Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
Vol
.
4
,
No
. 3,
J
une
2
0
1
4
,
pp
. 42
2~
43
2
I
S
SN
: 208
8-8
7
0
8
4
22
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Low Complexit
y
Adapti
ve Nois
e Cancell
e
r f
o
r M
o
bile Phones
Based Remote Health Monitoring
Jafar Ram
a
dhan
Mohamm
ed
Departement
of Communication Engineer
ing, Co
lleg
e
of
Electro
n
i
c
Engineering
,
University
of
Mosul, Iraq
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Dec 30, 2013
Rev
i
sed
May
2, 201
4
Accepted
May 20, 2014
Mobile phones are gain
ing accep
tance to be
come an effectiv
e tool
for remote
health monitoring. On one h
a
nd,
during electro
cardiog
raph
ic
(ECG)
recording
,
the pr
es
ence of variou
s
forms
of
noise is inevitable. On the other
hand, a
l
gori
t
hm
s
for adapt
i
ve n
o
is
e can
ce
lla
tion
m
u
s
t
be s
h
ared
b
y
lim
ited
computation
a
l p
o
wer offered b
y
the m
obile pho
nes. This pap
e
r
describes a
new adaptiv
e n
o
is
e canc
e
ll
er s
c
hem
e
, with lo
w com
putationa
l com
p
lexi
t
y
,
for simultaneous
can
cellation of
various
forms of noise in
ECG s
i
gnal. Th
e
proposed scheme is comprised of two stag
es. Th
e first stage uses
an adaptive
notch fil
t
ers
,
w
h
ich are us
ed to
elim
inat
e powe
r-line in
terf
eren
ce from
the
prim
ar
y and r
e
f
e
renc
e inpu
t s
i
g
n
als
,
wher
eas
th
e oth
e
r nois
e
s
a
r
e redu
ced
using modified
LMS algorithm in the
second stage. Low power consumption
and lower silico
n
area ar
e key
is
sues
in mobile p
hones based ad
aptive no
ise
canc
e
ll
ation
.
Th
e redu
ction
in
c
o
m
p
lexit
y
is ob
t
a
ined
b
y
using l
og-log LMS
algorithm for
updating
adap
tive filters
in the proposed s
c
heme. A
comprehensive complexity
and perform
ance
analy
s
is b
e
tween
th
e proposed
and tr
aditional s
c
hemes ar
e prov
ided.
Keyword:
Ad
ap
tiv
e no
ise
can
celler
Ad
ap
tiv
e no
tch
filter
EC
G si
gnal
s
M
odi
fi
e
d
LM
S
al
go
ri
t
h
m
s
Tel
e
m
e
di
ci
ne
Copyright ©
201
4 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Jafar
R
a
m
a
dhan M
o
ham
m
ed,
Depa
rt
em
ent
of C
o
m
m
uni
cat
i
o
n
E
ngi
neeri
n
g
,
C
o
l
l
e
ge of El
ect
ro
ni
c
En
gi
ne
eri
n
g,
Un
i
v
ersity of
Mo
su
l,
Mo
su
l, Ir
aq
Em
a
il: m
o
h
a
mmed
j
74@uo
mo
su
l
.
edu
.
i
q
1.
INTRODUCTION
Tel
e
m
e
di
ci
ne i
s
a use
f
ul
t
o
ol
i
n
pre
v
e
n
t
i
o
n
o
r
di
ag
no
s
i
s of
di
seases
, especi
al
l
y
i
f
t
h
ey
are
dynam
i
cally le
thal suc
h
as
ca
rdiac
diseases.
In
places
where access to m
e
dical serv
ices i
s
tim
e
-cons
uming
or
i
n
feasi
b
l
e
, t
e
l
e
m
e
di
ci
ne coul
d pr
o
v
e l
i
f
e-sa
vi
n
g
. T
hus
, t
h
e u
s
e o
f
Mob
ile Ph
on
es in
rem
o
te h
ealth
m
o
n
ito
ring
has
been of e
x
trem
e interest during
recent years
[1-3]. In su
c
h
case, the
m
obile phone i
s
utilized as a
signal
transm
itter and receiver
by both patient a
nd
doct
o
r, as s
h
own i
n
Figure 1. In t
h
e recei
ver side the tiny feature
s
o
f
th
e EC
G sig
n
a
l sh
ou
ld
be v
e
ry clear fo
r
b
e
tter d
i
agn
o
s
is
wh
ile in th
e tran
sm
it
ti
n
g
si
d
e
du
ri
n
g
EC
G
r
ecor
d
i
n
g,
th
e p
r
esen
ce of
var
i
ou
s
typ
e
s of
n
o
i
ses
is
in
ev
itab
l
e. Th
e
p
r
edo
m
in
an
t n
o
i
ses p
r
esen
t i
n
th
e ECG
in
clu
d
e
s: Base-lin
e
W
a
nd
er
(B
W), Power-Lin
e In
te
rfere
n
ce (PLI), M
u
scle
Artifacts (MA), an
d
Mo
tion
Artifacts (EM). Th
ese artifact
s stron
g
l
y affects th
e ST se
gmen
t, d
e
grad
es th
e sign
al qu
ality, p
r
od
u
ces
larg
e
a
m
p
litu
d
e
sig
n
als in
ECG th
at can
resem
b
le
PQRST wa
ve
form
s, and
m
a
sks tiny features that are im
p
o
rta
n
t
for
diagnosis in the
recei
ver
side. Ca
ncellation
of thes
e
noises in EC
G si
gnals
be
fore a
n
y ot
her proce
sses is
an
im
p
o
r
tan
t
task
for b
e
tter d
i
ag
no
sis.
One
of the
firs
t success
f
ul a
p
proac
h
es to E
C
G ex
t
r
action
problem
was developed by
W
i
drow et al.
b
a
sed
on
lin
ear ad
ap
tiv
e
filter [4
]. Fo
r th
is ap
pro
ach
and
so
m
e
clo
s
ely related
syste
m
s t
h
eoretical stu
d
ies for
t
h
e n
o
i
s
e re
d
u
c
t
i
on
per
f
o
r
m
a
nce
of EC
G c
ont
ai
ni
ng
B
W
,
PLI,
an
d M
A
are gi
ve
n i
n
[
5
]
.
Th
e wi
del
y
use
d
adaptive
noise
canceller consists of
t
w
o i
n
put
s (el
ect
r
o
de
s) nam
e
l
y
,
t
h
e pri
m
ary
el
ect
rode
(s
) an
d ref
e
renc
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
4, No
. 3,
J
u
ne 2
0
1
4
:
42
2 – 4
3
2
4
23
el
ect
rode
(s)
.
T
h
e
pri
m
ary
el
ect
ro
de(s
) i
s
pl
a
ced
o
n
t
h
e
ab
d
o
m
i
nal
regi
o
n
i
n
o
r
der
t
o
pi
c
k
up
t
h
e
EC
G
si
gnal
while t
h
e
othe
r electrode
(s) i
s
place
d cl
ose
to noise
so
urc
e
to se
nse
onl
y
the
b
ackground noise. T
h
e
recent
m
odel
s
of
M
o
bi
l
e
p
h
ones
ar
e de
pl
oy
i
n
g t
h
e co
nce
p
t
o
f
t
w
o
-
i
n
p
u
t
ada
p
t
i
ve n
o
i
s
e ca
nc
el
l
e
r [
6
]
.
T
h
er
efo
r
e, i
n
t
h
i
s
pape
r m
o
re at
t
e
nt
i
on has
been pai
d
t
o
devel
op
a
n
efficient and simplified ada
p
tive noise cancell
e
r for
ECG e
nha
nce
m
ent base
d
on two i
n
put
s
only that is c
o
m
p
atible with
recent m
ode
ls of m
obile phones
.
Ho
we
ver
,
t
h
e
f
o
l
l
o
wi
ng
chal
l
e
ngi
ng
i
ssue
s
m
u
st
be ad
dres
sed
fo
r i
t
s
s
u
cc
essful
de
pl
oy
m
e
nt
:
Efficien
t and
si
m
p
lified
two-inp
u
t
ad
ap
tive no
ise ca
n
celler cap
ab
le
o
f
d
ealing
with v
a
riou
s
n
o
i
ses
sim
u
l
t
a
neousl
y
:
In m
obi
l
e
p
h
one
s ba
sed
EC
G m
oni
t
o
ri
n
g
,
al
l
form
s of
n
o
i
se
m
a
y
occur
sim
u
l
t
a
neousl
y
an
d
u
n
p
r
ed
ictab
l
y. In
th
is situatio
n
,
th
e p
e
rform
a
n
ce o
f
th
e trad
ition
a
l two
-
in
pu
t ad
ap
tiv
e
n
o
i
se can
celler
(ANC) m
a
y d
e
g
r
ad
e sev
e
rel
y
. On
e of th
e
so
lu
tion
s
is b
y
u
s
ing
m
u
lt
i-ch
ann
e
l ad
ap
tive n
o
i
se can
cell
e
r
with
b
lind
spo
t
s (nu
lls) in
th
e arriv
a
l b
e
aring
o
f
n
o
i
se sig
n
a
ls. Ob
v
i
o
u
s
ly, th
e m
u
lti-ch
ann
e
l ANC
i
n
v
o
l
v
es i
n
cre
a
sed c
o
st
i
n
t
h
e f
o
rm
of
m
o
re refere
nc
e sens
ors
,
D/
A c
o
n
v
ert
e
rs,
com
put
at
i
ona
l
com
p
l
e
xi
t
y
, signal
pr
ocessi
n
g
p
o
w
er
. T
h
e
m
odern a
d
apt
i
ve n
o
i
s
e ca
nce
l
l
e
r pre
f
er
tw
o-
ch
ann
e
l [2
],
ov
er
m
u
l
ti-ch
ann
e
l
ANC
du
e to the lo
w co
m
p
u
t
atio
n
a
l co
m
p
lexity p
r
ov
id
ed
by th
e fo
rm
er ov
er t
h
e later.
To
cater th
is issu
e we
n
e
ed
n
e
w
an
d sim
p
le two
-
ch
an
n
e
l
a
d
a
p
tive noise ca
nc
eller capa
b
le t
o
deal effectively
wi
t
h
vari
ous
f
o
rm
s of
n
o
i
s
e
sim
u
l
t
a
neo
u
sl
y
.
The
p
r
o
p
o
s
e
d sc
hem
e
i
n
t
h
i
s
pa
per
i
s
c
o
m
p
ri
sed
of t
w
o
stages
of ada
p
tive filters.
The first sta
g
e c
onsist
of two
adaptive
no
tc
h filters
placed in
parallel to
est
i
m
a
t
e
and cancel
t
h
e PL
I i
n
cl
u
d
ed
i
n
t
h
e
pri
m
ary
i
nput
and
refe
re
nce i
n
p
u
t
si
g
n
al
s. T
h
e sec
o
n
d
st
a
g
e
co
nsists of m
o
d
i
fied
ad
ap
tiv
e no
is
e cancelle
r which estim
a
t
es and cancels
the
other nois
es present i
n
the
noi
sy
EC
G si
g
n
al
f
r
om
t
h
e fi
r
s
t
st
age a
n
d
wi
l
l
pr
ovi
de t
h
e
r
e
qui
red
EC
G
e
nha
ncem
ent
.
Low Co
m
p
u
t
atio
n
a
l Co
m
p
le
x
ity: Th
e trad
itio
n
a
l ANC
sch
e
m
e
with
LMS alg
o
r
ith
m
is u
s
ed
in
tele
m
e
d
i
cin
e
d
u
e
to
its com
p
u
t
atio
n
a
l si
m
p
l
i
city
. How
e
ver
,
i
n
m
obi
l
e
pho
ne base
d A
N
C
fu
rt
h
e
r
redu
ction
in
co
m
p
lex
ity is r
e
q
u
i
red. Th
e
reason
fo
r t
h
is redu
ction
in
co
m
p
lex
ity lead
s to
lo
wer
p
o
wer
co
nsu
m
p
tio
n
an
d
low silico
n
area. Lo
w
p
o
wer con
s
u
m
p
tion
is a k
e
y issu
e in
m
o
b
ile p
h
o
n
e
s. Thu
s
far, to
t
h
e best
o
f
t
h
e
aut
h
or
’s k
n
o
wl
edge
, n
o
ef
fo
rt
has bee
n
m
a
de t
o
red
u
ce t
h
e
com
put
at
i
onal
com
p
l
e
xi
t
y
of
th
e ad
ap
tiv
e no
ise can
celler syste
m
, p
a
rticu
l
arly, with
l
i
m
i
ted
co
m
p
u
t
atio
n
a
l po
wer o
f
fered
b
y
t
h
e
m
obi
l
e
pho
nes
.
The com
put
a
t
i
onal
com
p
l
e
xi
t
y
can be gre
a
t
l
y
reduced
b
y
usi
ng t
h
e l
o
g-l
og LM
S
[7
]
alg
o
rith
m
fo
r u
p
d
a
ting
th
e fi
lter co
efficien
t
s
in
th
e p
r
op
osed
sch
e
m
e
. Th
e red
u
c
tion
in
co
m
p
lex
ity i
s
obt
ai
ne
d
by
us
i
ng
val
u
es
of
t
h
e
refe
rence
i
n
put
dat
a
a
n
d t
h
e o
u
t
p
ut
e
r
r
o
r
,
qua
nt
i
zed
t
o
t
h
e nea
r
est
po
we
r
o
f
two
,
to
com
p
u
t
e th
e g
r
ad
ien
t
. Th
is eli
m
in
ates th
e n
eed
for m
u
ltip
li
ers o
r
sh
ifters
in
th
e alg
o
r
it
hm
’s
u
p
d
a
te section. Th
e
qu
an
tizatio
n
itself is
efficien
tly
real
izab
le in
h
a
rdware. Thu
s
, th
is algo
rith
m
i
s
si
m
ilar to
th
e sig
n
-b
ased
LM
S [8
].
Ho
wev
e
r, th
e co
m
p
lex
ity o
f
th
e log
-
l
o
g
LMS is lower th
an
th
at of th
e
sig
n
-b
ased
LM
S, wh
ile its p
e
rform
an
ce is su
p
e
ri
o
r
to
th
is al
g
o
rith
m
[7
]. Th
ese
goo
d
ad
van
t
ag
eo
us of
the
l
o
g
-
l
o
g LM
S
m
a
ki
ng i
t
a
g
o
o
d
can
di
dat
e
f
o
r m
obi
l
e
ph
o
n
e
base
d t
e
l
e
m
e
di
ci
ne a
ppl
i
c
a
t
i
on es
peci
al
l
y
i
t
requ
ires m
u
ch
lo
wer ch
ip
area fo
r
ASIC imp
l
em
en
tatio
n
.
Fi
gu
re
1.
A
r
chi
t
ect
ure
of
a M
o
bi
l
e
Ph
o
n
e B
a
s
e
d R
e
m
o
t
e
He
al
t
h
M
o
ni
t
o
ri
n
g
Incl
udi
ng
A
N
C
.
Belted detector
on
patient
Bluetooth
Hospital Server
Doctor
’
s
M
obile Phone
Patient’
s
M
obile
Phone
T
e
lephone Network
Tasks:-
Recei
v
e
co
mpr
e
ss
ed
ECG
Deco
mpre
ssi
on
HR
Detection
Draw
ECG
Tasks:-
Perform
ANC
Compressed
ECG
Transmit
ECG
via
SMS
SMS,
M
M
S,
...
, et
c
SMS,
M
M
S,
..,
etc
SMS,
M
M
S,
...
, et
c
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Lo
w C
o
mp
lexity Adap
tive No
i
s
e Can
celler for Mob
ile Ph
ones Based … (Ja
f
a
r
Rama
dhan
Moh
a
m
m
ed
)
42
4
Th
e
first aim
o
f
th
is
p
a
p
e
r
is to
in
t
r
odu
ce e
fficien
t and
sim
p
lified
two-ch
ann
e
l adap
tiv
e
no
ise
can
celler system
fo
r sim
u
ltan
e
o
u
s can
cellatio
n of
v
a
riou
s
form
s o
f
no
ise. Th
e secon
d
ai
m
is to
red
u
c
e th
e
com
put
at
i
onal
com
p
l
e
xi
t
y
(i
n t
e
r
m
s of po
w
e
r and c
h
i
p
ar
ea) of t
h
e
pr
o
pos
ed sc
hem
e
t
o
cope
wi
t
h
l
i
m
i
t
e
d
com
put
at
i
onal
po
we
r
o
ffe
re
d by
t
h
e
m
obi
l
e
ph
o
n
es. Thi
s
p
a
per
i
s
o
r
ga
ni
z
e
d
as fol
l
o
ws
. Sect
i
on 2
i
n
t
r
o
duce
s
the pri
n
ciple of the proposed schem
e
. The
expe
rim
e
nt
al r
e
su
lts o
f
th
e
differen
t
ad
ap
tiv
e no
ise can
cellatio
n
schem
e
s using real EC
G si
gnal a
n
d real
noise signal
s
o
b
t
ai
ned
fr
om
MIT-B
I
H
dat
a
ba
se are
p
r
ese
n
t
e
d a
n
d
di
scuss
e
d
i
n
Se
ct
i
on
3. C
o
ncl
u
si
ons
are
gi
ven
i
n
Sect
i
o
n
4.
2.
THE PROPOSED SCHE
ME
Fig
u
r
e
2
sh
ow
s th
e n
e
w
pr
opo
sed ad
ap
tiv
e
n
o
i
se can
c
ellatio
n
sch
e
m
e
. Th
e pr
im
ar
y in
pu
t and
refe
rence
i
n
put
si
g
n
al
s o
f
t
h
e
pr
o
pose
d
s
c
he
m
e
are gi
ven
a
s
f
o
l
l
o
ws
)
(
)
(
)
(
)
(
)
(
)
(
k
A
k
PLI
k
ECG
k
n
k
ECG
k
x
P
P
P
(1
)
)
(
)
(
)
(
k
A
k
PLI
k
n
R
R
R
(2
)
whe
r
e
)
(
k
x
i
s
prim
ary
i
nput
si
gnal
,
)
(
k
ECG
is clean ECG signal,
)
(
k
n
P
and
)
(
k
n
R
repre
s
ent
s
t
h
e noi
se
si
gnal
s
receive
d
by prim
ar
y electrode(s) a
nd
refe
rence
electrode
(s)
res
p
ec
tively,
)
(
)
(
)
(
)
(
k
EM
k
MA
k
BW
k
A
P
P
P
P
and
),
(
k
PLI
P
),
(
k
BW
P
),
(
k
MA
P
and
)
(
k
EM
P
rep
r
esen
t th
e p
o
wer-l
in
e
in
terferen
c
e, B
a
se-lin
e
wond
er, M
u
scle ar
tifacts, and
M
o
tion
artifacts, respectiv
ely.
Fi
gu
re
2.
The
Pro
p
o
se
d
Ada
p
t
i
v
e N
o
i
s
e C
a
n
cel
l
a
t
i
on Sc
he
m
e
.
The p
r
o
p
o
sed
adapt
i
v
e
noi
se
cancel
l
a
t
i
on sc
hem
e
shown i
n
Fi
gure
2 con
s
i
s
t
s
of t
w
o casc
a
de st
ages.
The first stage
consists of two ad
aptive
notc
h
filters which a
r
e nam
e
d
ANF1 and ANF2 pl
aced in pa
rallel and
use
d
fo
r re
du
ci
ng t
h
e P
L
I i
n
cl
ude
d i
n
t
h
e
pri
m
ary
i
nput
an
d
refere
nce i
n
pu
t
si
gnal
s
. Thi
s
con
n
ect
i
o
n gi
v
e
s t
h
e
ad
v
a
n
t
ag
e
o
f
ad
ap
tation
con
v
erg
e
n
ce at sa
me ti
me fo
r bo
th
ad
ap
tiv
e no
tch
filters
(ANF1
and
ANF2
) if we
ch
oo
se sam
e
v
a
lu
e of t
h
e step size and
sam
e
n
u
m
b
e
r
o
f
filter co
efficien
ts fo
r bo
th ANFs.
First, th
e PLI i
s
can
celled
b
y
b
o
t
h
ANF
filters (A
NF1
and
ANF2). Th
e
ou
tpu
t
s are
j
u
st rep
licas of
)
(
)
(
)
(
k
A
k
ECG
k
x
P
fo
r
AN
F1
o
u
tp
ut an
d
o
f
)
(
k
A
R
for
A
N
F
2
out
put
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
IJEC
E V
o
l
.
4, No
. 3,
J
u
ne 2
0
1
4
:
42
2 – 4
3
2
4
25
The sec
o
nd st
age in t
h
e sc
hem
e
is used for artifacts
cancellation.
The
)
(
k
A
R
is represen
t th
e
referen
c
e inp
u
t
sig
n
a
l t
o
th
e
m
o
d
i
fied
LMS ad
ap
tiv
e filter
in
th
e secon
d
stag
e, acco
r
d
i
ng to
th
e
ad
ap
tiv
e no
ise
filterin
g
p
r
i
n
cip
l
es, wh
ich
are ex
p
l
ain
e
d
in sectio
n
2.2. Th
e system o
u
t
p
u
t
)
(
ˆ
k
G
EC
is the enhanced EC
G
signal.
2.
1. Ad
ap
ti
ve
No
tch Fi
l
t
er
High quality ECG analysis requi
res
the am
plitude of t
h
e power line inte
rference to
be less than
0.5%
of t
h
e pea
k
-to
-
peak
QR
S am
plitude
[9
]
.
Th
eref
ore
,
the P
L
I sh
o
u
ld
be r
e
m
oved fr
om
the EC
G sig
n
al
bef
o
re
doi
ng
any
fu
rt
her a
n
aly
s
is.
A
n
ideal
PLI
su
pp
ressi
on m
e
thod
sh
o
u
ld
rem
ove
the
PLI
,
w
h
ile kee
p
in
g th
e EC
G
signal i
n
tact.
The c
o
nve
n
tional m
e
thod of
cancellati
on s
u
ch interfere
n
ce
is using a
nonadaptive
notch filter that is
tune
d to t
h
e f
r
e
que
ncy
of the
interfe
rence
[
10]
.
However, nonadapti
v
e notch
filte
r is suitable for stat
ionary
sinus
oidal inte
rfe
rence
(am
p
litude, f
r
e
que
nc
y
and p
h
ase a
r
e consta
nt),
but the PLI enc
o
unte
r
ed i
n
ECG signal
m
easurem
ent i
s
non-stationa
ry in na
ture, i.e, the am
plitude, frequency a
nd phase
are varying over
tim
e.
In
order t
o
handl
e
the non-stationary na
t
u
re
of
PLI, adapti
ve notch filter
is considered. The
details of t
h
e
adaptive
notch filter (ANF)
used to re
duce PLI in the proposed schem
e
ar
e explai
ned with the
help of the
bloc
k
diag
ram
give
n in
Fig
u
r
e
3.
An
adaptive notch filter with only
two adaptive weights is shown in
Figure 3. The input signal as
sho
w
n in
Fi
gu
r
e
3,
is re
p
r
esen
ted as
cos
(3
)
A 90
o
phase shifter
is used
to
pr
o
duce
the q
u
a
drat
ure sig
n
al
(4
)
The signals
)
(
0
k
v
and
)
(
1
k
v
are correlated with
)
(
k
PLI
P
. I
n
ad
di
tion, the
)
(
k
ECG
and artifacts
)
(
k
A
P
are
assum
e
d to be
unc
or
related
with
)
(
0
k
v
and
)
(
1
k
v
. Thus, if two signals,
)
(
0
k
v
and
)
(
k
PLI
P
, are c
o
rrelated,
then
)
(
k
PLI
P
m
a
y
be estim
a
ted by
)
(
ˆ
k
I
PL
P
fr
o
m
)
(
0
k
v
and
)
(
1
k
v
.
Esti
m
a
ting
)
(
ˆ
k
I
PL
P
dep
e
nd
s o
n
the strategy
o
f
h
o
w
the cost fu
nct
i
on is to be m
i
nim
i
zed, be it either least
m
ean squares
or
recursi
v
e least squa
res
[11]. For t
h
is paper, the cost
function
will be m
i
ni
m
i
zed based
on
least
m
ean squares
(LMS) algorithm
.
Th
e m
ean
squ
a
r
e
d err
o
r
o
f
th
e ANF1
as
shown in
Figu
r
e
3, is d
e
f
i
n
e
d
as
2
2
2
2
1
))]
(
ˆ
)
(
[(
))]
(
ˆ
)
(
(
))
(
)
(
[(
2
)]
(
)
(
[
]
)))
(
ˆ
)
(
(
)
(
)
(
[(
)]
(
[
k
I
PL
k
PLI
E
k
I
PL
k
PLI
k
A
k
ECG
E
k
A
k
ECG
E
k
I
PL
k
PLI
k
A
k
ECG
E
k
e
E
P
P
P
P
P
P
P
P
P
ANF
(5
)
)
(
0
k
v
)
(
ˆ
k
I
PL
P
Figure 3.
The Adaptive Notch
Filter
)
(
k
e
ANF
0
90
∑
+
_
LMS
)
(
0
k
w
)
(
1
k
w
Pr
im
ar
y I
n
pu
t
R
e
fere
nce I
n
pu
t
)
(
k
x
)
(
1
k
v
)
(
k
e
Adaptation
al
g
orith
m
Digital filter
+
-
Figure 4.
The Adaptive
Noise Canceller
Filter.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN:
208
8-8
7
0
8
Lo
w C
o
mp
lexity Adap
tive No
ise Can
celler for Mob
ile Ph
ones Based … (Ja
f
a
r
Rama
dhan
Moh
a
mmed
)
42
6
Since EC
G si
g
n
al, P
L
I a
n
d a
r
tifacts signals
are
unc
or
related,
0
)]
(
)
(
[
k
PLI
k
ECG
E
P
and
0
)]
(
)
(
[
k
PLI
k
A
E
P
P
,
then
0
))]
(
ˆ
)
(
))(
(
)
(
[(
2
k
I
PL
k
PLI
k
A
k
ECG
E
P
P
P
(6
)
The m
ean squa
red error
becom
e
s
2
2
2
1
))]
(
ˆ
)
(
[(
))]
(
)
(
[(
)]
(
[
k
I
PL
k
PLI
E
k
A
k
ECG
E
k
e
E
P
P
P
ANF
(7
)
M
i
nim
i
zing
)]
(
[
2
1
k
e
E
ANF
is equi
valent to
m
i
nim
i
zing
2
))]
(
ˆ
)
(
[(
k
I
PL
k
PLI
E
P
P
. There
f
ore, t
h
is
m
i
nim
i
zation
will cause
)
(
ˆ
k
I
PL
P
to be the m
i
nim
u
m
m
ean-squa
r
e estim
a
te
of
)
(
k
PLI
P
[11]
. T
h
e estim
a
ted output
of
ANF1 filter
)
(
ˆ
k
I
PL
P
w
h
ich is
sh
o
w
n
in Fi
gu
re
3 is
gi
ven
by
)
(
)
(
)
(
)
(
)
ˆ
1
1
(
k
v
k
w
k
v
k
w
k
I
PL
o
o
P
(8
)
Whe
r
e
)
(
k
w
o
and
)
(
1
k
w
are
two adaptive
f
ilter coefficient
s
.
The
o
u
tp
ut (e
rr
or
) si
gnal
of
A
N
F
1
is
give
n
b
y
)
(
)
(
)
(
1
k
A
k
ECG
k
e
P
ANF
(9
)
App
l
yin
g
th
e
sam
e
math
e
m
atical an
alysis
to
p
a
r
t
2
o
f
Fig
u
r
e
2
above, th
e inpu
t sig
n
a
ls
o
f
ANF2
are
represe
n
ted as
)
(
)
(
)
(
k
A
k
P
k
n
R
R
LI
R
(1
0)
cos
(1
1)
Since t
h
e si
gnal
)
(
k
v
o
is correlated
with
)
(
k
PLI
R
. In addition, the
artifacts
)
(
k
A
R
is assum
e
d to
be
uncorrelated with
)
(
0
k
v
, the esti
m
a
t
e
d si
gnal
of ANF2 filter,
)
(
ˆ
k
I
PL
R
,
is g
i
ven by
)
(
)
(
)
(
)
(
)
ˆ
1
1
(
k
v
k
w
k
v
k
w
k
I
PL
o
o
R
(1
2)
The
o
u
tp
ut (e
rr
or
) si
gnal
of
A
N
F
2
is
give
n
b
y
)
(
)
(
2
k
A
k
e
R
ANF
(1
3)
2.2. Modified LMS Algorithm
An a
d
a
p
tive
noise cancelle
r
with LMS al
gorithm
is shown i
n
Fi
gure
4.
T
h
e out
put signal
y
(
k)
is
fo
rm
ed as the
wei
ghte
d
s
u
m
of a set
o
f
in
put
sig
n
al
sam
p
les
)
1
(
),.......,
1
(
),
(
2
2
2
L
k
e
k
e
k
e
ANF
ANF
ANF
.Mathem
a
t
i
call
y
, the output y(k) is equal to
the inner p
r
o
duct o
f
the input vector
)
(
k
ANF2
e
and the weig
ht
vector
)
(
k
w
)
(
)
(
)
(
k
k
k
y
w
e
T
ANF2
(1
4)
w
h
er
e
)]
1
(
...,
..........
),........
1
(
),
(
[
)
(
L
k
w
k
w
k
w
k
w
(1
5)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN:
2
088
-87
08
IJEC
E V
o
l. 4, No
. 3,
J
u
ne 2
0
1
4
:
42
2 – 4
3
2
4
27
is the weight vector
of the a
d
aptiv
e filter.
Duri
ng t
h
e ada
p
t
a
tion process,
t
h
e wei
ghts a
r
e
adjuste
d
according
to the LMS algorithm
[11]
.
Th
e pr
im
ar
y i
n
pu
t sign
al
)
(
1
k
e
ANF
which contains the ECG signal, the artifacts
)
(
k
A
P
as well as res
i
dual PL
I f
r
o
m
outp
u
t of
AN
F1
. The
re
fere
nce in
put
signal
)
(
2
k
e
ANF
contains
the
artifacts
)
(
k
A
R
as we
ll as the residual PLI f
r
om
out
put o
f
A
N
F
2. T
h
e artifa
c
ts
)
(
k
A
R
are correlated with
)
(
k
A
P
in the prim
ary input si
gnal.
A
gene
ral e
x
p
r
ession
o
f
t
h
e
o
u
tp
ut can
be
ob
tained as
f
o
llo
ws
)
(
)
(
)
(
)
(
)
(
)
(
1
1
k
k
k
e
k
y
k
e
k
e
ANF
ANF
w
e
T
ANF2
(1
6)
The LM
S algorithm
updates
the
filter
coe
ffi
cients according to [11]
)
(
)
(
)
(
)
1
(
k
e
k
k
k
T
ANF2
e
w
w
(1
7)
whe
r
e
is the step size
which cont
rols
t
h
e convergence speed and the
stability of the
adaptive filter.
The weight update define
d in (17) requires L+1
m
u
ltiplicati
ons and
L additions if we
m
u
ltiply
)
(
k
e
outsi
de the lo
op
. I
n
ada
p
tiv
e noise cancel
lation con
cept,
the noise pat
h
has to
be m
odele
d by
the
adaptive filter. The
no
ise pat
h
is im
pulse response
from
the nois
e source to the
primar
y input.
Since this
im
pulse res
p
o
n
se ca
n
be
q
u
ite lon
g
a
n
d
hi
ghly
tim
e-va
ry
ing
d
u
e to
the
m
ovem
e
nt of
the patient
b
o
d
y
,
the
adaptive filter will
require
la
rge num
b
er of filter coefficients (high
com
putational com
p
lexity). So, we need
to develop l
o
w com
p
lexity adaptive al
gorithm
s
that
can
work
effectively
in m
obile
phones. The
r
e
a
r
e three
sim
p
lified ver
s
ions
of t
h
e L
M
S algo
rithm
that signifi
ca
ntly reduc
e the com
puta
tional com
p
lexity
[2, 8].
These algorithm
s
are attract
ive for thei
r as
sure
d c
o
nvergence and ro
bu
stness agai
nst the distu
r
ba
nc
es in
addition to the ease of im
ple
m
entation. The first algo
rithm called sign-error LMS al
gorithm
and its weight
update relation
is
sgn[e(k)]
)
(
)
(
)
1
(
k
k
k
ANF2
e
w
w
(1
8)
W
h
er
e
0
e(k)
1,
-
0
e(k)
0,
0
e(k)
1,
sgn[e(k)]
(1
9)
Because of the
replacem
ent of
e(k
)
by
its sign
, im
plem
entation o
f
this algorithm
may b
e
cheaper
than the standard LMS algorithm
,
especially
in biotele
m
etry where
these types of algorithm
s
m
a
ybe
necessa
ry.
The sig
num
operatio
n can
b
e
perf
o
r
m
e
d on refe
re
nce in
put
instea
d of
error, and it
results in the sign-data
LMS algorithm
can be expre
ssed as
]
)
(
sgn[
e(k)
)
(
)
1
(
k
k
k
ANF2
e
w
w
(2
0)
Finally
, the sig
num
ope
ratio
n
can
be
a
pplied
to b
o
th
er
ro
r a
n
d
re
fe
re
nce in
put si
gnals
, and it results
in the
sign-si
g
n LMS algorit
h
m
expres
sed
as
]
)
(
sgn[
sgn[e(k)]
)
(
)
1
(
k
k
k
ANF2
e
w
w
(2
1)
The com
putational c
o
m
p
lexity of these thre
e algorith
m
s
is
m
u
ch less com
p
ared to the standa
rd LM
S
algorithm
.
Howeve
r, the c
o
nverge
nce rates of these si
gne
d-base
d LMS algorithm
s
are
m
u
ch slower than t
h
e
standa
rd
LM
S
algo
rithm
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN:
208
8-8
7
0
8
Lo
w C
o
mp
lexity Adap
tive No
ise Can
celler for Mob
ile Ph
ones Based … (Ja
f
a
r
Rama
dhan
Moh
a
mmed
)
42
8
The l
o
g-log LMS algorith
m [7] is another class
of adaptive
al
gorithm
used to
update the filter
coefficients in the proposed schem
e
. In th
is algorithm
,
the re
d
u
ction i
n
com
p
lexity
is obtai
ned
by
usin
g
values
of the
reference input data an
d the
output error,
quantized to t
h
e
nearest
powe
r
of t
w
o, to c
o
m
pute t
h
e
gra
d
ient.
This
elim
inates the nee
d
for m
u
ltipliers or
s
h
ifters i
n
the
algorithm
s
update section. The
quantization i
t
self is effici
ently realizable in
ha
rdwa
re. Moreover, the convergence rate a
nd MSE
per
f
o
r
m
a
nce o
f
the lo
g-l
og
L
M
S algo
rithm
is close to
that
of t
h
e standard LMS algorithm
which m
a
kes it a
suitable algorithm
for
practical im
pl
em
entation
o
f
t
h
e a
d
apt
i
ve n
o
ise ca
nce
ller base
d m
o
b
ile ph
one
s.
The weight update
relation for
log-l
o
g LMS
algorithm
is as follows:
]
)
(
[
e(k)]
[
)
(
)
1
(
k
Q
Q
k
k
ANF2
e
w
w
(2
2)
Whe
r
e
Q
is qua
n
tization o
p
erat
ion a
n
d
the
values
of
e(k)]
[
Q
and
]
)
(
[
k
Q
ANF2
e
are all powe
rs
of t
w
o.
There
f
ore, the
y
can
be
repres
ented i
n
the log
2
dom
ain
usin
g fewe
r num
bers of
bits (sm
a
ller
w
o
r
d
-len
gth)
.
2.
3. C
o
mpl
e
xi
ty
An
al
y
s
i
s
In this section, we
show the
com
putational
com
p
lexity requirem
ents for
t
h
e pr
o
pose
d
sc
hem
e
.
The
com
putational cost is
m
easured in term
s of t
h
e num
b
er
of
m
u
l
tiplications, additio
ns, power
cons
um
pti
o
n, and
silicon area.
The
results are l
i
sted
in Table 1, where
L
is
the num
ber of
filter coefficients and
N is t
h
e word-
length
of t
h
e
input data. The word
-le
ngt
h
im
pacts the
com
p
lexity
(in term
s of
po
wer
an
d c
h
ip
are
a
)
significa
ntly
. Specifically
, fo
r
log-l
og LM
S algo
rithm
,
the word-length
is
m
u
ch
lo
wer than
other algorithm
s
.
For e
x
am
ple, the nea
r
est power of two quantized re
prese
n
tation of a data with a wo
rd-
l
eng
t
h
of
12
8 b
its in
the log
2
d
o
m
a
in re
q
u
ires
onl
y
7 bits f
o
r th
e
m
a
gnitu
de a
nd
o
n
e f
o
r the
sign
. T
h
e sig
n
-
er
ro
r LM
S al
go
rithm
uses t
h
e si
gnum
(polarity) of the error
while using
fu
ll
wo
rd
-len
gth
o
f
refe
re
nce in
p
u
t data
.
On
th
e othe
r
han
d
, the si
gn
-
d
ata LM
S algo
rithm
uses the
sign
um
of
the refe
rence in
p
u
t data and f
u
ll wo
rd
-len
gth
of
erro
r
data to update the adaptiv
e
filter. Thus, t
h
e proposed schem
e
w
ith sign-error
LMS algorithm
requires L
shifters
and 2L+8 full
word-length
additions. The proposed schem
e
with
sign-data LMS algorithm
requi
res
only
one
shifter but still requi
r
es
2L
+8 full word-length additions.
The si
gn-sign LMS
algorithm
eli
m
inates
the need
fo
r s
h
ifter b
u
t f
u
rt
her
wo
rse
n
s the co
nve
r
g
enc
e
rate. The propose
d
schem
e
with log-log LMS
algo
rithm
requ
ires L+
8 a
dditions
at
wo
rd
-le
ngt
h
resol
u
tion
o
f
lo
g
2
N
pe
r u
pdate.
Table
1. C
o
m
p
ariso
n
of
the C
o
m
putational
C
o
m
p
lexity
Algorith
m Mult.
Add.
Shifters
Chip
Area
Traditional ANC
with standard LM
S
2L
+1 2L
Nil
L
(
2N+5N
2
)
Pr
oposed ANC with
standar
d
L
M
S
*
2L
+11 2L
+8
Nil
L
(
2N+5N
2
)
Pr
oposed ANC with
sig
n
-
erro
r L
M
S
*
L+4
2
L
+8
L
L(2
N
+8
N)
Pr
oposed ANC with
sign-
data L
M
S
*
L
+
4 2L
+8
1
2L
N+8N
Pr
oposed ANC with
sign-
sign L
M
S
*
L
+
4 2L
+8
Nil
L
N
+2N
Pr
oposed ANC with
log-
log L
M
S
*
L+4
L+8
Nil
L(N+
N)
*
Including the com
p
l
e
xity
of notch f
ilters and the
m
u
lt
iplications that are
shown in second c
o
lu
m
n
is for
co
m
puting the ada
p
tive filter output.
The chi
p
areas of m
u
ltipliers, adde
rs, and shifters are proportional
to the word-lengt
h
(N). Table 1
also com
p
ares
the chi
p
areas
required
by the traditional schem
e
with
standard LMS, prop
osed scheme with
standa
rd
LM
S
,
pr
o
p
o
s
ed sc
hem
e
with sign-e
r
r
o
r
LM
S,
pr
o
pose
d
sc
he
m
e
with sign
-
d
ata LM
S,
pr
op
ose
d
schem
e
with si
gn
-sig
n
LM
S,
and
p
r
op
ose
d
s
c
hem
e
w
ith log-l
o
g LMS al
gorithm
s
.
Am
ong all the algori
thm
s
the chip
area
r
e
qui
red
by
the
pr
o
pose
d
sc
he
m
e
with lo
g-log LMS algorithm
is sl
igh
tly h
i
gh
er
th
an
sign
-
s
ig
n
LM
S an
d l
o
we
r tha
n
all
othe
r
algo
rithm
s
.
3.
E
X
PRE
M
ENTAL RESUL
T
S
Th
e perf
or
m
a
n
ce o
f
th
e
pr
oposed
adap
tiv
e
no
ise can
cellatio
n
sch
e
m
e
with
d
i
ff
er
en
t algo
r
ith
m
s
wer
e
investigate
d
us
ing the actual reco
rd
of EC
G
signal un
de
r real noise sourc
e
s and artifacts
such as power line
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN:
2
088
-87
08
IJEC
E V
o
l. 4, No
. 3,
J
u
ne 2
0
1
4
:
42
2 – 4
3
2
4
29
interference, base-line wander, m
u
sc
le artifacts and m
o
tion artifacts. Thes
e records
were
taken from
the MIT-
B
I
H
Ar
rhy
t
hm
ia database a
n
d M
I
T
-
B
I
H N
o
rm
al Sinus R
h
y
t
hm
database [1
2]
. T
h
ey
were
digitized
at 36
0
sam
p
les per second
per channel with
11-bit
resol
u
tion
ov
e
r
a 1
0
m
V
rang
e. In all o
u
r
ex
perim
e
nts, we
use
d
th
e f
i
r
s
t
3
600
sam
p
les (
1
0
seco
nd
s)
of
th
e ECG sign
als and we have con
s
idere
d
a d
a
taset
of
f
o
ur EC
G
reco
rd
s:
data
1
0
0
,
data1
0
5
,
d
a
ta11
8, data
20
8
to
en
su
re
th
e con
s
isten
c
y
of
r
e
su
lt.
Figu
r
e
5
shows clean ECG
(
d
ata
11
8 of
MI
T-
BIH ar
rhyth
m
ia d
a
tab
a
se)
,
b
a
se-
lin
e
wande
r
(data
bm
), m
u
scle artifacts (data
m
a
) and
m
o
tion a
r
tifacts (data em
). The noisy ECG signal a
n
d its s
p
ectro
gram
are
sho
w
n in
Fi
gu
r
e
6.
Figu
re 5.
M
I
T
-
B
I
H rec
o
r
d
ed
EC
G
Si
gnal (
d
ata
11
8
)
a
n
d
re
al noise
signals
(data bm
, data
m
a
, and
data e
m
).
Figu
re
6.
N
o
is
y
EC
G Si
gnal
at Pr
im
ary Input and its
Spect
rogram
.
First, som
e
experim
e
ntal results are
provided to
show the perform
ance of
t
h
e traditional schem
e
in
the prese
n
ce o
f
divers f
o
rm
s
of n
o
ise: PLI
,
B
W
, M
A
, EM
,
and Ga
ussia
n
white n
o
ise with varia
n
ce of
0.
00
1
.
The e
n
hance
d
ECG signal at
the out
put
of the tra
d
itional
ANC
schem
e
and its spect
rogram
are shown in
Figu
re
7. C
l
ea
rly
,
the c
o
nve
ntional
schem
e
is u
n
able
to
red
u
ce
PLI
an
d
othe
r n
o
ises
sim
u
ltaneo
u
sly
.
Thi
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN:
208
8-8
7
0
8
Lo
w C
o
mp
lexity Adap
tive No
ise Can
celler for Mob
ile Ph
ones Based … (Ja
f
a
r
Rama
dhan
Moh
a
mmed
)
43
0
m
i
sbehavi
o
r
of the traditional schem
e
unde
r vari
ous
fo
rm
s of
noise
, pa
rticularly
wide
ba
nd a
n
d na
rrowband
noise
signals,
has bee
n
als
o
observe
d
i
n
[13].
Figure
7. Output of t
h
e traditional
Schem
e
and its Sp
ectrogram
(step size=
0.02,
filter length=
3
1 wei
ght
s, and
LMS algorithm
)
.
In our second
expe
rim
e
nt, we show the im
portance
of adaptive notch fi
lters
in first st
age of the
proposed scheme for cancell
i
ng
PLI in
reference i
n
put such that adaptive
filter could
work effectively. The
enha
nce
d
EC
G
signal by
the
pr
o
pose
d
sc
he
m
e
is show
n
in Figure 8. T
h
e
im
provem
ent in the
noise
reduction
per
f
o
r
m
a
nce pro
v
ide
d
by
th
e pro
p
o
se
d schem
e
over tha
t
of the traditional schem
e
i
s
evident
when this
perform
a
nce in Fi
gure
8 is
com
p
ared
with that
of the t
r
aditional scheme
g
i
ven in
Figu
r
e
7. Fig
u
r
e
8
also
shows t
h
e outputs of the
ANF
1
and
AN
F2 i
n
the first sta
g
e
of t
h
e propose
d schem
e
. Note that the reduction
of
the
PLI
is d
one
to a
hi
gh
d
e
gree
.
Fig
u
r
e
8
.
Ou
tpu
t
of
t
h
e Pr
oposed
Sch
e
m
e
and
its Sp
ectro
gra
m
(
s
tep
size an
d f
ilter
len
g
t
h in
th
e seco
nd
stag
e
are 0.02
and 31 respectiv
ely,
and LMS al
gorith
m
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN:
2
088
-87
08
IJEC
E V
o
l. 4, No
. 3,
J
u
ne 2
0
1
4
:
42
2 – 4
3
2
4
31
Next, t
h
e learning c
u
rves
of various algorithm
s
th
at
m
a
ybe use
d
in t
h
e propose
d
sc
hem
e
are s
h
own
in Figure 9. From
these curves, it
is clear t
h
at the
propos
e
d sc
hem
e
w
ith sign-data
LMS and
with l
o
g-log
LM
S exhi
bit better pe
rf
orm
a
nce in term
s of b
o
th c
o
n
v
e
rge
n
ce rate a
nd m
ean squa
re err
o
r th
an
othe
r
realizations.
(a)
(b
)
(c)
(
d
) (
e
)
(
f)
Figure
9. Learning C
u
rves
of va
rious algorithm
s
(in all algorithm
s
,
step si
ze=0.02, L=
31).
(a) Traditional
Sch
e
m
e
w
ith
LMS.
(b
) Pr
opo
sed
w
ith
LMS,
(c)
Pr
opo
sed
w
ith
sign
-d
ata LMS,
(d
) pro
p
o
s
ed
w
ith
sig
n
-
e
rr
or
LMS, (e)
Propo
sed
with
sign-sig
n
LM
S,
(f) Propose
d w
ith log-log
LMS.
Scena
r
io 1:
W
i
thout ANC
Lo
w Pe
rf
orm
a
nce
Sender
Scenario 2: W
i
th
ANC
High
Per
f
or
m
a
n
c
e
Receiver
Figu
re
1
0
. T
w
o
Diffe
re
nt Sce
n
ari
o
s
of
a M
o
bile Ph
o
n
e B
a
s
e
d R
e
m
o
te He
alth M
o
nitori
n
g
.
500
100
0
1500
2000
2500
3
000
35
00
-4
5
-4
0
-3
5
-3
0
-2
5
-2
0
-1
5
-1
0
-5
0
Sa
m
p
le
s
M
SE [
d
B]
500
10
00
1
500
200
0
25
00
3
000
3
500
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Sa
m
p
le
s
MS
E
[
d
B
]
50
0
1
000
150
0
2
000
250
0
30
00
3
500
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Sa
m
p
l
e
s
MS
E
[
d
B
]
500
10
00
1
500
200
0
25
00
3
000
3
500
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Sa
m
p
le
s
M
SE [
d
B]
500
10
00
1
500
200
0
25
00
3
000
3
500
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Sa
m
p
le
s
M
SE [
d
B]
50
0
1
000
150
0
2
000
250
0
30
00
3
500
-45
-40
-35
-30
-25
-20
-15
-10
-5
0
Sa
m
p
l
e
s
M
SE [
d
B]
500
1000
1
500
200
0
2
500
3000
3500
0
0.
01
0.
02
0.
03
0.
04
0.
05
0.
06
0.
07
0.
08
0.
09
0.
1
Sa
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1:
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adam
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500
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3500
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5
Sa
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t
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R
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uc
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C
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1000
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3000
3500
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02
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Sa
m
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S
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nari
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2:
W
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adam
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f
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Sa
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c
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2
:
R
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c
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t
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A
N
C
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