T
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p
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on
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Vol.
18
,
No.
3
,
J
un
e
202
0
,
pp.
14
06
~
1
4
15
I
S
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N:
1693
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6930,
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DO
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:
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12928/
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n
s
o
m
n
i
a
s
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p
d
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s
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d
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r
s
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B
y
u
s
i
n
g
s
o
m
e
b
i
o
m
e
d
i
c
a
l
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n
s
o
r
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f
r
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p
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l
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o
m
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o
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a
p
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y
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i
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s
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t
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a
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a
p
h
y
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t
r
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p
p
l
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o
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t
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h
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a
t
a
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o
t
h
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m
e
d
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c
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l
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e
n
t
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r
.
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n
t
h
i
s
s
t
u
d
y
,
i
t
i
s
e
x
p
e
c
t
e
d
t
o
r
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d
u
c
e
t
h
e
c
o
s
t
a
n
d
t
i
m
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o
f
p
a
t
i
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n
t
s
c
o
m
p
a
r
e
d
t
o
t
h
e
w
a
y
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h
e
d
i
a
g
n
o
s
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s
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t
h
e
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o
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i
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l
i
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n
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a
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l
e
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p
d
i
s
o
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d
e
r
n
o
w
a
d
a
y
s
.
2.
RE
S
E
AR
CH
M
E
T
HO
D
T
his
s
e
c
ti
on
wil
l
e
xplain
how
the
s
ys
tem
c
r
e
a
t
e
d
c
a
n
be
us
e
f
ul
to
pe
r
f
o
r
m
f
unc
ti
ons
s
uc
h
a
s
the
objec
ti
ve
s
s
tate
d
in
the
in
tr
oduc
ti
on.
Ove
r
a
ll
,
the
s
ys
tem
in
thi
s
s
tudy
is
divi
de
d
int
o
f
ou
r
maj
or
pa
r
ts
.
T
his
s
ys
tem
c
ons
is
ts
of
a
ha
r
dwa
r
e
s
ys
tem,
a
s
of
t
wa
r
e
s
ys
tem,
c
omm
unica
ti
on
be
twe
e
n
them
,
a
nd
the
d
a
ta
c
las
s
if
ica
ti
on
s
tep.
I
n
thi
s
pa
pe
r
,
we
will
e
xplain
h
ow
e
a
c
h
ha
r
dwa
r
e
c
omponent
is
c
onne
c
ted.
Als
o
e
xplaine
d
the
pr
oc
e
s
s
of
how
the
c
omm
unica
ti
on
be
twe
e
n
ha
r
dwa
r
e
a
nd
s
of
twa
r
e
da
ta
e
xc
ha
nge
.
S
o
f
inally,
how
to
c
las
s
if
ica
ti
on
pa
ti
e
nts
with
ins
omni
a
s
lee
p
di
s
or
de
r
s
.
2.
1.
Hardwar
e
s
ys
t
e
m
s
ar
c
h
it
e
c
t
u
r
e
T
he
ha
r
dwa
r
e
pa
r
t
invol
ve
d
in
the
s
ys
tem
in
th
e
s
tudy
c
onduc
ted
in
thi
s
pa
pe
r
invol
ve
s
s
e
ve
r
a
l
ha
r
dwa
r
e
de
vice
s
.
T
he
ha
r
dwa
r
e
made
c
o
mpac
t
s
o
that
it
make
s
it
e
a
s
y
f
o
r
the
ha
r
dwa
r
e
to
move
f
r
om
one
loca
ti
on
to
a
n
othe
r
.
T
he
r
e
f
or
e
,
the
ha
r
dwa
r
e
m
a
de
with
s
ome
li
ghtwe
ight
c
omponents
a
nd
s
mall
s
ize
.
Ove
r
a
ll
,
the
ha
r
dwa
r
e
c
omponents
that
a
r
e
c
ompi
l
ing
int
o
a
ha
r
dwa
r
e
s
ys
tem
us
e
d
in
thi
s
s
tud
y
a
r
e
s
hown
in
F
igur
e
1.
I
n
F
i
g
u
r
e
1
,
w
e
c
a
n
s
e
e
t
h
a
t
w
e
c
a
n
g
r
o
u
p
t
h
e
m
i
n
t
o
f
o
u
r
pa
r
t
s
.
T
h
e
p
a
r
t
i
s
m
i
c
r
o
c
o
n
t
r
o
l
l
e
r
a
n
d
s
h
i
e
l
d
,
m
e
d
i
c
a
l
s
e
n
s
o
r
,
i
n
t
e
r
f
a
c
e
,
a
n
d
s
u
p
p
l
y
.
I
n
t
h
e
m
i
c
r
o
c
o
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t
r
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
,
Vol.
18
,
No.
3
,
J
une
2020:
14
06
-
14
15
1408
I
n
t
h
e
m
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.
F
igur
e
1.
Ha
r
dwa
r
e
s
ys
tems
a
r
c
hit
e
c
tur
e
2.
2.
S
of
t
war
e
e
m
b
e
d
d
e
d
S
o
f
t
w
a
r
e
e
m
b
e
d
d
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d
i
n
t
h
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a
r
d
w
a
r
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m
i
s
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a
r
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t
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s
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t
h
e
A
r
d
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D
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pr
o
g
r
a
m
.
T
h
e
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r
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e
s
s
f
l
o
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i
n
F
i
g
u
r
e
2
.
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t
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F
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t
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f
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c
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w
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t
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a
d
i
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i
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p
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r
f
o
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m
i
n
g
o
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t
h
e
“
v
o
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d
l
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o
p
(
)
”
f
u
n
c
t
i
o
n
o
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h
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a
r
d
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i
n
o
p
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a
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P
a
t
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t
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o
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i
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l
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t
d
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t
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t
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p
e
s
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A
f
t
e
r
t
h
e
d
a
t
a
o
b
t
a
i
n
e
d
,
t
h
e
n
t
r
y
t
o
c
o
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n
e
c
t
t
o
t
h
e
s
e
r
v
e
r
.
I
f
i
t
i
s
n
o
t
c
o
n
n
e
c
t
e
d
,
i
t
w
i
l
l
r
e
r
e
a
d
t
h
e
p
a
t
i
e
n
t
'
s
v
i
t
a
l
d
a
t
a
a
n
d
r
e
-
c
o
n
n
e
c
t
w
i
t
h
t
h
e
s
e
r
v
e
r
.
I
f
i
t
i
s
c
o
n
n
e
c
t
e
d
,
t
h
e
p
a
t
i
e
n
t
'
s
v
i
t
a
l
d
a
t
a
w
i
l
l
c
o
n
v
e
r
t
i
n
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o
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m
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d
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t
a
s
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n
g
w
h
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s
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a
r
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t
f
o
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s
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n
d
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t
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t
o
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h
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e
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v
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s
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g
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h
e
"
P
O
S
T
"
m
e
t
h
o
d
f
r
o
m
R
E
S
T
(
r
e
p
r
e
s
e
n
t
a
t
i
o
n
a
l
s
t
a
t
e
t
r
a
n
s
f
e
r
)
A
P
I
(
a
p
p
l
i
c
a
t
i
o
n
p
r
o
g
r
a
m
i
n
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e
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f
a
c
e
)
p
r
e
p
a
r
e
d
o
n
t
h
e
s
e
r
v
e
r
-
s
i
d
e
.
T
h
e
p
r
o
c
e
s
s
o
f
t
h
e
l
o
o
p
(
)
f
u
n
c
t
i
o
n
w
i
l
l
r
e
p
e
a
t
u
n
t
i
l
i
t
d
o
e
s
n
’
t
g
e
t
p
o
w
e
r
b
a
c
k
.
2.
3.
C
om
m
u
n
icat
io
n
T
his
s
e
c
ti
on
is
c
omm
unica
ti
on
be
twe
e
n
s
y
s
te
ms
loca
ted
in
r
e
mot
e
a
r
e
a
s
a
nd
s
e
r
ve
r
s
us
ing
int
e
r
c
onne
c
ti
on
ne
twor
ks
.
T
he
s
ys
tem
is
c
onduc
ti
n
g
c
omm
unica
ti
on
be
twe
e
n
s
ys
tems
loc
a
ted
in
r
e
m
ote
a
r
e
a
s
s
e
n
ding
da
ta
to
a
s
e
r
ve
r
whe
r
e
the
s
e
r
ve
r
a
c
t
s
a
s
a
R
E
S
T
API
.
T
he
c
onne
c
ti
on
f
low
p
r
oc
e
s
s
of
the
de
vice
s
ys
tem
a
t
a
r
e
mot
e
loca
ti
on
with
a
s
e
r
ve
r
is
s
hown
in
F
igur
e
3
.
T
he
p
r
oc
e
s
s
in
F
igur
e
3
s
tar
ts
f
r
om
the
ini
ti
a
ti
on
of
the
us
e
r
a
nd
the
we
bs
it
e
hos
t
to
lo
g
in
to
the
da
taba
s
e
.
Da
ta
r
e
c
e
ived
thr
ough
th
e
R
E
QU
E
S
T
meth
od
in
R
E
S
T
API
will
then
s
tor
e
d
in
e
a
c
h
tabl
e
in
the
M
yS
QL
da
taba
s
e
that
is
s
e
t
de
pe
nding
on
the
type
of
s
e
ns
or
type
us
e
d
by
us
ing
the
I
NSE
R
T
c
omm
a
nd
in
the
da
taba
s
e
.
F
ur
the
r
mor
e
,
us
ing
the
AP
I
f
or
e
a
c
h
table
by
taking
da
ta
f
r
om
the
da
taba
s
e
whic
h
c
on
v
e
r
t
int
o
the
J
S
ON
f
o
r
mat.
T
he
s
c
he
matic
pr
oc
e
s
s
f
low
f
or
c
r
e
a
ti
ng
the
API
i
s
s
hown
in
F
igur
e
4.
T
he
s
c
he
matic
is
to
e
xplain
how
the
pa
ti
e
nt
da
ta
f
low
is
take
n
f
r
om
the
da
taba
s
e
pr
e
pa
r
ing
the
da
ta
us
e
d
to
be
the
J
S
ON
f
or
mat
.
F
ir
s
t,
ini
ti
a
ti
ng
we
b
s
it
e
us
e
r
s
a
nd
hos
ts
who
then
log
in
to
the
da
taba
s
e
on
the
we
b
s
e
r
vice
.
R
e
tr
i
e
ve
da
ta
f
r
om
e
a
c
h
s
e
ns
or
table
f
r
om
the
da
taba
s
e
us
ing
th
e
GE
T
c
omm
a
nd
on
M
yS
QL
.
Af
te
r
the
pa
ti
e
nt
da
ta
took
,
then
the
ne
xt
s
tep
s
or
ts
the
da
ta
a
c
c
or
ding
to
ne
e
ds
a
nd
make
s
a
n
a
r
r
a
y
of
p
a
ti
e
n
t
da
ta
li
ne
s
.
N
e
xt
is
to
c
onve
r
t
the
da
ta
a
r
r
a
y
c
r
e
a
ted
p
r
e
vious
ly
int
o
da
ta
in
the
f
o
r
m
o
f
the
J
S
ON
f
or
mat
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
I
ns
omnia
analys
is
bas
e
d
on
int
e
r
ne
t
of
thi
ngs
us
in
g
e
lec
tr
oc
ar
diogr
aph
y
and
...
(
N
ov
i
A
z
man)
1409
Fi
gur
e
2.
Ha
r
dwa
r
e
p
r
oc
e
s
s
ing
f
lowc
ha
r
t
F
igur
e
3.
F
il
e
r
e
que
s
t
da
ta
pr
oc
e
s
s
f
lowc
ha
r
t
F
igur
e
4.
API
pr
oc
e
s
s
f
lowc
ha
r
t
2.
4.
I
n
s
om
n
ia
c
la
s
s
if
icat
ion
I
n
c
las
s
if
ying
pa
ti
e
nts
s
uf
f
e
r
ing
f
r
om
ins
omni
a
s
lee
p
dis
or
de
r
s
,
the
pr
oc
e
s
s
of
tes
ti
ng
the
da
ta
with
the
f
ir
s
t
a
r
t
if
icia
l
ne
ur
a
l
ne
twor
k
a
im
s
to
c
omp
a
r
e
the
pa
ti
e
nt's
c
a
r
diac
a
c
ti
vit
y
tes
t
da
ta
obtain
e
d
f
r
om
e
lec
tr
oc
a
r
diogr
a
phy
s
e
ns
or
s
with
t
r
a
ini
ng
da
ta
f
r
om
medic
a
l
de
vice
s
.
T
he
s
e
c
ond
pr
oc
e
s
s
is
to
tes
t
da
ta
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
,
Vol.
18
,
No.
3
,
J
une
2020:
14
06
-
14
15
1410
a
r
ti
f
icia
l
ne
ur
a
l
ne
twor
ks
that
c
ompar
e
the
p
a
ti
e
nt's
moveme
nt
da
ta
dur
ing
s
lee
p
c
ondit
io
ns
f
r
om
e
lec
tr
omyogr
a
phy
s
e
ns
or
s
with
da
ta
f
r
om
medic
a
l
de
vice
s
.
T
he
pr
e
dicte
d
output
is
the
r
e
s
ult
of
c
o
mpar
is
on
with
a
c
tual
output
.
I
f
the
pr
e
dicte
d
output
a
pp
r
oa
c
he
s
the
va
lue
of
one
of
the
a
c
tual
output
s
with
the
s
ma
ll
e
s
t
e
r
r
or
va
lue,
then
i
t
c
a
n
be
c
onc
luded
t
ha
t
the
pr
e
dicte
d
output
is
c
las
s
if
ying
a
c
c
or
ding
to
s
pe
c
if
ied
c
o
ndi
ti
ons
.
T
he
s
e
tw
o
pr
oc
e
s
s
e
s
a
r
e
s
hown
in
F
igur
e
5
,
the
pr
oc
e
s
s
of
c
ompar
ing
da
ta
us
ing
a
r
ti
f
icia
l
ne
ur
a
l
ne
twor
ks
a
s
s
hown
in
F
igur
e
5
(
a)
.
F
ur
ther
mor
e
,
the
las
t
pr
oc
e
s
s
is
to
c
las
s
if
y
ins
o
mni
a
.
I
ns
omni
a
c
las
s
if
ica
ti
on
de
r
ives
f
r
o
m
two
c
ombi
ne
d
da
ta
be
twe
e
n
the
pr
e
dicte
d
output
f
r
om
E
C
G
a
nd
E
M
G
da
ta
c
ompa
r
e
d
with
a
c
tua
l
output
f
r
om
medic
a
l
da
ta.
I
f
the
r
e
s
ult
s
obtaine
d
f
r
om
pa
ti
e
nts
a
ppr
oa
c
h
one
o
f
the
va
lues
o
f
the
a
c
tua
l
output
with
the
s
malles
t
e
r
r
or
va
lue,
then
the
c
onc
lu
s
ions
of
the
mea
s
ur
e
d
pa
ti
e
nt
da
ta
c
a
n
be
inc
luded
in
the
c
las
s
if
ica
ti
on
a
c
c
or
ding
to
the
s
pe
c
if
ied
c
ondit
i
ons
.
T
he
p
r
oc
e
s
s
in
c
las
s
if
ica
ti
on
s
hown
in
F
igur
e
5
(
b)
.
(
a
)
(
b)
F
igur
e
5
.
(
a
)
C
ompar
is
on
pr
oc
e
s
s
of
tr
a
ini
ng
a
nd
te
s
ti
ng
da
ta
us
ing
a
r
ti
f
icia
l
n
e
ur
a
l
n
e
two
r
k,
(
b)
I
ns
omni
a
c
las
s
i
f
ica
ti
on’
pr
oc
e
s
s
us
ing
a
r
ti
f
icia
l
n
e
ur
a
l
n
e
twor
k
3.
RE
S
UL
T
S
A
ND
AN
AL
YSI
S
3.
1.
E
f
f
e
c
t
ivenes
s
of
s
e
n
d
in
g
d
a
t
a
T
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
o
f
s
e
n
d
i
n
g
d
a
t
a
n
e
e
d
s
t
o
b
e
e
x
a
m
i
n
e
d
f
u
r
t
h
e
r
b
e
c
a
u
s
e
i
n
s
o
m
e
c
a
s
e
s
i
n
r
e
m
o
t
e
l
o
c
a
t
i
o
n
s
,
s
i
g
n
a
l
s
t
r
e
n
g
t
h
m
a
y
b
e
w
o
r
s
e
t
h
a
n
i
n
u
r
b
a
n
l
o
c
a
t
i
o
n
s
;
t
h
e
r
e
f
o
r
e
t
e
s
t
i
n
g
w
i
t
h
v
a
r
y
i
n
g
s
i
g
n
a
l
s
t
r
e
n
g
t
h
s
a
p
p
e
a
r
s
i
n
t
h
i
s
s
t
u
d
y
.
W
e
c
a
n
s
e
e
i
n
T
a
b
l
e
1
,
a
c
o
m
p
a
r
i
s
o
n
o
f
d
a
t
a
w
i
t
h
v
a
r
y
i
n
g
s
i
g
n
a
l
s
t
r
e
n
g
t
h
s
.
T
a
b
l
e
1
i
s
t
h
e
d
a
t
a
s
e
n
t
f
r
o
m
t
h
e
E
S
P
3
2
m
i
c
r
o
c
o
n
t
r
o
l
l
e
r
w
h
i
c
h
h
a
s
b
e
e
n
s
e
t
u
p
b
y
s
e
n
d
i
n
g
t
w
o
d
a
t
a
p
e
r
s
e
c
o
n
d
(
7
2
0
0
d
a
t
a
p
e
r
h
o
u
r
)
w
i
t
h
t
h
e
c
o
n
d
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%
.
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r
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m
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t
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s
e
d
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s
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e
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t
a
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l
e
.
T
a
ble
1.
S
e
nding
d
a
ta
a
c
c
ur
a
c
y
No
S
e
ndi
ng
ti
me
(
in
ho
ur
s
)
A
mount
of
pa
c
ke
t
da
ta
S
ig
na
l
S
tr
e
ngt
h
-
108 dB
m
-
97 dB
m
-
89 dB
m
-
81dB
m
D
a
ta
r
e
c
e
iv
e
d
A
c
c
ur
a
c
y
D
a
ta
r
e
c
e
iv
e
d
A
c
c
ur
a
c
y
D
a
ta
r
e
c
e
iv
e
d
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c
c
ur
a
c
y
D
a
ta
r
e
c
e
iv
e
d
A
c
c
ur
a
c
y
1
0.5
3600
3560
98.80%
3560
100.00%
3560
100.00%
3560
100.00%
2
1
7200
7020
97.
50%
7020
1
00.00%
7020
100.00%
7020
100.00%
3
1.5
10800
10580
97.90%
10580
100.00%
10580
100.00%
10580
100.00%
4
2
14400
14080
9
7
.70%
14080
100.00%
14080
100.00%
14080
100.
00%
T
h
e
e
f
f
e
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t
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v
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a
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.
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e
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t
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l
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y
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d
i
s
a
r
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a
l
-
time
g
r
a
p
h
,
t
h
e
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s
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o
f
s
t
a
b
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i
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c
o
n
n
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c
t
i
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t
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s
a
m
u
s
t
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
I
ns
omnia
analys
is
bas
e
d
on
int
e
r
ne
t
of
thi
ngs
us
in
g
e
lec
tr
oc
ar
diogr
aph
y
and
...
(
N
ov
i
A
z
man)
1411
3.
2.
I
n
s
om
n
ia
c
las
s
if
icat
ion
Da
ta
s
tor
e
d
on
the
s
e
r
ve
r
in
a
ddit
ion
to
vi
s
ua
li
z
a
t
ion
by
making
dyna
m
ic
c
ha
r
ts
a
ls
o
the
da
ta
is
us
e
f
ul
f
or
c
las
s
if
ying
ins
omni
a
,
whe
ther
the
pa
ti
e
nt
ha
s
ins
omni
a
or
not
.
B
y
us
ing
a
r
ti
f
icia
l
int
e
ll
igenc
e
thr
ough
a
r
ti
f
icia
l
ne
u
r
a
l
ne
twor
k
methods
pe
r
f
o
r
med
on
the
s
e
r
ve
r
.
P
a
ti
e
nt
da
ta
will
be
te
s
t
da
ta
a
nd
c
ompar
e
d
with
tr
a
ini
ng
da
ta
de
r
ived
f
r
om
ve
r
if
ied
medic
a
l
de
vice
da
ta.
T
he
r
e
a
r
e
thr
e
e
s
tage
s
of
the
pr
oc
e
s
s
of
us
ing
ar
t
i
f
icia
l
ne
ur
a
l
ne
twor
ks
to
obtain
pa
ti
e
nt
c
la
s
s
if
ica
ti
on
r
e
s
ult
s
.
I
n
the
a
r
ti
f
icia
l
ne
ur
a
l
ne
twor
k
that
us
e
s
,
ther
e
is
a
n
i
nput
laye
r
a
n
d
tar
ge
t
output
,
whe
r
e
ther
e
a
r
e
ten
a
r
r
a
ys
of
da
ta
f
r
om
th
r
e
e
pa
ti
e
nts
whic
h
a
r
e
tr
a
ini
n
g
da
ta
that
be
c
ome
the
input
l
a
ye
r
a
nd
one
e
xpe
c
ted
output
tar
ge
t.
W
it
h
the
ini
ti
a
ti
on
of
the
number
of
laye
r
s
us
e
d
in
the
f
or
m
o
f
ten
input
laye
r
s
,
ten
h
idden
la
ye
r
s
,
a
nd
one
output
laye
r
.
T
e
s
t
da
ta
f
r
om
the
s
e
ns
or
a
s
muc
h
a
s
ten
a
r
r
a
ys
o
f
da
ta
f
r
om
the
tr
a
ini
ng
da
ta
c
omp
a
r
e
with
ten
a
r
r
a
ys
of
tes
t
da
ta,
whic
h
is
the
input
laye
r
.
On
e
in
ten
hidden
laye
r
s
c
ons
is
ts
of
ne
ur
ons
that
r
e
c
e
ive
e
a
c
h
da
ta
f
r
om
ten
inpu
t
laye
r
s
.
T
he
r
e
s
ult
s
in
the
outpu
t
laye
r
a
r
e
a
c
a
lcula
ti
on
of
the
va
lue
of
the
input
la
ye
r
to
be
the
tar
ge
t
output
.
T
r
a
ini
ng
d
a
ta
us
e
d
to
t
r
a
in
tr
a
ini
ng
da
ta,
whe
r
e
th
e
tr
a
ini
ng
da
ta
us
e
d
is
da
ta
f
r
o
m
medic
a
l
de
vice
s
.
T
he
r
e
a
r
e
th
r
e
e
t
r
a
ini
ng
da
ta
in
puts
f
r
om
med
ic
a
l
de
vice
s
pr
ovided
that
two
da
ta
a
r
e
not
ins
om
nia
da
ta,
a
nd
one
is
ins
omni
a
s
uf
f
e
r
e
r
da
ta.
E
a
c
h
input
va
lue
wil
l
c
l
a
s
s
if
y
wi
th
the
output
va
lue
a
gr
e
e
d
with
the
s
pe
c
ialis
t
doc
tor
.
L
a
ter
the
tr
a
ini
ng
da
ta
will
g
o
thr
ough
a
tr
a
ini
ng
pr
oc
e
s
s
unti
l
c
or
r
e
c
t
i
ons
to
th
e
a
gr
e
e
d,
a
nd
e
xpe
c
ted
output
va
lues
a
r
e
r
e
a
c
he
s
.
T
he
va
lu
e
of
tr
a
ini
ng
us
e
d
is
350
,
whe
r
e
the
va
lue
of
thi
s
tr
a
ini
ng
ha
s
the
s
malles
t
e
r
r
or
of
the
whole
tes
ted.
T
he
s
e
r
e
s
ult
s
a
r
e
s
hown
in
T
a
ble
2
.
E
C
G
a
na
lys
i
s
i
s
s
hown
in
F
igur
e
6
,
is
a
c
onc
lus
ion
f
r
om
the
r
e
s
ult
s
of
the
a
na
lys
is
of
pa
ti
e
nt
bios
ignal
da
ta
us
ing
the
B
ioS
ppy
li
br
a
r
y,
g
r
a
phs
a
r
e
f
il
te
r
e
d,
a
na
lyze
d
f
or
c
a
r
dia
c
a
c
ti
vit
y
a
nd
r
e
view
e
d
with
the
P
QR
S
T
s
ignal
pa
tt
e
r
n
dis
playe
d
in
the
"
T
e
mpl
a
tes
"
im
a
ge
c
olum
n.
T
a
ble
2.
Da
ta
tr
a
i
ning
a
nd
e
r
r
or
o
f
outpu
t
N
o
T
r
a
in
E
r
r
or
O
ut
pu
t
No
T
r
a
in
E
r
r
or
O
ut
pu
t
1
10
25.8 %
9
300
3.1 %
2
30
22.6 %
10
350
1.0 %
3
50
22.3 %
11
400
8.4 %
4
80
15.2 %
12
450
9.1 %
5
100
11.9 %
13
500
9.9 %
6
150
11.9 %
14
550
8.0 %
7
200
6.2 %
15
600
8.8 %
8
2
50
3.2 %
F
igur
e
6
.
E
C
G
g
r
a
ph
a
na
lys
is
in
we
b
s
e
r
vice
s
T
a
ble
3
is
the
r
e
s
ult
of
tes
ti
ng
ten
tes
t
da
ta
f
r
om
the
de
vice
us
e
d
in
thi
s
s
tudy
,
na
mely
the
e
lec
tr
oc
a
r
dio
gr
a
phy
s
e
ns
or
,
whic
h
c
ompar
e
s
with
medic
a
l
tr
a
ini
ng
da
ta
.
Ac
tual
output
is
the
r
e
s
u
lt
of
the
tr
a
ini
ng
f
r
o
m
the
e
xpe
c
ted
output
.
T
he
pr
e
dict
e
d
output
of
the
de
vice
us
e
d
in
thi
s
s
tudy
is
c
ompar
e
d
with
the
a
c
tual
output
to
f
ind
the
c
los
e
s
t
da
ta
with
the
s
mal
les
t
e
r
r
or
in
one
of
the
tar
ge
t
va
lues
.
T
he
r
e
a
r
e
f
our
out
of
ten
tes
t
da
ta
that
a
r
e
c
las
s
if
ied
a
s
ha
ving
c
a
r
diac
a
bnor
malit
ies
but
ha
ve
not
b
e
e
n
c
onf
ir
med
to
s
uf
f
e
r
f
r
om
ins
om
nia.
T
he
e
r
r
or
va
lue
o
f
th
is
tes
t
is
be
twe
e
n
0.
4
%
a
nd
1
.
2%
.
E
r
r
o
r
mea
n
dif
f
e
r
e
nc
e
be
twe
e
n
a
c
tual
output
a
nd
pr
e
dicte
d
output
.
F
igur
e
7
s
hows
the
c
onc
lus
ions
of
th
e
E
M
G
c
onduc
ted
by
the
B
ioS
ppy
li
b
r
a
r
y.
I
n
F
igur
e
7
,
the
uppe
r
pa
r
t
is
the
im
a
ge
be
f
o
r
e
f
il
ter
ing
by
the
B
ioS
ppy
li
br
a
r
y
while
a
t
the
bot
tom
is
t
he
im
a
ge
that
ha
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
,
Vol.
18
,
No.
3
,
J
une
2020:
14
06
-
14
15
1412
be
e
n
f
il
t
e
r
e
d
by
the
B
ioS
ppy
l
ibr
a
r
y
.
T
a
ble
4
is
th
e
r
e
s
ult
of
tes
ti
ng
ten
tes
t
da
ta
f
r
om
th
e
de
vice
us
e
d
in
th
is
s
tudy,
na
mely
the
e
lec
tr
omyogr
a
phy
s
e
ns
or
,
whi
c
h
c
ompar
e
s
with
medic
a
l
tr
a
ini
ng
da
ta.
Ac
tual
output
is
the
r
e
s
ult
o
f
the
tr
a
ini
ng
f
r
om
the
e
xpe
c
ted
ou
tpu
t.
T
he
pr
e
dicte
d
output
o
f
the
de
vice
us
e
d
in
thi
s
s
tudy
is
c
ompar
e
d
with
the
a
c
tual
outpu
t
to
f
ind
the
c
los
e
s
t
da
ta
with
the
s
malles
t
e
r
r
or
in
one
of
the
tar
ge
t
va
lues
.
T
he
r
e
two
of
ten
tes
t
da
ta
a
r
e
c
las
s
if
ied
e
xpe
r
i
e
nc
ing
tens
e
mus
c
les
but
ha
s
not
be
e
n
c
onf
ir
med
to
s
uf
f
e
r
f
r
om
I
ns
omni
a
.
T
he
e
r
r
or
va
lue
o
f
th
is
tes
t
is
be
twe
e
n
0
.
1%
to
1
.
8%
.
E
r
r
or
mea
n
di
f
f
e
r
e
nc
e
be
twe
e
n
a
c
tual
output
a
nd
pr
e
dicte
d
output
.
T
a
ble
3.
E
C
G
a
na
lys
is
us
ing
a
r
ti
f
icia
l
n
e
ur
a
l
n
e
tw
or
k
No
O
bt
a
in
e
d
D
a
ta
M
e
di
c
a
l
D
e
vi
c
e
D
a
ta
T
r
a
in
T
r
a
in
in
g R
e
s
ul
t
E
r
r
or
C
la
s
s
if
ic
a
ti
on
R
e
s
ul
t
H
e
a
lt
y H
e
a
r
t
H
e
a
lt
y H
e
a
r
t
H
e
a
r
t
T
r
o
ubl
e
A
c
tu
a
l
O
ut
put
P
r
e
di
c
te
d
O
ut
put
1
[
1.1,
1.2,2.4,0
.9,1.3,1.4,1.3,
1.3
,
1.3,1.3]
[
1.5,
1.2,2.4,0.
8,1.5,1
.6,1.7
,
1
.5,1.5,1
.2]
[
1.3,1.4,2.4,1.
0,1.3,
1.4,
1.3,
1.3,1.4,1.4]
[
1.8,
1.
7
,
2.9,1
.2,1.6,1.7,
1.8,
1.7,1.5,1.8]
35
0
0.940,
0.955,
0.981
0.961
0.6%
H
e
a
lt
y H
e
a
r
t
2
[
1.4,1.2,2.4,1
.0,1.3,1.4,1.3,
1.3,1.5,1.5]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,
1.3,1.4,
1.3,
1.3,
1.4,1.4]
[
1.8,1.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.935
0.6%
H
e
a
lt
y H
e
a
r
t
3
[
1.8,1.6,2.9,1
.2,1.6,1.8,1.8,
1.7
,1.6,1.5]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9
,1
.2,1.6
,1.7,1.8
,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.991
1.0%
He
a
r
t
P
r
obl
e
m
4
[
1.6,1.8,2.8,1
.2,1.5,1.6,1.8,
1.7,1.5,1
.
8]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1
.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.
940,
0.9
55,
0.98
1
0.97
1.2%
H
e
a
r
t
P
r
obl
e
m
5
[
1.7,1.7,2.8,1
.2,1.5,1
.6,1.8,
1.7,1.7,1.7]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1.3,
1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.99
0.90%
H
e
a
r
t
Pr
obl
e
m
6
[
1.2,1.
2,1.6,1
.6,2.0,2.3,2.0,
1.4,1.7,1.2]
[
1.5,1.2,2.4,0.
8
,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9,1
.
2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.967
1.2%
H
e
a
lt
y H
e
a
r
t
7
[
1.4,1.4,2.2,2
.4,2.4,1.9,1.4
,
1.4,1.4
,1.3]
[1
.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.
4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.947
0.8%
H
e
a
lt
y H
e
a
r
t
8
[
1.1,1.2,2.4,1
.0,1.3,1.4,1.3,
1.3,1.2,1.4]
[
1.5,1.2,2.4,0.
8,1.5,1.
6,1.7,
1.
5,1.5,1.
2]
[
1.3,1.4,2.4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1
.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.951
0.5%
H
e
a
lt
y H
e
a
r
t
9
[
1.4,1.4,2.3,0
.9,1.3,1.4,1.3,
1.3,1.4,1.2]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1
.3,1.4,1
.3,
1.3,1
.4,1.4]
[
1.8,1.7,2.9,1
.2,1.6,1.7,1.8,
1.7,1.5,1.8]
3
50
0.940,
0.955,
0.981
0.959
0.4%
H
e
a
lt
y H
e
a
r
t
10
[
1.6,1.8,2.8,1
.1,1.6,1.7,1.8,
1.7
,1.6,1.6]
[
1.5,1.2,2.4,0.
8,1.5,1.6,1.7,
1.5,1.5,1.2]
[
1.3,1.4,2.4,1.
0,1.3,1.4,1.3,
1.3,1.4,1.4]
[
1.8,1.7,2.9
,1
.2,1.6
,1.7,1.8
,
1.7,1.5,1.8]
350
0.940,
0.955,
0.981
0.993
1.2%
He
a
r
t
P
r
obl
e
m
F
igur
e
7
.
E
M
G
g
r
a
ph
a
na
lys
is
in
we
b
s
e
r
vice
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
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M
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KA
T
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omm
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omput
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C
ontr
ol
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analys
is
bas
e
d
on
int
e
r
ne
t
of
thi
ngs
us
in
g
e
lec
tr
oc
ar
diogr
aph
y
and
...
(
N
ov
i
A
z
man)
1413
T
a
ble
4.
E
M
G
a
n
a
lys
is
us
ing
a
r
t
if
icia
l
n
e
ur
a
l
n
e
tw
or
k
No
O
bt
a
in
e
d
D
a
ta
M
e
di
c
a
l
D
e
vi
c
e
D
a
ta
T
r
a
in
T
r
a
in
in
g R
e
s
ul
t
E
r
r
or
C
la
s
s
if
ic
a
ti
on
R
e
s
ul
t
R
e
la
x
R
e
la
x
T
e
ns
e
A
c
tu
a
l
O
ut
put
P
r
e
di
c
te
d
O
ut
put
1
[
1.1,1.
3
,
1
.
9
,
2
.
4
,
2
.
4
,
2
.
3
,1.
9
,
1.3,1.3,1.
7
]
[
1.
8
,1.
7
,2.
1
,
2
.
4
,
2
.
4
,
2
.
0
,1.
8
,
1
.
7
,1.
8
,1.
7
]
[
1.
3
,1.4,2.2,2.
4,2.4,1.
9
,1.3,
1.3,1.
3
,1.
3
]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.91
0.1%
R
e
la
x M
u
s
c
le
2
[
1.8,1.8,2.0,2
.6,2.3,2.2,1.8,
1.7,
1.8,1.7]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,
1
.3,
1.3
,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.929
1.0%
R
e
la
x M
u
s
c
le
3
[
1.4,1.4,2.2,2
.3,2.4,1.7,1.3,
1.3,1.3,1.3]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8
,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8
,
2.5,1.
8,
1.7,1.8
,
1.7]
350
0.938,
0.919,
0.983
0.92
1.0%
R
e
la
x M
u
s
c
le
4
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9
,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919
,
0.983
0.98
0.6
%
T
e
ns
e
M
us
c
le
5
[
1.9,1.7,2.0,2
.7,2.8,2.5,1.9,
1.7,1.8,1.7]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8
,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.993
0.7%
T
e
ns
e
M
us
c
le
6
[
1.
4
,1.4,1
.6,1
.8,2.
0
,2.4,2.0,
1.4,1.7,1.2]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.91
9,
0.983
0.908
1.2%
R
e
la
x M
u
s
c
le
7
[
1.3,1.4,2.2,2
.4,2.4,1.9,1.4,
1.5,1.3,1.4
]
[
1.8,
1.7,2.1,2
.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.922
0.3%
R
e
la
x M
u
s
c
le
8
[
1.7,1.8,2.0,2
.2,2.4,2.3,1.8,
1.8,1.7,1.7]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.
8
,1.7]
[
1.3,1.4,
2
.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.934
0.5%
R
e
la
x M
u
s
c
le
9
[
1.0,1.1,1.8,2
.2,2.3,2.0,1.7,
1.3,1.3,1
.3]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1
.
3,1.3,
1.3]
[
1.8
,
1.7,1.8,2
.7,2.8,2.5,1.8,
1.7,1.8,1.7]
350
0.938,
0.919,
0.983
0.902
1.8%
R
e
la
x M
u
s
c
le
10
[
1.6,1.8,1.6,1
.8,2.0,2.4,2.0,
1.8,1.7,1.7]
[
1.8,1.7,2.1,2.
4,2.4,2.0,1.8,
1.7
,1.8,1.7]
[
1.3,1.4,2.2,2.
4,2.4,1.9,1.3,
1.3,1.3,1.3]
[
1.8,1.7,1.8,2
.7,2.8,2.5
,
1.8,
1.
7,1.8,1.7
]
350
0.938,
0.919,
0.983
0.928
1.1%
R
e
la
x M
u
s
c
le
T
a
b
l
e
5
i
s
t
h
e
r
e
s
u
l
t
o
f
t
e
s
t
i
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g
t
e
n
t
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s
t
d
a
t
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m
t
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d
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v
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c
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u
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e
d
i
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t
h
i
s
s
t
u
d
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,
n
a
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e
l
y
t
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E
l
e
c
t
r
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c
a
r
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i
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a
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w
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r
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t
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p
l
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C
las
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if
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r
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in
we
b
s
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r
vice
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
ol
,
Vol.
18
,
No.
3
,
J
une
2020:
14
06
-
14
15
1414
T
a
bl
e
5.
I
ns
omni
a
c
las
s
if
ica
ti
on
us
ing
a
r
ti
f
icia
l
ne
ur
a
l
ne
twor
k
No
T
r
a
in
T
r
a
in
in
g R
e
s
ul
t
E
r
r
or
C
la
s
s
if
ic
a
ti
on R
e
s
ul
t
A
c
tu
a
l
O
ut
put
P
r
e
di
c
te
d
O
ut
put
1
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
63.2
2.7%
N
on i
ns
omni
a
2
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
6
0.9
1.5%
N
on i
ns
omni
a
3
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
44.1
2.0%
P
e
r
s
on w
it
h i
ns
omni
a
4
350
[
65]
, [
60]
,
[
55]
,
[
50
]
, [
45]
,
[
40]
,
[
35]
39.8
0.5%
P
e
r
s
on w
it
h i
ns
omni
a
5
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
40.4
1.0%
P
e
r
s
on w
it
h i
ns
omn
ia
6
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
54.2
1.5%
N
on i
ns
omni
a
7
350
[6
5]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
59.
1
1.5%
P
e
r
s
on w
it
h
in
s
omni
a
8
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
58.9
1.9%
N
on i
ns
omni
a
9
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
, [
35]
60.7
1.1%
N
on i
ns
omni
a
10
350
[
65]
, [
60]
,
[
55]
, [
50
]
, [
45]
,
[
40]
,
[
35]
40.1
0.2%
P
e
r
s
on w
it
h i
ns
omni
a
4.
CONC
L
USI
ON
B
a
s
e
d
on
the
r
e
s
ult
s
obtaine
d
in
thi
s
s
tudy,
we
c
a
n
c
onc
lude
the
f
oll
owing.
S
ignal
s
tr
e
ngth
is
ve
r
y
inf
luential
in
s
e
nding
da
ta
s
e
nt
f
r
om
the
mi
c
r
o
c
ontr
oll
e
r
to
the
s
e
r
ve
r
.
W
he
r
e
in
thi
s
s
tudy,
th
e
r
e
s
ult
s
obtaine
d
with
the
s
ignal
s
tr
e
ngth
of
-
81
dB
m
t
o
-
97dB
m
to
ge
t
100%
a
c
c
ur
a
c
y
o
f
da
ta
t
r
a
ns
mi
s
s
ion.
W
hil
e
tes
ti
ng
a
t
-
108
dB
m
ge
ts
a
n
a
c
c
ur
a
c
y
a
bove
97%
.
F
r
om
the
a
na
lys
is
of
pa
ti
e
nt
da
ta
obtaine
d
,
it
take
s
a
bout
350
tr
a
ini
ng
da
ta
,
whic
h
pr
oduc
e
s
th
e
s
mall
e
s
t
e
r
r
o
r
.
P
r
e
diction
r
e
s
ult
s
f
r
om
10
E
M
G
s
e
ns
or
tes
t
da
ta,
ther
e
a
r
e
2
out
o
f
10
da
ta
that
s
uf
f
e
r
f
r
om
tens
e
mus
c
les
.
T
he
r
e
s
ult
ing
a
c
c
ur
a
c
y
leve
l
is
100
%
,
with
the
mos
t
s
ig
nif
ica
nt
e
r
r
or
va
lue
of
1.
8
%
,
a
nd
the
s
malles
t
e
r
r
or
is
0.
1%
.
P
r
e
diction
re
s
ult
s
f
r
om
10
E
C
G
s
e
ns
or
tes
t
da
ta,
ther
e
a
r
e
4
out
o
f
10
da
ta
that
s
uf
f
e
r
f
r
o
m
he
a
r
t
pr
oblems
.
T
he
r
e
s
ult
ing
a
c
c
ur
a
c
y
leve
l
is
100%
,
with
the
mos
t
s
igni
f
ica
nt
e
r
r
or
va
lue
o
f
1.
2
%
,
a
nd
t
he
s
malles
t
e
r
r
or
is
0.
4
%
.
T
he
a
c
c
ur
a
c
y
of
c
las
s
if
ic
a
ti
on
of
pe
ople
w
it
h
ins
o
mni
a
with
ne
ur
a
l
ne
twor
k
r
e
a
c
h
e
s
100%
.
T
he
r
e
a
r
e
4
out
of
10
da
ta
that
a
r
e
pr
e
dicte
d
to
s
uf
f
e
r
f
r
om
ins
omni
a
.
T
he
s
malles
t
e
r
r
o
r
va
lue
is
0.
2%
,
a
nd
the
mos
t
s
igni
f
ica
nt
e
r
r
o
r
va
lue
is
2.
7%
.
W
it
h
thes
e
r
e
s
ult
s
,
the
diagnos
is
of
ins
omni
a
us
in
g
our
s
ys
tem
in
thi
s
s
t
udy
c
a
n
pr
ovide
a
s
olut
ion
to
make
a
r
e
mot
e
ins
omni
a
d
iagnos
is
that
is
mor
e
c
os
t
e
f
f
e
c
ti
ve
a
nd
les
s
time
-
c
ons
umi
ng.
F
or
f
u
r
ther
s
tud
ies
,
it
is
r
e
c
omm
e
nde
d
to
us
e
ot
he
r
biom
e
dica
l
s
e
ns
or
s
that
a
r
e
mor
e
c
ompl
e
te
a
nd
mo
r
e
e
qua
l
than
the
gol
d
s
tanda
r
d
f
or
tes
t
ing
ins
omni
a
s
uc
h
a
s
a
dding
medic
a
l
s
e
ns
or
s
E
E
G,
E
OG
,
a
nd
o
ther
medic
a
l
s
e
ns
or
s
that
c
a
n
e
qua
l
s
pe
c
if
ica
ti
on
with
p
olys
omnogr
a
phy
de
vice
.
T
he
s
e
r
e
s
ult
s
a
ppe
a
r
in
a
we
b
-
ba
s
e
d
f
or
m
with
the
h
ope
that
thes
e
r
e
s
ult
s
c
a
n
r
e
a
c
h
pa
ti
e
nts
in
r
e
m
ote
l
oc
a
ti
ons
a
nd
do
c
tor
s
in
lar
ge
c
it
ies
c
a
n
s
e
e
da
ta
f
r
om
pa
ti
e
nts
in
or
de
r
to
be
a
ble
to
r
ight
tr
e
a
tm
e
nt
.
RE
F
E
RE
NC
E
S
[1
]
Sp
i
eg
el
K
.
,
K
n
u
t
s
o
n
K
.
,
L
ep
ro
u
l
t
R
.
,
T
as
a
l
i
E
.
,
Cau
t
er
E
.
V
an
,
“
Sl
eep
l
o
s
s
:
a
n
o
v
e
l
ri
s
k
fact
o
r
fo
r
i
n
s
u
l
i
n
res
i
s
t
a
n
ce
an
d
T
y
p
e
2
d
i
a
b
et
e
s
,
”
J
A
pp
l
P
h
ys
i
o
l
,
v
o
l
.
99
,
n
o
.
5
,
p
p
.
2
0
0
8
-
2
0
1
9
,
2
0
0
5
[2
]
Bro
o
k
s
D
.
,
H
o
r
n
er
R
.
L
.
,
K
o
zar
L
.
F
.
,
Ren
d
er
-
T
ei
x
e
i
ra
C
.
L
.
,
Ph
i
l
l
i
p
s
o
n
E
.
A
,
“
O
b
s
t
ru
c
t
i
v
e
s
l
ee
p
ap
n
ea
as
a
cau
s
e
o
f
s
y
s
t
em
i
c
h
y
p
er
t
en
s
i
o
n
E
v
i
d
en
ce
fr
o
m
a
can
i
n
e
mo
d
el
,
”
J
Cl
i
n
In
ve
s
t
,
v
o
l
.
99
,
n
o
.
1
,
p
p
.
10
6
-
1
0
9
,
1
9
9
7
.
[3
]
L
i
u
X
.
,
L
i
u
L
.,
“
Sl
eep
h
a
b
i
t
s
a
n
d
i
n
s
o
m
n
i
a
i
n
a
s
am
p
l
e
o
f
e
l
d
erl
y
p
er
s
o
n
s
i
n
Ch
i
n
a
,
”
S
l
ee
p
,
v
o
l
.
28
,
n
o
.
12
,
pp.
1
5
7
9
-
1
5
8
7
,
2
0
0
5
.
[4
]
L
i
u
X
.
,
L
i
u
L
.
,
O
w
en
s
J
.
A
.
,
K
ap
l
a
n
D
.
L.
,
“
Sl
eep
p
at
t
e
rn
s
an
d
s
l
eep
p
r
o
b
l
ems
amo
n
g
s
ch
o
o
l
ch
i
l
d
ren
i
n
t
h
e
U
n
i
t
e
d
St
at
e
s
an
d
C
h
i
n
a
,
”
P
ed
i
a
t
r
i
cs
,
v
o
l
.
1
1
5
,
n
o
.
Su
p
p
l
eme
n
t
1
,
p
p
.
2
4
1
-
2
4
9
,
2
0
0
5
.
[5
]
Xia
n
g
Y
.
T
.
,
Ma
X
.
,
Cai
Z
.
J
.
,
L
i
S
.
R
.
,
X
i
an
g
Y
.
Q
.
,
G
u
o
H
.
L
.
,
et
al
.
,
“
T
h
e
p
rev
al
en
ce
o
f
i
n
s
o
mn
i
a,
i
t
s
s
o
c
i
o
d
emo
g
rap
h
i
c
a
n
d
c
l
i
n
i
ca
l
co
r
rel
a
t
es
,
an
d
t
reat
me
n
t
i
n
r
u
ral
a
n
d
u
r
b
a
n
re
g
i
o
n
s
o
f
Be
i
j
i
n
g
,
Ch
i
n
a
:
a
g
e
n
eral
p
o
p
u
l
at
i
o
n
-
b
a
s
ed
s
u
rv
e
y
,
”
S
l
ee
p
,
v
o
l
.
31
, n
o
.
12
,
p
p
.
1
6
5
5
-
1
6
6
2
,
2
0
0
8
.
[6
]
A
s
g
h
ar
i
A
.
,
Farh
ad
i
M
.
,
K
amrav
a
S
.
K
.
,
G
h
al
eh
b
a
g
h
i
B.
,
“
Su
b
j
e
ct
i
v
e
s
l
ee
p
q
u
a
l
i
t
y
i
n
u
rb
a
n
p
o
p
u
l
at
i
o
n
,
”
A
r
c
h
Ir
a
n
M
ed
,
v
o
l
.
15
,
n
o
.
2
,
p
p
.
95
-
98
,
2
0
1
2
.
[7
]
Z
ai
l
i
n
aw
a
t
i
A
.
H
.
,
Mazza
D
.
,
T
en
g
C
.
L.
,
“
Prev
al
en
c
e
o
f
i
n
s
o
m
n
i
a
a
n
d
i
t
s
i
m
p
act
o
n
d
ai
l
y
f
u
n
c
t
i
o
n
am
o
n
g
s
t
Mal
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0
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[2
2
]
L
i
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T
.
H
.
Y
.
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o
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.
J.
,
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[2
4
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u
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.
,
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.
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á
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.
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Mu
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10
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