A Comparison of Backpropagation and LVQ : a case study of lung sound recognition
ICACSIS 201-1zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A Comparison of Backpropagation and LVQ : a case
study of lung sound recognition
Fadhilah Syafria', Agus Buono'' and Bib Paruhum Silalahi3zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQ
Department of Computer Science and 3 Departement of Mathematics
J: zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Faculty of Mathematics and Natural Sciences Bogor Agricultural University
Bogor. Indonesia
Email: ·[email protected]@[email protected]
sounds are sounds
Abstract=Cnie
lungs
is
by
stethoscope.
low
sounds
factors.
be
human
made
alrnost
method
used
of lung
addition
to
recognition
Cepstrum
as method
is
Auscultation
of
by
to
technique
of
the
of these
to
In this research,
will
ability.
auditory,
with
perfonned
neural
and
and
accuracy
the method
(MFCC)
in
recommend
in the case of lung sounds.
In
results
almost
between
similar
Because
sound
is
of breathing
The next problem
are
the
the pattern
problem
of
of sounds
that
one type of breath
if the procedure
the
who
human hearing
the
of these
on
sound is also a problem
frequency.
and
of breath
relies
of the doctor
In addition,
techniques
noise
a subjective
technique
because
auscultation
of
This
but it has
of the analysis
to low frequency
environmental
occur
the
experience
a low enough
the other.
of Mei
is also used
The results
and
that is used
sounds.
and inexpensive,
results
the analysis.
occupies
breath
auscultation
in th is technique,
learning
of these two
recognition
The
less sensitive
network
that
This
techniques.
is a basic technique
evaluate
is by
a stethoscope.
auscultation
is fairly simple
disadvantage
of the lungs
using
as
technique
physicians
[2].
recognition
the state
sounds
known
using
determine
extraction.
technique
sounds
(BP)
better
to breath
analysis
sound
Coefficient
of feature
listening
of
to the above two methods.
Frequency
panern
lung disorder.
way to evaluate
procedure
if
Arti ficial
provide
One
but it
results
and
(L VQ). Comparison
performed
of speech
using
Because
is Backpropagation
which
sounds
the
noise
occur
approach.
Vector Quantization
of the
is less sensitive
similar.
can
a model
network
algorithms
hearing
is not done properly.
neural
methods
are
em ironmental
that
state
and inexpensive,
They
misdiagnosis
auscultation
breath
is fairly simple
analysis.
the
is knO\\TI as auscultation.
disadvantage.
frequency,
lung
to
This technique
sorne
subjective
to evaluate
listening
This technique
has
way
factors,
sounds
and
misdiagnosis
of auscultation
can
is not done
properly.
show the
accuracy of using Backpropagation
is 93.17%, while
the value of using the L VQ is R6.R8%. It can be
Based on the above problems.
we interested
in
conducting
research in perfonning
speech recognition
concluded
of
that the introduction
Backpropagation
compared
method
of lung sounds
gives
to the L VQ method
better
using
perfonnance
for speech
recognition
cases of lung sounds.
normal
lung
and
lung
interference
(abnormal),
some
of the disease
that
cases
can
be recognized.
taken as material
sounds
Sounds
showed
This
were
a specific
sound
for diagnosis
Lung
sounds
Respiratory
trachea
are
sounds
while
lung
part
of the
include
respiratory
occur
arnund
the respiratory
turbulence
occurs
tract during
because
duct that is wider to narrower
ln general.
sounds.
the chest.
Sounds of lungs occurs due to air turbulence
air enters
indicates
breathing.
the air flow
airways
when the
from
This
the air
or vice \ ersa.
the sound of the lung is divided
into two.
.
Voice
partern
whilc
abnormalities
recognition
or identified
advance
to be able
cIassifies
recognition
A system
is
a
to
used
computational
or verify the sound
need to be trained
to recognize
the voice
the
is needed
system to perfonn
sound
and whceze
in order to the voice
systems
according
an algorithrn
vesicular.
so that it can be
system
These
be
in the lungs [4][5].
is an effort
that is able to identify
is autornatically.
can
vcsicular
the crackle
applicarion
respiratory
lung
lung.
and
rccognition
purposcs.
abnormal
Tracheal
Avoice
voice
whcrcas
nornal
can be recognized
sounds is the sound of the lungs that are not detectable
abnormality.
wheeze.
sound indicates
the sound of the mouth and
sounds
and
in
pattern
[3]. Lung sound will be
I.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I;\TRODlT 110;-':
cIassified into four classes, narnely tracheal.
crackle
detected
of lungs resulting
class.
and
For
in
the
such
to train the speech
more prcc ise .
can lcarn and recognize
pauerns
of sound
ICACSIS 2014
can be done with the application of artificial neural
Start
nerworks. Artificial neural network is a computarional
~~--)
method that mimics the biologicai neural network of
Uteratcre
review
human that capable trained to solve problems. In
lu", sound theory
MFCC
I
general, there are several artificial neural network
Backpropagation and LVQ 1
algorithms that can be appJied to partern recognition.
MATLAB
!
-------,,------'
This research will use two artificial neural network
algorithms, they are Backpropagation
(BP) and
Data ecquuinon .
Learning Vector Quantization (LVQ). Both methods
Lun, sound
have advantages
and disadvantages
of each.
Comparison of these two methods performed to
Segrnentaucn
determine and recommend algorithrns which provide
bener recognition accuracy of speech recognition in
the case of lung sounds.
Trajnlng Data
ln addition to using an artificial neural network
algorithm in the classification process, it is no less
Feature ertracuon
. !
Feature extr~:
important in voice recognition is the feature extraction.
MFCC
MfCC
ln this study, feature extraction methods are used Mei
Frequency Cepstrum Coeffisient (MFCC). MFCC is a
Establlsh the model
(8ackpropagation
~nd l VQ ~
feature extraction method which gives excellent results
in classifying normal lung sounds and wheeze soundszyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
Model testmg
[6J.
t
---r---
II.
RESEARCH
METHOD
Companng.
Anal!sys. dan
. - "r--discuanon
Research method conducted is illustrated in Figure
I. In the image looks that research begins with a
literature review of the theories necessary for the
completion of this study, such as lung sound theory.
MFCC.
Backpropagation.
LVQ and MATLAB
progranuning.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A.
Materials Research
Documentancn
I
J
and
repcrts
Figur~ I. Research Method
Lung sound data obtained from the repository of
respiratory sounds on the Internet, namely Linmann
Wlwe::es sound is a type of sound that IS cominuous,
Repository. The sound data consist of 32 lung sound
have ii high pitch (Fig. 2d). and more often heard on
data. which is divided into 8 tracheal sounds. 8
the expiratory. This sound occurs when the flow of air
vesicular sounds. 8 crackle sound and 8 wheeze sound.
narrowcd
airways due to secretions.
through
the
Tracheal sound is a sound that is audible at the base
foreign body or wounds that preclude
[3]. The
of the neck and larynx. Tracheal sound is very loud
duration of the process of inspiration longer than the
and his pitch is high (Fig. 2a). 50 this sound is very
cxpiratory process. Besides that inspiring sound
clear sounding compared to another normal lung.
intensity greater than the expiratory. The conditions
Inspiration and expiratory are relatively equal in
that is cause the wliee:e are asthma. CIIF, chronic
length [7J.
hronchitis, COPD dan pulm onarv cdcma [9].
Vesicular sounds are normal breath sounds that are
heard on the side chest and chest near the stomach.
The sounds ware soft with a low pitch (Fig. 2b).
Inspiration sounds much stronger than expiratory
~I
I
sounds, Often the expiratory process is hardly audible
(7]
i..
a. Tracheal
b.Vesicular
.
Crack/es sounds are a short burst sound that is
discontinuous (Fig. lc). it is generally more audible
; I
sound m the process of inspiration l,] The conditions
~II
that is cause the crackles are ARDS, asthma,
bronchiectosis. chronic bronchitis. consolidation.
hlllg disease dan pulnionarv
carly CflF. nterstitialzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
edem a [9J.
l.~.~~~II ~"t.
I
II
Il
II
d. Crackle
d
\\'h(,Graph of accuracy Based on l.enrning' Rate
• BV (! e~tl~
U
91 %
96 -13
3
97..'2
96 73
15
);988
94.94
9H)5
95.l'\3
9643
OaL))
----.~
8/.41
93.1 J
94.42
-
9~.16
-
~
ln contrast. in Backpropagation,
increased learning
rate rends to increase the accuracy. This is due to the
91.96
94.9-1
80.36
89.5R
95.24
30
algorithm Backpropagation
just changing the network
weights by summing up the weighis (without the
C. Effect ofXutnbc:zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
o{,\fFC C Coefficient Against
nature of the competition) so that the network weight
Accllracl"
rernains stable.
From the cntire experiment. the average accuracy
E. lntroductorv
Rate of Each LIII/g Sounds
obtained at each value of the coefficient is shown in
A
verage
of
accuracy
of the entire experiment based
Figure 5. From the graph it can be seen that there is a
on the type of lung sounds is shown in Figure 7. From
tendency that the higher the coefficient MFCC makes
the graph it can be seen that the type of tracheal sound
111
L VQ
and Backpropagation.
lower accuracy
has the highest accuracy. rncaning that most can be
although the decrease was not significant. Accuracy at
distinguished
from other types of sound. [t is. as
MFCC coefficient
13 tend to be higher th an the
mentioned previously (Figure 7) that a tracheal sound
accuracy of the MFCC coefficient greater. This is due
signals has the form the most different than the othcr
to the higher coefficient of MFCC impact on the
thrce types of sound. Meanwhile,
the sound of
dimensions of a larger data. Largc data dirncnsions
vesicular
type. crackle.
and wheezc
has little
make the generalization capability of A\,1\,1 is lowcr so
resemblance so that iIS accuracy is lower. meaning that
the accuracy is dccrcased.
the sound of vesiculer is sornetimes identified as a
D. E[{cct o] Learning Rate Against Accllrac\'
crackle type or whecze and vice versa,
lnfluence
of the learning rate throughout
the
cxpcrirnent to the average accuracy is shown in Figure
n. From the graph. in L VQ there is a tendcncy that
20.·
87.5
91 91>
93.75
'15.54
9-1.35
to I
IS13:\
!J7x-1)7~)-1121-22.-)
ICACSIS 201-1zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Graph of accuracy of eadi type sound
100
98
96
94
---~~-------
of L YQ, In addition, tracheal sound can be recognized
better than the lung sound types vesicular, Crackle,
and wheeze.
92
90
REFERENCES
88
86
84
82
80
A. Cohen.
D, Landsberg,
"Analysis and automatic
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Suara
Paru
Menggunakan
Spektogram
dan
K-Mean
Figure 7 Graph of accuracy in Each Type SoundzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Clusrering", Proceeding SI TIA 20 I O. Februari 20 I 0
[4] AA, Abaza. 18. Day. JS. Reynolds. AM. Mahmoud. WT
Goldsmith,
WG, McKinncy.
EL Petsonk. DG. Frazer,
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"Classification of voluntary cough sound and airflow patterns
and L VQ Method
for detecting abnormal pulrnonary function". Cough. 5(8).
2009,
Comparison
of the accuracy
of LYQ and
[5] A. Gurung. CG. Scrafford. JM. Tielsch. OS. Levine. w.
Backpropagation
method in identifying lung sounds is
Checkley
\Ii,
"Computerized
lung sound analysis
as
presented
in Figure 8, Overall, backpropagation
diagnostic aid for the detection of abnormal lung sounds: a
method gives better accuracy results than L YQ
systernatic review and meta-analysis".
Respir Med, 105(9):
1396-1403,20 II.
method, with an overall average accuracy of 94,58%
f6] M. Bahoura, "Pauern
Recognition
Methods Applied to
for training data and 93,17% for test data, while L VQ
Respiratory Sounds Classification into Normal and Wheeze
only offer results of R7,83% for training data and
Classes. Computcrs and Biolog- and ~kdiClne. 39(9):82486,88% for test data, Testing the training data provide
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[71 A. Rizal. L Anggraeni, V Suryani, "Pengenalan Suara Parugreater accuracy results because the data is also used
Paru Normal Menggunakan LPC dan Jaringan Syaraf Tiruan
in the training process, while the test data were not
Back-Propagatiou'.
Proceeding EECClS2006, Mei 2006.
included during training,
-~-~--,
[81 T. Katila. P Piirila. K. Kallio. E, Paajanen. T. Rosqvist. AR,
Sovijarvi, "Original waveform of lung sound crackles: a case
Comparison of the accuracy of lVQ method and BP
study of the effect of high-pass filtration" Journal of Applied
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Phvsiologv. 7/(6),2173-2177,
t991.
98
[t]
r--96
g
~
ee
;:
9'
92
90
AA
86
84
8}
80
_ tIJ Q
• BP
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~lZ. Ramadhan, "Perancangan
Sistem Instrumentasi untuk
Identifikasi dan Analisis Suara Paru-Paru Menggunakan DSP
H1S320CMI6T
[Essay]",
lkpok[IDI
Universitas
lndonesia, 2012.
1101 K. Patcl, KK. Prasad. "Spccch Rccognition and Vcrification
Using MFCC & "Q". Intcrntional Journal. 20) 3,
[111 A. Buono. "Representasi nilai hos dan model MFCC sebagai
Ir ainmg O.lh zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
T(>$tng O.t.1 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGF
ekstraksi
ciri pada sistem
indentifikasi
pembicara
di
8/,877,)
8 6 ,8 /')
lingkungan
ber-noiscmcnggunakan
HMM [disscrtation [".
93.17l5
94,5775
Depok: Program Studi llmu Komputer. Universitas lndonesia .
Figure 8 Comparison of Accuracy graphs of LVQ and
Backpropagation
Although L YQ method gives lower accuracy, but
the method is simpler than computing aspects 50 that
training time is faster than the Backpropagation
method, This is consistent with studies conducted
gives
Hawickhorst ef al. (1995) that Backpropagation
berter accuracy
results than LYQ. but higher
computational complexity [18],
tv.
CO~CLL:SIO~
From the research th at has been done. it can be
concluded that it has made a model of lung sound
recogniuon
with
MFCC
feature
extraction.
Classification with LVQ and Backpropagation
method
can be applied for the idenrification of lung sounds. by
adjust paramcrers MFCC coefficient and learning rate
that maximizes the accuracy, Backpropagation
method
has bener accuracy
than LVQ with all ayeragc
accuracy on the test data was 93.17n o. whilc Ro.RRO n
2009.
f 121 L. Muda, M, Begam. L Elamvazuthi. "Voice Rccogniiion
Algoriihms
Using Mei Frcquencv
Ccpstral
Cocfficicnt
IMFCC) and Dynamic Tune Warping IDTW) Tcchniqucs",
Journal of Cornpuring. Volume 2, lssuc J, 2010.
[131:--'1.
Slancy.
"Auditory
Toolbox
Interval
Research
Corporation". Tech. Rcp, ID, 1998,
r 141 F. Suhandi, "Prediksi Harga Saham dcngan Pcndckatan
Anificial
Ncural
1\ el work mcnacunakan
Algoritma
Backpropagarion", 2009.
[151 L. Fausett. "Fundamcnrals of ncural nctworks architcctures
algorithms and applications". F lo rid a l.atitudc of Technologv,
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11(>1 S, Kusumadewi.
S. Hartati. "lntcgras: SIStem Fuzzy dan
Jaringan Syaraf', Yogyakana. (iraha llmu, ~O I O.
1171 L, Rahadianu.
"Pengembangan
Algoritma
Pembelajaran
Berbasis Dimensi serta Kornparasinva rcrhadao Pembelajaran
F II::\ -Xcuro
Learning
Berbasiskan
Vektor
rada
'· l'd o rQ IIO llfi:O l/o 1 l
untuk Pengenalan Citra \\-3)3h Frontal
! Essa\ [". Depok [IDI Fa-rlkorn l.nivcrsuas lndoncsra. ~009
[I XI BA Hawickhorst.
S:\
Zahonan.
K
Rajagopal.
..:\
Comparison
of Thrcc Ncural :--'-ctwork Architccrurcs
for
Auiornauc Specch Rccognition"
DI dalam Dagli CH, el al.
editor Procccdmgs of Ihe Aruficiol Xcurol Xctw orks in
1 :-"g m C (,f'ln g
[191
,1
r
lIl2
.
.-I.\"S II:"'95. ~'cw York
:\S\1E
Press. hlm ~~1·"
ICACSIS 201-1zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A Comparison of Backpropagation and LVQ : a case
study of lung sound recognition
Fadhilah Syafria', Agus Buono'' and Bib Paruhum Silalahi3zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQ
Department of Computer Science and 3 Departement of Mathematics
J: zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Faculty of Mathematics and Natural Sciences Bogor Agricultural University
Bogor. Indonesia
Email: ·[email protected]@[email protected]
sounds are sounds
Abstract=Cnie
lungs
is
by
stethoscope.
low
sounds
factors.
be
human
made
alrnost
method
used
of lung
addition
to
recognition
Cepstrum
as method
is
Auscultation
of
by
to
technique
of
the
of these
to
In this research,
will
ability.
auditory,
with
perfonned
neural
and
and
accuracy
the method
(MFCC)
in
recommend
in the case of lung sounds.
In
results
almost
between
similar
Because
sound
is
of breathing
The next problem
are
the
the pattern
problem
of
of sounds
that
one type of breath
if the procedure
the
who
human hearing
the
of these
on
sound is also a problem
frequency.
and
of breath
relies
of the doctor
In addition,
techniques
noise
a subjective
technique
because
auscultation
of
This
but it has
of the analysis
to low frequency
environmental
occur
the
experience
a low enough
the other.
of Mei
is also used
The results
and
that is used
sounds.
and inexpensive,
results
the analysis.
occupies
breath
auscultation
in th is technique,
learning
of these two
recognition
The
less sensitive
network
that
This
techniques.
is a basic technique
evaluate
is by
a stethoscope.
auscultation
is fairly simple
disadvantage
of the lungs
using
as
technique
physicians
[2].
recognition
the state
sounds
known
using
determine
extraction.
technique
sounds
(BP)
better
to breath
analysis
sound
Coefficient
of feature
listening
of
to the above two methods.
Frequency
panern
lung disorder.
way to evaluate
procedure
if
Arti ficial
provide
One
but it
results
and
(L VQ). Comparison
performed
of speech
using
Because
is Backpropagation
which
sounds
the
noise
occur
approach.
Vector Quantization
of the
is less sensitive
similar.
can
a model
network
algorithms
hearing
is not done properly.
neural
methods
are
em ironmental
that
state
and inexpensive,
They
misdiagnosis
auscultation
breath
is fairly simple
analysis.
the
is knO\\TI as auscultation.
disadvantage.
frequency,
lung
to
This technique
sorne
subjective
to evaluate
listening
This technique
has
way
factors,
sounds
and
misdiagnosis
of auscultation
can
is not done
properly.
show the
accuracy of using Backpropagation
is 93.17%, while
the value of using the L VQ is R6.R8%. It can be
Based on the above problems.
we interested
in
conducting
research in perfonning
speech recognition
concluded
of
that the introduction
Backpropagation
compared
method
of lung sounds
gives
to the L VQ method
better
using
perfonnance
for speech
recognition
cases of lung sounds.
normal
lung
and
lung
interference
(abnormal),
some
of the disease
that
cases
can
be recognized.
taken as material
sounds
Sounds
showed
This
were
a specific
sound
for diagnosis
Lung
sounds
Respiratory
trachea
are
sounds
while
lung
part
of the
include
respiratory
occur
arnund
the respiratory
turbulence
occurs
tract during
because
duct that is wider to narrower
ln general.
sounds.
the chest.
Sounds of lungs occurs due to air turbulence
air enters
indicates
breathing.
the air flow
airways
when the
from
This
the air
or vice \ ersa.
the sound of the lung is divided
into two.
.
Voice
partern
whilc
abnormalities
recognition
or identified
advance
to be able
cIassifies
recognition
A system
is
a
to
used
computational
or verify the sound
need to be trained
to recognize
the voice
the
is needed
system to perfonn
sound
and whceze
in order to the voice
systems
according
an algorithrn
vesicular.
so that it can be
system
These
be
in the lungs [4][5].
is an effort
that is able to identify
is autornatically.
can
vcsicular
the crackle
applicarion
respiratory
lung
lung.
and
rccognition
purposcs.
abnormal
Tracheal
Avoice
voice
whcrcas
nornal
can be recognized
sounds is the sound of the lungs that are not detectable
abnormality.
wheeze.
sound indicates
the sound of the mouth and
sounds
and
in
pattern
[3]. Lung sound will be
I.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I;\TRODlT 110;-':
cIassified into four classes, narnely tracheal.
crackle
detected
of lungs resulting
class.
and
For
in
the
such
to train the speech
more prcc ise .
can lcarn and recognize
pauerns
of sound
ICACSIS 2014
can be done with the application of artificial neural
Start
nerworks. Artificial neural network is a computarional
~~--)
method that mimics the biologicai neural network of
Uteratcre
review
human that capable trained to solve problems. In
lu", sound theory
MFCC
I
general, there are several artificial neural network
Backpropagation and LVQ 1
algorithms that can be appJied to partern recognition.
MATLAB
!
-------,,------'
This research will use two artificial neural network
algorithms, they are Backpropagation
(BP) and
Data ecquuinon .
Learning Vector Quantization (LVQ). Both methods
Lun, sound
have advantages
and disadvantages
of each.
Comparison of these two methods performed to
Segrnentaucn
determine and recommend algorithrns which provide
bener recognition accuracy of speech recognition in
the case of lung sounds.
Trajnlng Data
ln addition to using an artificial neural network
algorithm in the classification process, it is no less
Feature ertracuon
. !
Feature extr~:
important in voice recognition is the feature extraction.
MFCC
MfCC
ln this study, feature extraction methods are used Mei
Frequency Cepstrum Coeffisient (MFCC). MFCC is a
Establlsh the model
(8ackpropagation
~nd l VQ ~
feature extraction method which gives excellent results
in classifying normal lung sounds and wheeze soundszyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
Model testmg
[6J.
t
---r---
II.
RESEARCH
METHOD
Companng.
Anal!sys. dan
. - "r--discuanon
Research method conducted is illustrated in Figure
I. In the image looks that research begins with a
literature review of the theories necessary for the
completion of this study, such as lung sound theory.
MFCC.
Backpropagation.
LVQ and MATLAB
progranuning.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A.
Materials Research
Documentancn
I
J
and
repcrts
Figur~ I. Research Method
Lung sound data obtained from the repository of
respiratory sounds on the Internet, namely Linmann
Wlwe::es sound is a type of sound that IS cominuous,
Repository. The sound data consist of 32 lung sound
have ii high pitch (Fig. 2d). and more often heard on
data. which is divided into 8 tracheal sounds. 8
the expiratory. This sound occurs when the flow of air
vesicular sounds. 8 crackle sound and 8 wheeze sound.
narrowcd
airways due to secretions.
through
the
Tracheal sound is a sound that is audible at the base
foreign body or wounds that preclude
[3]. The
of the neck and larynx. Tracheal sound is very loud
duration of the process of inspiration longer than the
and his pitch is high (Fig. 2a). 50 this sound is very
cxpiratory process. Besides that inspiring sound
clear sounding compared to another normal lung.
intensity greater than the expiratory. The conditions
Inspiration and expiratory are relatively equal in
that is cause the wliee:e are asthma. CIIF, chronic
length [7J.
hronchitis, COPD dan pulm onarv cdcma [9].
Vesicular sounds are normal breath sounds that are
heard on the side chest and chest near the stomach.
The sounds ware soft with a low pitch (Fig. 2b).
Inspiration sounds much stronger than expiratory
~I
I
sounds, Often the expiratory process is hardly audible
(7]
i..
a. Tracheal
b.Vesicular
.
Crack/es sounds are a short burst sound that is
discontinuous (Fig. lc). it is generally more audible
; I
sound m the process of inspiration l,] The conditions
~II
that is cause the crackles are ARDS, asthma,
bronchiectosis. chronic bronchitis. consolidation.
hlllg disease dan pulnionarv
carly CflF. nterstitialzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
edem a [9J.
l.~.~~~II ~"t.
I
II
Il
II
d. Crackle
d
\\'h(,
A Comparison of Backpropagation and LVQ : a case
study of lung sound recognition
Fadhilah Syafria', Agus Buono'' and Bib Paruhum Silalahi3zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQ
Department of Computer Science and 3 Departement of Mathematics
J: zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Faculty of Mathematics and Natural Sciences Bogor Agricultural University
Bogor. Indonesia
Email: ·[email protected]@[email protected]
sounds are sounds
Abstract=Cnie
lungs
is
by
stethoscope.
low
sounds
factors.
be
human
made
alrnost
method
used
of lung
addition
to
recognition
Cepstrum
as method
is
Auscultation
of
by
to
technique
of
the
of these
to
In this research,
will
ability.
auditory,
with
perfonned
neural
and
and
accuracy
the method
(MFCC)
in
recommend
in the case of lung sounds.
In
results
almost
between
similar
Because
sound
is
of breathing
The next problem
are
the
the pattern
problem
of
of sounds
that
one type of breath
if the procedure
the
who
human hearing
the
of these
on
sound is also a problem
frequency.
and
of breath
relies
of the doctor
In addition,
techniques
noise
a subjective
technique
because
auscultation
of
This
but it has
of the analysis
to low frequency
environmental
occur
the
experience
a low enough
the other.
of Mei
is also used
The results
and
that is used
sounds.
and inexpensive,
results
the analysis.
occupies
breath
auscultation
in th is technique,
learning
of these two
recognition
The
less sensitive
network
that
This
techniques.
is a basic technique
evaluate
is by
a stethoscope.
auscultation
is fairly simple
disadvantage
of the lungs
using
as
technique
physicians
[2].
recognition
the state
sounds
known
using
determine
extraction.
technique
sounds
(BP)
better
to breath
analysis
sound
Coefficient
of feature
listening
of
to the above two methods.
Frequency
panern
lung disorder.
way to evaluate
procedure
if
Arti ficial
provide
One
but it
results
and
(L VQ). Comparison
performed
of speech
using
Because
is Backpropagation
which
sounds
the
noise
occur
approach.
Vector Quantization
of the
is less sensitive
similar.
can
a model
network
algorithms
hearing
is not done properly.
neural
methods
are
em ironmental
that
state
and inexpensive,
They
misdiagnosis
auscultation
breath
is fairly simple
analysis.
the
is knO\\TI as auscultation.
disadvantage.
frequency,
lung
to
This technique
sorne
subjective
to evaluate
listening
This technique
has
way
factors,
sounds
and
misdiagnosis
of auscultation
can
is not done
properly.
show the
accuracy of using Backpropagation
is 93.17%, while
the value of using the L VQ is R6.R8%. It can be
Based on the above problems.
we interested
in
conducting
research in perfonning
speech recognition
concluded
of
that the introduction
Backpropagation
compared
method
of lung sounds
gives
to the L VQ method
better
using
perfonnance
for speech
recognition
cases of lung sounds.
normal
lung
and
lung
interference
(abnormal),
some
of the disease
that
cases
can
be recognized.
taken as material
sounds
Sounds
showed
This
were
a specific
sound
for diagnosis
Lung
sounds
Respiratory
trachea
are
sounds
while
lung
part
of the
include
respiratory
occur
arnund
the respiratory
turbulence
occurs
tract during
because
duct that is wider to narrower
ln general.
sounds.
the chest.
Sounds of lungs occurs due to air turbulence
air enters
indicates
breathing.
the air flow
airways
when the
from
This
the air
or vice \ ersa.
the sound of the lung is divided
into two.
.
Voice
partern
whilc
abnormalities
recognition
or identified
advance
to be able
cIassifies
recognition
A system
is
a
to
used
computational
or verify the sound
need to be trained
to recognize
the voice
the
is needed
system to perfonn
sound
and whceze
in order to the voice
systems
according
an algorithrn
vesicular.
so that it can be
system
These
be
in the lungs [4][5].
is an effort
that is able to identify
is autornatically.
can
vcsicular
the crackle
applicarion
respiratory
lung
lung.
and
rccognition
purposcs.
abnormal
Tracheal
Avoice
voice
whcrcas
nornal
can be recognized
sounds is the sound of the lungs that are not detectable
abnormality.
wheeze.
sound indicates
the sound of the mouth and
sounds
and
in
pattern
[3]. Lung sound will be
I.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I;\TRODlT 110;-':
cIassified into four classes, narnely tracheal.
crackle
detected
of lungs resulting
class.
and
For
in
the
such
to train the speech
more prcc ise .
can lcarn and recognize
pauerns
of sound
ICACSIS 2014
can be done with the application of artificial neural
Start
nerworks. Artificial neural network is a computarional
~~--)
method that mimics the biologicai neural network of
Uteratcre
review
human that capable trained to solve problems. In
lu", sound theory
MFCC
I
general, there are several artificial neural network
Backpropagation and LVQ 1
algorithms that can be appJied to partern recognition.
MATLAB
!
-------,,------'
This research will use two artificial neural network
algorithms, they are Backpropagation
(BP) and
Data ecquuinon .
Learning Vector Quantization (LVQ). Both methods
Lun, sound
have advantages
and disadvantages
of each.
Comparison of these two methods performed to
Segrnentaucn
determine and recommend algorithrns which provide
bener recognition accuracy of speech recognition in
the case of lung sounds.
Trajnlng Data
ln addition to using an artificial neural network
algorithm in the classification process, it is no less
Feature ertracuon
. !
Feature extr~:
important in voice recognition is the feature extraction.
MFCC
MfCC
ln this study, feature extraction methods are used Mei
Frequency Cepstrum Coeffisient (MFCC). MFCC is a
Establlsh the model
(8ackpropagation
~nd l VQ ~
feature extraction method which gives excellent results
in classifying normal lung sounds and wheeze soundszyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
Model testmg
[6J.
t
---r---
II.
RESEARCH
METHOD
Companng.
Anal!sys. dan
. - "r--discuanon
Research method conducted is illustrated in Figure
I. In the image looks that research begins with a
literature review of the theories necessary for the
completion of this study, such as lung sound theory.
MFCC.
Backpropagation.
LVQ and MATLAB
progranuning.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A.
Materials Research
Documentancn
I
J
and
repcrts
Figur~ I. Research Method
Lung sound data obtained from the repository of
respiratory sounds on the Internet, namely Linmann
Wlwe::es sound is a type of sound that IS cominuous,
Repository. The sound data consist of 32 lung sound
have ii high pitch (Fig. 2d). and more often heard on
data. which is divided into 8 tracheal sounds. 8
the expiratory. This sound occurs when the flow of air
vesicular sounds. 8 crackle sound and 8 wheeze sound.
narrowcd
airways due to secretions.
through
the
Tracheal sound is a sound that is audible at the base
foreign body or wounds that preclude
[3]. The
of the neck and larynx. Tracheal sound is very loud
duration of the process of inspiration longer than the
and his pitch is high (Fig. 2a). 50 this sound is very
cxpiratory process. Besides that inspiring sound
clear sounding compared to another normal lung.
intensity greater than the expiratory. The conditions
Inspiration and expiratory are relatively equal in
that is cause the wliee:e are asthma. CIIF, chronic
length [7J.
hronchitis, COPD dan pulm onarv cdcma [9].
Vesicular sounds are normal breath sounds that are
heard on the side chest and chest near the stomach.
The sounds ware soft with a low pitch (Fig. 2b).
Inspiration sounds much stronger than expiratory
~I
I
sounds, Often the expiratory process is hardly audible
(7]
i..
a. Tracheal
b.Vesicular
.
Crack/es sounds are a short burst sound that is
discontinuous (Fig. lc). it is generally more audible
; I
sound m the process of inspiration l,] The conditions
~II
that is cause the crackles are ARDS, asthma,
bronchiectosis. chronic bronchitis. consolidation.
hlllg disease dan pulnionarv
carly CflF. nterstitialzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
edem a [9J.
l.~.~~~II ~"t.
I
II
Il
II
d. Crackle
d
\\'h(,Graph of accuracy Based on l.enrning' Rate
• BV (! e~tl~
U
91 %
96 -13
3
97..'2
96 73
15
);988
94.94
9H)5
95.l'\3
9643
OaL))
----.~
8/.41
93.1 J
94.42
-
9~.16
-
~
ln contrast. in Backpropagation,
increased learning
rate rends to increase the accuracy. This is due to the
91.96
94.9-1
80.36
89.5R
95.24
30
algorithm Backpropagation
just changing the network
weights by summing up the weighis (without the
C. Effect ofXutnbc:zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
o{,\fFC C Coefficient Against
nature of the competition) so that the network weight
Accllracl"
rernains stable.
From the cntire experiment. the average accuracy
E. lntroductorv
Rate of Each LIII/g Sounds
obtained at each value of the coefficient is shown in
A
verage
of
accuracy
of the entire experiment based
Figure 5. From the graph it can be seen that there is a
on the type of lung sounds is shown in Figure 7. From
tendency that the higher the coefficient MFCC makes
the graph it can be seen that the type of tracheal sound
111
L VQ
and Backpropagation.
lower accuracy
has the highest accuracy. rncaning that most can be
although the decrease was not significant. Accuracy at
distinguished
from other types of sound. [t is. as
MFCC coefficient
13 tend to be higher th an the
mentioned previously (Figure 7) that a tracheal sound
accuracy of the MFCC coefficient greater. This is due
signals has the form the most different than the othcr
to the higher coefficient of MFCC impact on the
thrce types of sound. Meanwhile,
the sound of
dimensions of a larger data. Largc data dirncnsions
vesicular
type. crackle.
and wheezc
has little
make the generalization capability of A\,1\,1 is lowcr so
resemblance so that iIS accuracy is lower. meaning that
the accuracy is dccrcased.
the sound of vesiculer is sornetimes identified as a
D. E[{cct o] Learning Rate Against Accllrac\'
crackle type or whecze and vice versa,
lnfluence
of the learning rate throughout
the
cxpcrirnent to the average accuracy is shown in Figure
n. From the graph. in L VQ there is a tendcncy that
20.·
87.5
91 91>
93.75
'15.54
9-1.35
to I
IS13:\
!J7x-1)7~)-1121-22.-)
ICACSIS 201-1zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Graph of accuracy of eadi type sound
100
98
96
94
---~~-------
of L YQ, In addition, tracheal sound can be recognized
better than the lung sound types vesicular, Crackle,
and wheeze.
92
90
REFERENCES
88
86
84
82
80
A. Cohen.
D, Landsberg,
"Analysis and automatic
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IEEE Transactions on. (9). 585-590. 1984,
87.7
• LVQ(Tr~jnil"€ zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
D.lt.l)
94,6
84
85
[2] H. Kiyokawa, MDM. Greenberg. K. Shirota. H, Pasterkamp,
81.3
84
87,8
al VO (To'ting Data)
94,6
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Sounds", CHEST. t 19(6): 1886-1892.2013
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a SP ITo,ting Data)
88,6
98,2
91,4
[3] A. Rizal. MD. Samudra, I. lwut. V. Suryani. "Pengenalan
Suara
Paru
Menggunakan
Spektogram
dan
K-Mean
Figure 7 Graph of accuracy in Each Type SoundzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Clusrering", Proceeding SI TIA 20 I O. Februari 20 I 0
[4] AA, Abaza. 18. Day. JS. Reynolds. AM. Mahmoud. WT
Goldsmith,
WG, McKinncy.
EL Petsonk. DG. Frazer,
F. Comparison of Accuracy Rate Backpropagation
"Classification of voluntary cough sound and airflow patterns
and L VQ Method
for detecting abnormal pulrnonary function". Cough. 5(8).
2009,
Comparison
of the accuracy
of LYQ and
[5] A. Gurung. CG. Scrafford. JM. Tielsch. OS. Levine. w.
Backpropagation
method in identifying lung sounds is
Checkley
\Ii,
"Computerized
lung sound analysis
as
presented
in Figure 8, Overall, backpropagation
diagnostic aid for the detection of abnormal lung sounds: a
method gives better accuracy results than L YQ
systernatic review and meta-analysis".
Respir Med, 105(9):
1396-1403,20 II.
method, with an overall average accuracy of 94,58%
f6] M. Bahoura, "Pauern
Recognition
Methods Applied to
for training data and 93,17% for test data, while L VQ
Respiratory Sounds Classification into Normal and Wheeze
only offer results of R7,83% for training data and
Classes. Computcrs and Biolog- and ~kdiClne. 39(9):82486,88% for test data, Testing the training data provide
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[71 A. Rizal. L Anggraeni, V Suryani, "Pengenalan Suara Parugreater accuracy results because the data is also used
Paru Normal Menggunakan LPC dan Jaringan Syaraf Tiruan
in the training process, while the test data were not
Back-Propagatiou'.
Proceeding EECClS2006, Mei 2006.
included during training,
-~-~--,
[81 T. Katila. P Piirila. K. Kallio. E, Paajanen. T. Rosqvist. AR,
Sovijarvi, "Original waveform of lung sound crackles: a case
Comparison of the accuracy of lVQ method and BP
study of the effect of high-pass filtration" Journal of Applied
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t991.
98
[t]
r--96
g
~
ee
;:
9'
92
90
AA
86
84
8}
80
_ tIJ Q
• BP
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~lZ. Ramadhan, "Perancangan
Sistem Instrumentasi untuk
Identifikasi dan Analisis Suara Paru-Paru Menggunakan DSP
H1S320CMI6T
[Essay]",
lkpok[IDI
Universitas
lndonesia, 2012.
1101 K. Patcl, KK. Prasad. "Spccch Rccognition and Vcrification
Using MFCC & "Q". Intcrntional Journal. 20) 3,
[111 A. Buono. "Representasi nilai hos dan model MFCC sebagai
Ir ainmg O.lh zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
T(>$tng O.t.1 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGF
ekstraksi
ciri pada sistem
indentifikasi
pembicara
di
8/,877,)
8 6 ,8 /')
lingkungan
ber-noiscmcnggunakan
HMM [disscrtation [".
93.17l5
94,5775
Depok: Program Studi llmu Komputer. Universitas lndonesia .
Figure 8 Comparison of Accuracy graphs of LVQ and
Backpropagation
Although L YQ method gives lower accuracy, but
the method is simpler than computing aspects 50 that
training time is faster than the Backpropagation
method, This is consistent with studies conducted
gives
Hawickhorst ef al. (1995) that Backpropagation
berter accuracy
results than LYQ. but higher
computational complexity [18],
tv.
CO~CLL:SIO~
From the research th at has been done. it can be
concluded that it has made a model of lung sound
recogniuon
with
MFCC
feature
extraction.
Classification with LVQ and Backpropagation
method
can be applied for the idenrification of lung sounds. by
adjust paramcrers MFCC coefficient and learning rate
that maximizes the accuracy, Backpropagation
method
has bener accuracy
than LVQ with all ayeragc
accuracy on the test data was 93.17n o. whilc Ro.RRO n
2009.
f 121 L. Muda, M, Begam. L Elamvazuthi. "Voice Rccogniiion
Algoriihms
Using Mei Frcquencv
Ccpstral
Cocfficicnt
IMFCC) and Dynamic Tune Warping IDTW) Tcchniqucs",
Journal of Cornpuring. Volume 2, lssuc J, 2010.
[131:--'1.
Slancy.
"Auditory
Toolbox
Interval
Research
Corporation". Tech. Rcp, ID, 1998,
r 141 F. Suhandi, "Prediksi Harga Saham dcngan Pcndckatan
Anificial
Ncural
1\ el work mcnacunakan
Algoritma
Backpropagarion", 2009.
[151 L. Fausett. "Fundamcnrals of ncural nctworks architcctures
algorithms and applications". F lo rid a l.atitudc of Technologv,
199~.
11(>1 S, Kusumadewi.
S. Hartati. "lntcgras: SIStem Fuzzy dan
Jaringan Syaraf', Yogyakana. (iraha llmu, ~O I O.
1171 L, Rahadianu.
"Pengembangan
Algoritma
Pembelajaran
Berbasis Dimensi serta Kornparasinva rcrhadao Pembelajaran
F II::\ -Xcuro
Learning
Berbasiskan
Vektor
rada
'· l'd o rQ IIO llfi:O l/o 1 l
untuk Pengenalan Citra \\-3)3h Frontal
! Essa\ [". Depok [IDI Fa-rlkorn l.nivcrsuas lndoncsra. ~009
[I XI BA Hawickhorst.
S:\
Zahonan.
K
Rajagopal.
..:\
Comparison
of Thrcc Ncural :--'-ctwork Architccrurcs
for
Auiornauc Specch Rccognition"
DI dalam Dagli CH, el al.
editor Procccdmgs of Ihe Aruficiol Xcurol Xctw orks in
1 :-"g m C (,f'ln g
[191
,1
r
lIl2
.
.-I.\"S II:"'95. ~'cw York
:\S\1E
Press. hlm ~~1·"
ICACSIS 201-1zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A Comparison of Backpropagation and LVQ : a case
study of lung sound recognition
Fadhilah Syafria', Agus Buono'' and Bib Paruhum Silalahi3zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQ
Department of Computer Science and 3 Departement of Mathematics
J: zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
Faculty of Mathematics and Natural Sciences Bogor Agricultural University
Bogor. Indonesia
Email: ·[email protected]@[email protected]
sounds are sounds
Abstract=Cnie
lungs
is
by
stethoscope.
low
sounds
factors.
be
human
made
alrnost
method
used
of lung
addition
to
recognition
Cepstrum
as method
is
Auscultation
of
by
to
technique
of
the
of these
to
In this research,
will
ability.
auditory,
with
perfonned
neural
and
and
accuracy
the method
(MFCC)
in
recommend
in the case of lung sounds.
In
results
almost
between
similar
Because
sound
is
of breathing
The next problem
are
the
the pattern
problem
of
of sounds
that
one type of breath
if the procedure
the
who
human hearing
the
of these
on
sound is also a problem
frequency.
and
of breath
relies
of the doctor
In addition,
techniques
noise
a subjective
technique
because
auscultation
of
This
but it has
of the analysis
to low frequency
environmental
occur
the
experience
a low enough
the other.
of Mei
is also used
The results
and
that is used
sounds.
and inexpensive,
results
the analysis.
occupies
breath
auscultation
in th is technique,
learning
of these two
recognition
The
less sensitive
network
that
This
techniques.
is a basic technique
evaluate
is by
a stethoscope.
auscultation
is fairly simple
disadvantage
of the lungs
using
as
technique
physicians
[2].
recognition
the state
sounds
known
using
determine
extraction.
technique
sounds
(BP)
better
to breath
analysis
sound
Coefficient
of feature
listening
of
to the above two methods.
Frequency
panern
lung disorder.
way to evaluate
procedure
if
Arti ficial
provide
One
but it
results
and
(L VQ). Comparison
performed
of speech
using
Because
is Backpropagation
which
sounds
the
noise
occur
approach.
Vector Quantization
of the
is less sensitive
similar.
can
a model
network
algorithms
hearing
is not done properly.
neural
methods
are
em ironmental
that
state
and inexpensive,
They
misdiagnosis
auscultation
breath
is fairly simple
analysis.
the
is knO\\TI as auscultation.
disadvantage.
frequency,
lung
to
This technique
sorne
subjective
to evaluate
listening
This technique
has
way
factors,
sounds
and
misdiagnosis
of auscultation
can
is not done
properly.
show the
accuracy of using Backpropagation
is 93.17%, while
the value of using the L VQ is R6.R8%. It can be
Based on the above problems.
we interested
in
conducting
research in perfonning
speech recognition
concluded
of
that the introduction
Backpropagation
compared
method
of lung sounds
gives
to the L VQ method
better
using
perfonnance
for speech
recognition
cases of lung sounds.
normal
lung
and
lung
interference
(abnormal),
some
of the disease
that
cases
can
be recognized.
taken as material
sounds
Sounds
showed
This
were
a specific
sound
for diagnosis
Lung
sounds
Respiratory
trachea
are
sounds
while
lung
part
of the
include
respiratory
occur
arnund
the respiratory
turbulence
occurs
tract during
because
duct that is wider to narrower
ln general.
sounds.
the chest.
Sounds of lungs occurs due to air turbulence
air enters
indicates
breathing.
the air flow
airways
when the
from
This
the air
or vice \ ersa.
the sound of the lung is divided
into two.
.
Voice
partern
whilc
abnormalities
recognition
or identified
advance
to be able
cIassifies
recognition
A system
is
a
to
used
computational
or verify the sound
need to be trained
to recognize
the voice
the
is needed
system to perfonn
sound
and whceze
in order to the voice
systems
according
an algorithrn
vesicular.
so that it can be
system
These
be
in the lungs [4][5].
is an effort
that is able to identify
is autornatically.
can
vcsicular
the crackle
applicarion
respiratory
lung
lung.
and
rccognition
purposcs.
abnormal
Tracheal
Avoice
voice
whcrcas
nornal
can be recognized
sounds is the sound of the lungs that are not detectable
abnormality.
wheeze.
sound indicates
the sound of the mouth and
sounds
and
in
pattern
[3]. Lung sound will be
I.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
I;\TRODlT 110;-':
cIassified into four classes, narnely tracheal.
crackle
detected
of lungs resulting
class.
and
For
in
the
such
to train the speech
more prcc ise .
can lcarn and recognize
pauerns
of sound
ICACSIS 2014
can be done with the application of artificial neural
Start
nerworks. Artificial neural network is a computarional
~~--)
method that mimics the biologicai neural network of
Uteratcre
review
human that capable trained to solve problems. In
lu", sound theory
MFCC
I
general, there are several artificial neural network
Backpropagation and LVQ 1
algorithms that can be appJied to partern recognition.
MATLAB
!
-------,,------'
This research will use two artificial neural network
algorithms, they are Backpropagation
(BP) and
Data ecquuinon .
Learning Vector Quantization (LVQ). Both methods
Lun, sound
have advantages
and disadvantages
of each.
Comparison of these two methods performed to
Segrnentaucn
determine and recommend algorithrns which provide
bener recognition accuracy of speech recognition in
the case of lung sounds.
Trajnlng Data
ln addition to using an artificial neural network
algorithm in the classification process, it is no less
Feature ertracuon
. !
Feature extr~:
important in voice recognition is the feature extraction.
MFCC
MfCC
ln this study, feature extraction methods are used Mei
Frequency Cepstrum Coeffisient (MFCC). MFCC is a
Establlsh the model
(8ackpropagation
~nd l VQ ~
feature extraction method which gives excellent results
in classifying normal lung sounds and wheeze soundszyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
Model testmg
[6J.
t
---r---
II.
RESEARCH
METHOD
Companng.
Anal!sys. dan
. - "r--discuanon
Research method conducted is illustrated in Figure
I. In the image looks that research begins with a
literature review of the theories necessary for the
completion of this study, such as lung sound theory.
MFCC.
Backpropagation.
LVQ and MATLAB
progranuning.zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
A.
Materials Research
Documentancn
I
J
and
repcrts
Figur~ I. Research Method
Lung sound data obtained from the repository of
respiratory sounds on the Internet, namely Linmann
Wlwe::es sound is a type of sound that IS cominuous,
Repository. The sound data consist of 32 lung sound
have ii high pitch (Fig. 2d). and more often heard on
data. which is divided into 8 tracheal sounds. 8
the expiratory. This sound occurs when the flow of air
vesicular sounds. 8 crackle sound and 8 wheeze sound.
narrowcd
airways due to secretions.
through
the
Tracheal sound is a sound that is audible at the base
foreign body or wounds that preclude
[3]. The
of the neck and larynx. Tracheal sound is very loud
duration of the process of inspiration longer than the
and his pitch is high (Fig. 2a). 50 this sound is very
cxpiratory process. Besides that inspiring sound
clear sounding compared to another normal lung.
intensity greater than the expiratory. The conditions
Inspiration and expiratory are relatively equal in
that is cause the wliee:e are asthma. CIIF, chronic
length [7J.
hronchitis, COPD dan pulm onarv cdcma [9].
Vesicular sounds are normal breath sounds that are
heard on the side chest and chest near the stomach.
The sounds ware soft with a low pitch (Fig. 2b).
Inspiration sounds much stronger than expiratory
~I
I
sounds, Often the expiratory process is hardly audible
(7]
i..
a. Tracheal
b.Vesicular
.
Crack/es sounds are a short burst sound that is
discontinuous (Fig. lc). it is generally more audible
; I
sound m the process of inspiration l,] The conditions
~II
that is cause the crackles are ARDS, asthma,
bronchiectosis. chronic bronchitis. consolidation.
hlllg disease dan pulnionarv
carly CflF. nterstitialzyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA
~
edem a [9J.
l.~.~~~II ~"t.
I
II
Il
II
d. Crackle
d
\\'h(,