
m
a
n
ce
o
b
tain
e
d
in
t
h
i
s
o
p
ti
m
izatio
n
h
a
s
b
ee
n
co
m
p
ar
ed
w
i
th
th
e
e
x
is
ted
p
ap
er
s
,
[
2
]
an
d
[
2
4
]
.
Hen
ce
,
t
h
is
co
m
p
ar
i
s
o
n
co
n
tain
s
f
o
u
r
t
y
p
es
o
f
E
C
G
d
atab
ase
f
r
o
m
t
h
e
P
h
y
s
io
b
an
k
A
T
M.
So
,
t
h
e
s
ig
n
i
f
ica
n
t
SNR
i
m
p
r
o
v
e
m
en
t
an
d
MSE
m
i
n
i
m
izat
io
n
r
es
u
lted
f
r
o
m
t
h
e
d
if
f
er
e
n
t
p
r
o
p
o
s
ed
ca
s
ca
d
e
f
ilter
d
esig
n
s
,
ar
e
illu
s
tr
ated
in
t
h
e
T
ab
le
3
.
Firstl
y
,
th
i
s
s
t
u
d
y
d
ea
l
s
w
it
h
t
h
e
p
r
o
b
lem
o
f
p
o
s
itio
n
i
n
g
f
ilt
er
s
in
t
h
e
s
er
ial
f
ilter
,
m
ain
l
y
d
u
e
to
th
e
lack
o
f
a
w
ar
en
e
s
s
o
f
t
h
eir
i
m
p
o
r
tan
ce
in
p
r
ev
io
u
s
w
o
r
k
s
.
T
h
is
m
et
h
o
d
o
f
s
elec
ti
n
g
t
h
e
b
est
co
n
f
i
g
u
r
atio
n
o
f
th
e
f
ilter
u
s
ed
to
r
e
m
o
v
e
n
o
i
s
e
h
as
ac
h
ie
v
ed
f
air
l
y
i
m
p
r
e
s
s
i
v
e
s
u
cc
es
s
i
n
ter
m
s
o
f
th
e
q
u
a
lit
y
o
f
p
er
f
o
r
m
an
ce
an
d
th
e
ap
p
ea
r
an
ce
o
f
t
h
e
r
esu
lt
in
g
s
i
g
n
al
i
n
g
e
n
er
al.
S
ec
o
n
d
l
y
,
af
ter
a
n
in
-
d
ep
th
a
n
al
y
s
i
s
o
f
th
e
d
ata
o
b
tain
ed
,
w
e
r
ea
ch
t
h
at
th
is
ta
s
k
g
iv
e
s
v
al
u
ab
le
an
d
ad
d
itiv
e
co
n
tr
ib
u
tio
n
i
n
th
e
f
ield
o
f
s
u
cc
es
s
iv
e
f
ilter
i
n
g
.
Fin
all
y
,
t
h
e
alg
o
r
it
h
m
p
r
eser
v
es
u
s
e
f
u
l
in
f
o
r
m
a
tio
n
w
h
ile
r
em
o
v
i
n
g
n
o
is
e
s
f
r
o
m
th
e
E
C
G
s
i
g
n
al
w
it
h
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
n
o
is
e
r
ed
u
ctio
n
is
o
u
t
s
ta
n
d
in
g
.
T
ab
le
3
.
C
o
m
p
ar
is
o
n
o
f
p
r
o
p
o
s
ed
ca
s
ca
d
ed
FIR f
ilter
d
esi
g
n
w
it
h
ex
is
ti
n
g
w
o
r
k
s
A
u
t
h
o
r
n
a
me
P
h
y
si
o
b
a
n
k
A
T
M
d
a
t
a
b
a
se
S
N
R
i
m
p
r
o
v
e
me
n
t
a
f
t
e
r
f
i
l
t
e
r
i
n
g
(
d
B
)
M
S
E
m
i
n
i
m
i
z
a
t
i
o
n
(
%)
P
a
t
r
o
e
t
a
l
.
,
2
0
1
5
[
2
]
M
I
T
-
B
I
H
N
S
R
D
A
TA
4
.
1
4
2
1
.
8
1
M
I
T
-
B
I
H
EC
G
I
D
D
A
TA
2
.
4
7
3
5
.
3
0
N
a
v
d
e
e
p
e
t
a
l
.
,
2
0
1
9
[
2
4
]
M
I
T
-
B
I
H
A
r
r
h
y
t
h
mi
a
7
.
7
5
-
P
r
e
se
n
t
w
o
r
k
P
T
B
d
i
a
g
n
o
st
i
c
EC
G
d
a
t
a
b
a
se
3
1
.
2
5
7
3
2
6
.
4
3
4.
CO
NCLU
SI
O
N
E
v
er
y
ea
r
lier
r
esear
ch
m
a
y
a
g
r
ee
th
at
th
e
ca
s
ca
d
ed
f
ilter
ca
n
b
e
ap
p
lied
as
a
d
esig
n
f
o
r
s
u
p
p
r
ess
in
g
m
u
lti
-
n
o
i
s
es
f
r
o
m
E
C
G
s
ig
n
a
ls
,
b
u
t
if
it
ca
n
s
a
v
e
th
e
m
o
r
p
h
o
lo
g
y
o
f
t
h
e
s
i
g
n
al.
W
ith
t
h
e
d
ee
p
s
tu
d
y
,
t
h
e
ca
p
ab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
o
p
ti
m
izat
io
n
tec
h
n
iq
u
e
w
as
id
e
n
ti
f
ied
an
d
ev
al
u
ated
u
s
in
g
t
h
e
p
o
ten
tial
o
f
SN
R
an
d
MSE
p
ar
a
m
eter
s
.
T
h
er
ef
o
r
e,
th
e
o
v
er
al
l r
es
u
lts
o
b
tain
ed
s
h
o
w
t
h
at
s
e
lecte
d
f
ilter
s
ar
e
a
r
eliab
le
tech
n
iq
u
e
to
b
e
u
s
ed
i
n
r
ec
o
g
n
izi
n
g
t
h
e
v
ar
io
u
s
e
f
f
ec
ts
o
n
ca
s
ca
d
ed
f
ilter
d
esig
n
.
T
h
e
e
x
p
er
i
m
e
n
ta
l
r
esu
lt
s
ar
e
s
h
o
w
n
th
at
R
ec
ta
n
g
u
lar
w
i
n
d
o
w
i
s
m
o
r
e
p
o
ten
t
t
h
a
n
t
h
e
Kai
s
er
w
i
n
d
o
w
.
He
n
ce
,
th
e
b
est
SN
R
le
v
els
ar
e
tr
ap
p
ed
n
ea
r
l
y
lik
e
3
.
4
0
d
B
<
SNR
<
3
.
9
0
d
B
in
Kaiser
w
i
n
d
o
w
an
d
4
.
9
5
d
B
<
SNR
<
5
.
7
5
d
B
in
R
ec
ta
n
g
u
lar
w
i
n
d
o
w
.
He
n
ce
,
t
h
e
d
is
p
ar
it
y
av
er
ag
e
o
f
SN
R
v
alu
e
s
,
i
n
Kaiser
an
d
R
ec
ta
n
g
u
lar
w
in
d
o
w
s
ar
e
r
esp
ec
tiv
e
l
y
esti
m
ated
b
y
±
0
.
3
8
0
4
6
d
B
an
d
±
0
.
7
0
2
7
8
d
B
.
Ho
w
e
v
er
,
th
e
MSE
p
er
f
o
r
m
an
ce
s
h
a
v
e
r
e
m
ain
ed
s
tab
le
o
r
th
e
d
if
f
er
e
n
ce
i
s
v
er
y
litt
le
in
b
o
th
.
O
n
t
h
e
o
t
h
er
h
a
n
d
,
t
h
is
ap
p
lied
ap
p
r
o
ac
h
h
as
led
t
o
3
1
.
3
0
d
B
SNR
i
m
p
r
o
v
e
m
en
t
w
it
h
MSE
m
in
i
m
izatio
n
o
f
2
6
.
4
3
%.
So
,
th
e
o
b
j
ec
tiv
es
o
f
t
h
is
s
t
u
d
y
h
a
v
e
b
ee
n
s
u
cc
es
s
f
u
ll
y
ac
h
iev
ed
w
i
th
t
h
e
d
esire
d
ex
p
ec
tatio
n
s
,
e
v
en
b
y
u
s
i
n
g
m
o
r
e
th
an
E
C
G
’
s
s
i
g
n
al
w
it
h
in
co
r
p
o
r
atin
g
d
if
f
er
en
t
d
iag
n
o
s
t
ic
clas
s
es.
T
h
e
ex
ce
ll
en
t
co
n
f
i
g
u
r
atio
n
o
r
f
ilter
s
p
o
s
itio
n
(
H
-
B
-
L
)
w
as
p
r
o
v
ed
a
s
u
cc
ess
f
u
l
d
e
-
n
o
i
s
i
n
g
ac
tio
n
w
it
h
s
av
in
g
t
h
e
m
o
r
p
h
o
lo
g
y
o
f
E
C
G
s
i
g
n
al
s
.
T
h
is
o
p
ti
m
izatio
n
w
ill
ce
r
tain
l
y
p
r
o
v
id
e
an
ef
f
ic
ien
t
ad
d
itio
n
al
to
o
l
in
E
C
G
s
ig
n
al
an
al
y
s
is
,
w
h
er
e
t
h
e
p
o
s
itio
n
o
f
th
e
f
ilter
p
la
y
s
a
v
i
ta
l
r
o
le
s
ig
n
i
f
ican
tl
y
i
m
p
r
o
v
i
n
g
t
h
e
ca
s
ca
d
ed
f
ilter
p
er
f
o
r
m
a
n
ce
.
Mo
r
eo
v
er
,
an
e
x
ten
s
io
n
to
i
m
p
le
m
e
n
ti
n
g
t
h
i
s
m
et
h
o
d
in
s
p
ec
i
f
ic
h
ar
d
w
ar
e
co
-
s
i
m
u
la
tio
n
e
n
v
ir
o
n
m
e
n
t
s
is
r
ec
o
m
m
e
n
d
ed
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
li
k
e
to
t
h
an
k
t
h
e
an
o
n
y
m
o
u
s
r
ev
ie
wer
s
w
h
o
s
e
ca
r
ef
u
l
r
e
v
ie
w
s
a
n
d
d
etailed
f
ee
d
b
ac
k
h
elp
ed
to
en
h
an
ce
t
h
e
r
ea
d
ab
ilit
y
o
f
t
h
i
s
p
ap
er
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
M
o
h
a
m
e
d
,
“
Hig
h
f
re
q
u
e
n
c
y
n
o
ise
a
p
p
r
o
x
im
a
ti
o
n
a
n
d
a
d
d
a
p
t
iv
e
re
d
u
c
ti
o
n
i
n
t
h
e
ECG
sig
n
a
ls
,”
Do
c
to
ra
l
Diss
.
,
Un
iv
e
rsit
y
o
f
Be
lg
r
a
d
e
,
F
a
c
u
lt
y
o
f
El
e
c
tri
c
a
l
En
g
in
e
e
rin
g
,
2
0
1
8
.
[2
]
K.
K
.
P
a
tr
o
a
n
d
P
.
R
.
Ku
m
a
r,
“
De
-
n
o
isin
g
o
f
ECG
r
a
w
sig
n
a
l
b
y
c
a
s
c
a
d
e
d
w
in
d
o
w
b
a
se
d
d
ig
it
a
l
f
il
ters
c
o
n
f
ig
u
ra
ti
o
n
,
”
2
0
1
5
IEE
E
P
o
w
e
r,
Co
mm
u
n
ica
ti
o
n
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
Co
n
fer
e
n
c
e
(PCIT
C)
,
2
0
1
5
,
pp.
1
2
0
-
124
.
[3
]
C.
V
e
n
k
a
tes
a
n
,
P
.
K
.K
u
m
a
r,
a
n
d
R.
V
a
ra
th
a
ra
jan
,“
F
P
G
A
i
m
p
le
m
e
n
tatio
n
o
f
m
o
d
if
ied
e
rro
r
n
o
rm
a
li
z
e
d
L
M
S
a
d
a
p
ti
v
e
f
il
ter f
o
r
EC
G
n
o
ise
re
m
o
v
a
l
,
”
Clu
ste
r Co
mp
u
ti
n
g
,
p
p
.
1
-
9
,
2
0
1
8
.
[4
]
L
.
S
a
ra
n
g
,
J.
Ra
m
b
a
b
u
,
A
.
V
a
tt
iR
u
p
a
li
,
a
n
d
V
.
T
o
r
n
e
k
a
r
,“
A
su
rv
e
y
o
n
E
CG
sig
n
a
l
d
e
-
n
o
isin
g
tec
h
n
i
q
u
e
s
,
”
in
2
0
1
3
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
C
o
mm
u
n
ica
t
io
n
S
y
ste
ms
a
n
d
Ne
tw
o
rk
T
e
c
h
n
o
l
o
g
ies
,
p
p
.
6
0
-
64
,
2
0
1
3
.
[5
]
N.
S
.
N
.
S
h
a
h
r
u
d
i
n
,
K.
A
.
S
id
e
k
,
a
n
d
A
.
Z
.
Ju
s
o
h
,“
El
e
c
tro
c
a
rd
io
g
ra
m
(EC
G
)
b
a
se
d
stre
ss
re
c
o
g
n
it
io
n
in
teg
ra
ted
w
it
h
d
if
fe
re
n
t
c
l
a
ss
i
f
ica
ti
o
n
o
f
a
g
e
a
n
d
g
e
n
d
e
r
,
”
In
d
o
n
e
si
a
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
15
,
n
o
.
1
,
pp
.
1
9
9
-
2
1
0
,
2
0
1
9
.
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
lecE
n
g
&
C
o
m
p
Sci,
Vo
l.
21
,
No
.
2
,
Feb
r
u
ar
y
2
0
2
1
:
8
2
9
-
838
838
[6
]
R.
Nid
h
i
a
n
d
R.
M
e
h
ra
,
”
A
n
a
l
y
sis
o
f
b
u
tt
e
rw
o
rth
a
n
d
c
h
e
b
y
sh
e
v
f
il
ters
f
o
r
e
c
g
d
e
n
o
isin
g
u
sin
g
w
a
v
e
lets,
”
I
OS
R
Jo
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
C
o
mm
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
(
IOS
R
-
J
E
CE)
,
v
o
l.
6
,
n
o
.
6
,
pp
.
3
7
-
4
4
,
2
0
1
3
.
[7
]
E.
M
u
sta
p
h
a
,
E.
A
b
d
e
lm
o
u
n
im
,
H.
Ra
c
h
id
,
a
n
d
B.
A
b
d
e
laz
iz,
“
El
e
c
tro
c
a
rd
io
g
ra
m
sig
n
a
l
d
e
n
o
isin
g
u
sin
g
d
isc
re
te
w
a
v
e
let
tran
s
f
o
r
m
,
”
J
o
u
rn
a
l
o
f
C
o
mp
u
ter
T
e
c
h
n
o
l
o
g
y
a
n
d
Ap
p
li
c
a
ti
o
n
(
J
CT
A)
,
v
o
l.
5
,
n
o
.
2
,
p
p
.
9
8
-
1
0
4
,
2
0
1
4
.
[8
]
B.
M
o
h
a
m
m
e
d
a
n
d
E.
Ha
ss
a
n
,
“
F
P
GA
-
i
m
p
le
m
e
n
tatio
n
o
f
w
a
v
e
le
t
-
b
a
se
d
d
e
-
n
o
isi
n
g
tec
h
n
iq
u
e
to
re
m
o
v
e
p
o
w
e
r
-
li
n
e
in
terf
e
re
n
c
e
f
ro
m
EC
G
si
g
n
a
l,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
1
0
th
I
EE
E
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
I
n
fo
rm
a
t
io
n
T
e
c
h
n
o
l
o
g
y
a
n
d
A
p
p
l
ica
ti
o
n
s i
n
B
io
me
d
icin
e
,
p
p
.
1
-
4
,
2
0
1
0
.
[9
]
G
.
M
o
sta
f
a
,
S
.
Ga
ss
e
r,
a
n
d
M
.
S
.
El
M
a
h
a
ll
a
wy
,
“
M
ATLA
B
si
m
u
latio
n
c
o
m
p
a
riso
n
f
o
r
d
i
f
fe
re
n
t
a
d
a
p
ti
v
e
n
o
ise
c
a
n
c
e
latio
n
a
lg
o
rit
h
m
s,
”
T
h
e
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Di
g
it
a
l
In
f
o
rm
a
ti
o
n
,
Ne
two
rk
in
g
,
a
n
d
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s (
DINW
C),
S
o
c
iety
o
f
Di
g
it
a
l
In
f
o
rm
a
ti
o
n
a
n
d
W
ir
e
les
s Co
mm
u
n
ica
ti
o
n
,
2
0
1
4
.
[1
0
]
V
.
A
sw
a
th
y
a
n
d
P
.
S
o
n
iy
a
,
“
No
ise
A
n
a
l
y
sis
a
n
d
Dif
f
e
r
e
n
t
De
n
o
is
in
g
T
e
c
h
n
iq
u
e
s
o
f
ECG
S
ig
n
a
l
-
A
S
u
rv
e
y
,”
IOS
R
J
o
u
rn
a
l
o
f
El
e
c
tro
n
ics
a
n
d
C
o
mm
u
n
ica
ti
o
n
E
n
g
i
n
e
e
rin
g
(
IOS
R
-
J
E
CE)
,
2
0
1
6
.
[1
1
]
E.
M
u
sta
p
h
a
,
E.
A
b
d
e
lm
o
u
n
im
,
H.
Ra
c
h
id
,
a
n
d
B.
A
b
d
e
laz
iz,
“
Re
a
l
T
i
m
e
EM
G
No
ise
Ca
n
c
e
ll
a
ti
o
n
f
ro
m
EC
G
S
ig
n
a
ls
u
sin
g
A
d
a
p
ti
v
e
F
il
terin
g
,
”
Pro
c
e
e
d
in
g
s
o
f
t
h
e
2
n
d
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
C
o
mp
u
ti
n
g
a
n
d
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
S
y
ste
ms
(
ICC
W
C
S
)
,
p
.
5
4
,
2
0
1
7
.
[1
2
]
B.
De
e
p
a
k
a
n
d
S
.
P
rim
a
l
,“
ECG
No
ise
Re
m
o
v
a
l
u
sin
g
A
d
a
p
ti
v
e
F
il
terin
g
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
En
g
in
e
e
rin
g
Res
e
a
rc
h
a
n
d
A
p
p
li
c
a
ti
o
n
s(
IJ
ER
A)
,
2
0
1
4
.
[1
3
]
V
.
Ka
d
a
m
,
R.
G
.
Da
b
h
a
d
e
,
a
n
d
A
.
B.
G
a
i
k
w
a
d
,
“B
a
sic
sig
n
a
l
p
ro
c
e
ss
in
g
s
y
ste
m
d
e
si
g
n
o
n
f
p
g
a
u
s
in
g
L
M
S
b
a
se
d
a
d
a
p
ti
v
e
f
il
ter,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Res
e
a
rc
h
in
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(
IJ
RE
T
)
,
v
o
l.
4
,
n
o
.
1
2
,
2
0
1
5
.
[1
4
]
V
.
B
.
G
a
lp
h
a
d
e
a
n
d
P
.
C.
B
h
a
sk
a
r,
“S
w
a
r
m
a
l
g
o
rit
h
m
b
a
se
d
a
d
a
p
ti
v
e
f
il
ter
d
e
sig
n
to
re
m
o
v
e
p
o
w
e
r
li
n
e
in
terf
e
re
n
c
e
f
ro
m
EC
G
sig
n
a
l
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
Res
e
a
rc
h
i
n
En
g
in
e
e
rin
g
a
n
d
T
e
c
h
n
o
lo
g
y
(
IJ
RE
T
)
,
v
o
l.
4
,
n
o
.
3
,
2
0
1
5
.
[1
5
]
S
.
Kh
a
n
,
S
.
M
.
A
n
w
a
r,
W
.
A
b
b
a
s,
a
n
d
Q.
Rizw
a
n
,
“
A
n
o
v
e
l
a
d
a
p
ti
v
e
a
lg
o
rit
h
m
f
o
r
re
m
o
v
a
l
o
f
p
o
w
e
r
li
n
e
in
terf
e
re
n
c
e
f
ro
m
e
c
g
si
g
n
a
l
,
”
S
c
ien
c
e
In
ter
n
a
ti
o
n
a
l
,
v
o
l.
2
8
,
n
o
.
1
,
p
p
.
1
3
9
-
1
4
3
,
2
0
1
6
.
[1
6
]
P.
C.
Bh
a
sk
a
r
a
n
d
M
.
D.
Up
lan
e
,
“
Hig
h
f
re
q
u
e
n
c
y
e
lec
tro
m
y
o
g
ra
m
n
o
ise
re
m
o
v
a
l
f
ro
m
e
lec
tro
c
a
rd
io
g
ra
m
u
sin
g
F
I
R
lo
w
p
a
ss
f
il
ter b
a
se
d
o
n
F
P
G
A
,
”
P
ro
c
e
d
ia
T
e
c
h
n
o
l
o
g
y
,
v
o
l.
2
5
,
p
p
.
497
-
5
0
4
,
2
0
1
6
.
[1
7
]
P
.
C.
Bh
a
sk
a
r
a
n
d
M
.
D.
U
p
lan
e
,
“
F
P
GA
Ba
se
d
No
tch
F
il
te
r
to
Re
m
o
v
e
P
L
I
No
ise
f
ro
m
EC
G
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
Rec
e
n
t
a
n
d
I
n
n
o
v
a
ti
o
n
T
re
n
d
s in
Co
mp
u
ti
n
g
a
n
d
C
o
mm
u
n
ica
t
io
n
,
v
o
l.
3
,
n
o
.
4
,
p
p
.
2
2
4
6
-
2
2
5
0
,
2
0
1
5
.
[1
8
]
M.
S
w
e
ta,
M
.
A
k
a
n
k
sh
a
,
C.
M
u
s
k
a
n
a
n
d
P
.
K.
Ra
h
i
,“
De
sig
n
a
n
d
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
o
f
F
IR
Lo
w
-
P
a
ss
F
il
ter
u
sin
g
Ba
rtl
e
tt
,
Blac
k
m
a
n
a
n
d
T
a
y
lo
r
W
in
d
o
w
T
e
c
h
n
iq
u
e
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
n
Rec
e
n
t
a
n
d
I
n
n
o
v
a
ti
o
n
T
re
n
d
s
in
Co
m
p
u
t
in
g
a
n
d
Co
mm
u
n
ica
ti
o
n
(
IJ
RE
S
T
),
v
o
l.
5,
n
o
.
5
,
pp.
1
1
1
6
-
1
1
2
1
,
2
0
1
7
.
[1
9
]
P.
C.
B
h
a
sk
a
r
a
n
d
M
.
D.
Up
lan
e
,
“
F
P
GA
Ba
se
d
Dig
it
a
l
F
IR
M
u
lt
il
e
v
e
l
F
il
terin
g
F
o
r
ECG
De
n
o
isi
n
g
,”
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
In
f
o
rm
a
ti
o
n
Pro
c
e
ss
in
g
(
ICIP)
,
p
p
.
7
3
3
-
7
38
,
2
0
1
5
.
[2
0
]
S
.
Ku
m
a
r,
R.
M
e
h
ra
,
a
n
d
Ch
a
n
d
n
i,
“
I
m
p
le
m
e
n
tatio
n
a
n
d
d
e
sig
n
i
n
g
o
f
F
IR
f
il
ters
u
sin
g
k
a
is
e
r
w
in
d
o
w
f
o
r
d
e
-
n
o
isin
g
o
f
e
l
e
c
tro
c
a
rd
io
g
ra
m
sig
n
a
ls
o
n
F
P
G
A
,”
IEE
E
7
th
P
o
we
r In
d
ia
I
n
te
rn
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
(
PII
CON)
,
p
p
.
1
-
6
,
2
0
1
6
.
[2
1
]
G
.
W
a
n
g
e
t
a
l.
,
“
ECG
sig
n
a
l
d
e
n
o
isin
g
b
a
se
d
o
n
d
e
e
p
f
a
c
to
r
a
n
a
ly
sis
,”
Bi
o
me
d
ica
l
S
ig
n
a
l
Pro
c
e
ss
in
g
a
n
d
C
o
n
tro
l
,
v
o
l.
5
7
,
p
.
1
0
1
8
2
4
,
2
0
2
0
.
[2
2
]
S.
H
ong
e
t
a
l.
,
“
Op
p
o
rt
u
n
i
ti
e
s
a
n
d
c
h
a
ll
e
n
g
e
s
o
f
d
e
e
p
lea
rn
in
g
m
e
th
o
d
s
f
o
r
e
lec
tro
c
a
rd
io
g
ra
m
d
a
t
a
:
A
s
y
ste
m
a
ti
c
re
v
ie
w
,”
Co
mp
u
ter
s in
Bi
o
l
o
g
y
a
n
d
M
e
d
icin
e
,
p
.
1
0
3
8
0
1
,
2
0
2
0
.
[2
3
]
A
.
K.
V
e
r
m
a
,
I.
S
a
in
i,
a
n
d
B.
S
.
S
a
in
i
,
“
A
lex
a
n
d
e
r
f
ra
c
ti
o
n
a
l
d
if
fe
re
n
ti
a
l
w
in
d
o
w
f
il
ter
f
o
r
E
CG
d
e
n
o
isin
g
,
”
Au
stra
l
a
sia
n
p
h
y
sic
a
l
&
e
n
g
in
e
e
rin
g
sc
ien
c
e
s in
me
d
icin
e
,
v
o
l.
41
,
n
o
.
2,
p
p
.
5
1
9
-
5
3
9
,
2
0
1
8
.
[2
4
]
P
.
Na
v
d
e
e
p
,
S
.
M
e
e
n
a
k
sh
i,
a
n
d
J.
S
h
ru
ti
,“
De
sig
n
a
n
d
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
o
f
C
a
sc
a
d
e
Dig
it
a
l
F
il
ter
f
o
r
EC
G
S
ig
n
a
l
P
ro
c
e
ss
in
g
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
In
n
o
v
a
ti
v
T
e
c
h
n
o
lo
g
y
a
n
d
Exp
l
o
rin
g
E
n
g
i
n
e
e
rin
g
(
IJ
IT
EE
)
,
v
o
l.
8
,
n
o
.
8
,
2
0
1
9
.
[2
5
]
M
.
S
.
C
h
a
v
a
n
,
R.
A
.
A
g
a
r
w
a
la,
a
n
d
M
.
D.
Up
lan
e
,“
Us
e
o
f
Ka
ise
r
win
d
o
w
f
o
r
EC
G
p
ro
c
e
ss
in
g
,”
Pro
c
e
e
d
in
g
s
o
f
th
e
5
th
W
S
EA
S
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
S
i
g
n
a
l
Pro
c
e
ss
in
g
,
R
o
b
o
ti
c
s a
n
d
Au
t
o
ma
ti
o
n
,
2
0
0
6
.
[2
6
]
P
h
y
sio
Ne
t
.
h
tt
p
s://
p
h
y
sio
n
e
t.
o
rg
/c
o
n
ten
t/
?
to
p
ic=
P
T
B
(a
c
c
e
ss
e
d
J
ul
.
2
3
,
2
0
1
8
)
.
[2
7
]
J.W
a
n
g
,
Y.
Ye
a
,
X
.
P
a
n
,
a
n
d
X.
G
a
o
,
“
P
a
ra
ll
e
l
-
ty
p
e
f
r
a
c
ti
o
n
a
l
z
e
ro
-
p
h
a
se
f
il
terin
g
f
o
r
ECG
sig
n
a
l
d
e
n
o
isin
g
,
”
Bi
o
me
d
ica
l
S
i
g
n
a
l
Pro
c
e
ss
in
g
a
n
d
Co
n
tr
o
l
,
v
o
l
.
1
8
,
p
p
.
36
-
4
1
,
2
0
1
5
.
[2
8
]
V
.
M
.
Dik
h
o
le,
S
.
R.
De
sh
m
u
k
h
,
N
.
W
.
L
a
b
a
d
e
,
P
.
P
.
Ch
a
v
a
n
,
a
n
d
N
.
M
.
V
e
ru
lk
a
r,
“
Eff
e
c
t
o
f
F
in
it
e
W
o
rd
L
e
n
g
th
f
o
r
F
IR
F
il
ter
C
o
e
ff
icie
n
t
in
El
e
c
tro
c
a
rd
io
g
ra
m
F
il
terin
g
,
”
Na
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
In
n
o
v
a
ti
v
e
T
re
n
d
s
in
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
(
NC
-
IT
S
E
'1
6
)
,
v
o
l.
4
,
n
o
.
7
,
2
0
1
6
.
[2
9
]
K.
S
.
T
h
y
a
g
a
r
a
jan
,
In
tro
d
u
c
ti
o
n
to
Dig
it
a
l
S
ig
n
a
l
Pro
c
e
ss
in
g
Us
in
g
M
AT
L
AB
wit
h
A
p
p
l
ic
a
ti
o
n
t
o
Dig
it
a
l
Co
mm
u
n
ica
ti
o
n
s
,
S
p
rin
g
e
r
,
2
0
1
8
.
[3
0
]
M
a
tl
a
b
h
e
l
p
.
h
tt
p
s
:/
/www
.
m
a
th
w
o
rk
s.co
m
/h
e
lp
/sig
n
a
l/
u
g
/
h
e
lp
/sig
n
a
l/
u
g
(a
c
c
e
ss
e
d
No
v
.
13
,
2
0
1
9
)
.
[3
1
]
S
.
A
.
Ch
o
u
a
k
ri,
F
.
Be
re
k
si
-
Re
g
u
ig
,
S
.
A
h
m
a
id
i,
a
n
d
O.
F
o
k
a
p
u
,
“
ECG
sig
n
a
l
s
m
o
o
th
in
g
b
a
se
d
o
n
c
o
m
b
in
i
n
g
w
a
v
e
let
d
e
n
o
isin
g
lev
e
ls
,”
Asia
n
J
o
u
rn
a
l
o
f
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
5
,
p
p
.
6
6
6
-
6
7
7
,
2
0
0
6
.
[3
2
]
T
.
W
.
Ba
e
a
n
d
K.
K.
Kw
o
n
,“
Ef
f
i
c
ien
t
Re
a
l
-
T
i
m
e
R
a
n
d
QRS
De
t
e
c
ti
o
n
M
e
t
h
o
d
Us
in
g
a
P
a
ir
o
f
De
riv
a
ti
v
e
F
il
ters
a
n
d
M
a
x
F
il
ter f
o
r
P
o
rtab
le E
CG
De
v
ice
,”
Ap
p
li
e
d
S
c
ien
c
e
s,
v
o
l.
9
,
n
o
.
1
9
,
p
.
4
1
2
8
,
2
0
1
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.