Journal of Theoretical and Applied Information Technology
15th August 2016. Vol.90. No.1

© 2005 - 2016 JATIT & LLS. All rights reserved.

ISSN: 1992-8645

www.jatit.org

E-ISSN: 1817-3195

ANALYSIS AND METHODS TO TEST CLASSIFICATION OF
NORMAL AND PATHOLOGICAL HEART SOUND SIGNALS
1

RIMULJO HENDRADI, 2,8ACHMAD ARIFIN, 3HIRO SHIDA, 4SUHENDAR GUNAWAN,
5
MAURIDHI HERY PURNOMO, 6HIDEYUKI HASEGAWA, 7HIROSHI KANAI

1,2,5

Graduate Program of Electrical Engineering, Department of Electrical Engineering, Institut Teknologi
Sepuluh Nopember Surabaya 60111, Indonesia
3
Makabi, Naha, Okinawa, 902-0068, Japan
4
Kebonjati Hospital Bandung 40181, Indonesia
6
Graduate School of Science and Engineering for Research, University of Toyama 3190 Gofuku, Toyama
930-8555, Japan
7
Graduate Schools of Engineering and Biomedical Engineering, Tohoku University, Sendai 980-8579,
Japan
8
Biomedical Engineering Department, Institut Teknologi Sepuluh Nopember Surabaya 60111, Indonesia
E-mail: 1hendrasain@elect-eng.its.ac.id, 2arifin@bme.its.ac.id, 3loharve08@yahoo.co.jp,
4
gunawansuhendar@yahoo.com, 5hery@ee.its.ac.id, 6hasegawa@eng.u-toyama.ac.jp,
7hkanai@ecei.tohoku.ac.jp
ABSTRACTAn acute shortage of cardiologists and many rural clinics were run by nurses in Indonesia. We proposed todevelop of a screening technique based on artificial intelligence that classifies of normal and pathologicalheart sound signals of human subjects due to signs important and symptoms for heart diagnosis based on
knowledge of auscultation experts. Heart sound signal analysis system consisted of three stages. Firstly,
preprocessing. Secondly, feature extraction with respect to the cardiac cycle based on wavelet analysis todifferentiate normal and pathological heart sounds. Feature reduction using PCA was also carried out toreduce the dimension of the heart sound feature vectors for classification. Thirdly, three classifiers: ANN
MLP-BP, FCM clustering and HCM clustering to classify normal, systolic murmur, diastolic murmur, andcontinuous murmur, respectively. The performance of each classifier was evaluated with statistical
validation method. From our experimental results, the three classifiers that showed significant potential intheir use as an alternative diagnostic tool were compared. The ANN achieved the best performance as an
automated classifier rather than FCM and HCM methods. Its performance was 100% for sensitivity,
specificity, and accuracy, respectively, of input 20,000 features. Furthermore, for input 300 features, theperformance was 98.90%, 99.37%, and 99.03% for sensitivity, specificity, and accuracy, respectively. The
heart sound signal analysis system was suitable to classify of normal and pathological cases. The proposed
method was considered very important for objective screening and very useful as an alternative diagnostic
tool that complies with the requirements for rural clinics. We hoped that the method would be beneficial instudy of auscultatory technique for medical students. Surrogate data modeling of pathological heart sounds
signals as an alternative tool of the heart sound simulator and for classification purpose was further study.
Keywords: Wavelet Analysis, Principal Component Analysis (PCA), Artificial Neural Network Multilayer
Perceptron Back Propagation (ANN MLP-BP), Fuzzy C-Means (FCM) Clustering, Hard CMeans (HCM) Clustering
1.

INTRODUCTION

Cardiac disease is the second most mortality
disease after cerebrovascular accident in the world[1], [2] and has long been the leading cause ofdeath in developed countries, particularly inIndonesia [3]. A preliminary, non-invasive
identification of the cardiac diseases, withparticular manners and follow-up treatments can

avoid increasing of deadly hazards of the heartassaults [4]-[7]. One of the manners for early
identification of cardiac disease can be performed
thru detection of heart sounds [1], [8].
Heart sounds are low-frequency transient
signals produced by vibration after opening andclosing of cardiac valves, and/or by vibration ofwhole myocardium and connected structures during

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a cardiac cycle. The cardiac cycle is divided into
two periods; systole when the ventricles contractand ejects blood from the heart, and diastole, whenthe ventricles are relaxed and the heart is filled withblood. A normal functioning heart contains of two
basic heart sounds, i.e., a first heart sound (S1) and asecond heart sound (S2) [9]. The S1 and S2 producedby the closing of the atrioventricular valves (mitralvalve and tricuspid valve) and semilunar valves(aortic valve and pulmonary valve), respectively. If
closure of heart valve is not perfect and resulted inbackflow of blood (regurgitation) or opening ofheart valve is not perfect and ensued force blood
thru a narrow opening (stenosis), then will becaused additional sound. An Abnormal heart soundappears as a result of turbulence flow of blood thatpasses by means of narrow openings is named
murmur [9].
One of the valuable methods of heart valve
diagnostic can be carried out by auscultation [1],[8]. The auscultation is conducted by listening to
the heart sounds utilizing a stethoscope. Its
auscultation is an imperative tool in the diagnosisof cardiac disease, non-invasive, and low-costscreening method. It can provide precious andmodest diagnostic tool for detecting cardiac
dysfunction, because of its ability to offer valuableevidence concerning the integrity and function of
the heart valves and hemodynamics of the heart[10], [11]. Furthermore, electrocardiogram (ECG),
Doppler, and ultrasound imaging techniques as new
diagnostic modalities, although can afford more
direct evidence but these diagnostic tests do notassure all valvular diseases. Additionally,
auscultation remains the principal tool for screening
and diagnosis in major health care, due in part tohigher cost and limitedness existence theequipment, and to the special expertise necessary to
manage and analyze the results of these new
diagnostic modalities. In developing countries,
particularly in Indonesia rural clinics, auscultation
may be the only means available.
The heart sounds analysis by auscultatory
technique is a very special expertise. Accuratenessof the examination relies highly on the skill andpractical learning of a listener that takes years togain and fix [12]. Because this expertise is greatly
difficult to be taught in a structured way, so thathearing of a cardiologist with another one who
cannot be duplicated, although this technique has

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been learned both in educational and clinicalaspects by means of basic literatures [9], [13].
Hence, the above cases and subjectivity of a
cardiologist become deteriorations of auscultatory
technique. Limitations of the auscultatory techniquehave stimulated us to utilize digital signal
processing based method for identification andclassification of normal and pathological heartsound signals. Mechanical events of the heart soundsignals relating to a given normal and murmur wereconceived in a single interval of cardiac cycle.
Therefore, it was very essential to identify thesingle cycle for analysis of normal and murmur. For
implementation of heart sound signal analysis
system, it was necessary to detect the single cycle,
extract feature from the single cycle, and thenclassify the signal due to signs important andsymptoms for heart diagnosis based on knowledgeof auscultation experts.
Feature extraction was one of the mostimportant steps for implementation of the heartsound signal analysis system, since even the bestclassifier would perform poorly if the features were
not chosen well. In our previous research, extensive
characterization method wavelet-based analysis hasrevealed ability to outgrow problems of traditional
auscultation to clarify objectively mechanisms ofheart valves functions [2]. Furthermore, the method
was more precisely to extract the heart soundcharacteristics rather than Short Time Fourier
Transform (STFT) [14] and Fast Fourier Transform(FFT) [15] methods. Spectrogram STFT could not
perfectly detect both S1 components (mitral andtricuspid) and both S2 components (aortic andpulmonary). The problem was caused by a trade-off
between time and frequency resolutions along withpreference of a time window length [14].
Furthermore, the FFT did not consider about time,
so it could not reveal which of two valves of S1 andS2 components, respectively, were closed firstly[15]. In this study, the wavelet analysis would be
performed to extract characteristics of the heartsounds. It was a method to acquire simultaneous
high-resolution time and frequency explanation
about a signal. The method used a flexible window
modulation. Hence, it could provide detailed
information of the heart sound during the cardiac
cycle. The calculation results of feature extraction
would be input for classification of the heart soundsignals.

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E-ISSN: 1817-3195

In previous researches, Chebil et al. [16] andGupta et al. [17] have efficiently used the Artificial
Neural Network (ANN) for detecting andclassifying the heart sounds. However, satisfactory
performance was not reported. Therefore,
increment the classification performances of heartsounds should be managed into a more sensitiveand could be more rigorous in analyzing
abnormalities that appear in characteristics of theheart sounds for heart diagnosis.
We proposed to develop of a screening
technique based on artificial intelligence toovercome limitations of the auscultatory technique
for the heart diagnosis. The screening techniqueclassifies of normal and pathological heart soundsignals of human subjects, i.e., normal, systolicmurmur, diastolic murmur, and continuous
murmur, respectively, due to signs important andsymptoms based on knowledge of auscultation
experts. Three potential classifiers would becompared: Artificial Neural Network Multilayer
Perceptron Back Propagation (ANN MLP-BP),
Fuzzy C-Means (FCM) clustering and Hard CMeans (HCM) clustering. The proposed method
would be very important for objective screening
and very useful as an alternative diagnostic tool thatcomplies with the requirements for rural clinics.
Additionally, the method promises a prominent rolein study of auscultatory technique for medical
students.
2.

Figure 1: Block Diagram Of Heart Sound Signal
Analysis System

METHODS

The procedures used in analysis and methods
to test classification of the normal and pathologicalheart sound signals are illustrated in Fig. 1.
Discussion follows in the illustrated order.
2.1 Data Collection
Recording of the heart sounds was composedof three sets of 70 human subjects. The first set wascollected from an acoustic transducer of ALOKA
ProSound II SSD-6500 SV and the heart sounds
were digitized with 1 kHz sampling frequency of
two normal subjects. The second set was from anelectronic stethoscope (3M Litmann Model 4100)
and the sounds were digitized with 8 kHz sampling
frequency of one mitral valve regurgitation (MR)subject with pathologic systolic murmur. The firstand second parts of the subjects were participatedin this research. Aim of an experiment was clarified
to each subject and subject’s consent was attained.

The third set was collected from a cassette ofliterature [13] containing a selection of sevenconditions of normal and pathological heart sounds,
i.e., 5 normal, 5 MR, 10 atrial septal defect (ASD),
20 stenosis aorta (SA), 10 ventricular septal defect(VSD), 9 mitral stenosis (MS), and 8 persistent
ductus arterious (PDA) subjects, respectively. The
heart sounds of literature [13] were digitized with
11 kHz sampling frequency and they recorded for
educational purpose. In this study, the pathologicalheart sounds consisted of systolic murmur (e.g.
MR, ASD, SA, VSD), diastolic murmur (e.g. MS),and continuous murmur (e.g. PDA).
2.2 Heart Sound Signal Analysis
Heart sound signal analysis system fordeveloping of a screening technique consisted ofthree stages (Fig. 1). The first stage presented three
preprocessing units, i.e., decimation, segmentation
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ISSN: 1992-8645

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for augmenting quality of the signal. The second
stage was feature extraction based on wavelet
analysis (CWT, i.e., Continuous Wavelet
Transform) to distinguish normal and pathologicalheart sounds. This feature extraction results would
be input for classification purpose. Reduction of thefeature using Principal Component Analysis (PCA)
was also carried out in this stage to decrease thedimension of the heart sound feature vectors forclassification. The third stage was the classification.
Three classifiers, i.e., NN MLP-BP, FCM, andHCM were conducted to make their classification
decisions of a heart sound’s class based on signs
important and symptoms for heart diagnosis inknowledge of auscultation experts. Hence, eachclassifier as an artificial knowledge based system
could classify four classes, i.e., normal, systolicmurmur, diastolic murmur, and continuous
murmur,respectively.
Furthermore,theclassification performance of each classifier was
evaluated with statistical validation method.
Evaluation of the performance would be derived
from classifier vector input of the wavelet analysis
results as well as the feature reduction results. In
this paper, Pascal programming language for theheart sound signal analysis was performed.
2.2.1 Preprocessing
Two filtering steps of the signal to reduce
disturbance by background noise were conducted.
First step, an eighth-order Butterworth bandpass
IIR filter with cutoff at 20 Hz and 330 Hz was usedfrom native signal of 1 kHz and 8 kHz sampling
frequency, respectively, also with cutoff at 20 Hz
and 1100 Hz from native signal of 11 kHz sampling
frequency. Furthermore, the native signal of 8 kHz
and 11 kHz sampling frequency was decimated byfactor 4 and factor 5, respectively. Determination of
bandwidth filtering was appropriate with Stein et al.
[13]. The signal was afterward segmented thatwould be clarified later. Second step, algorithm
implementation of the fixed threshold wavelet
denoising that explained in Hall et al. [18] wasperformed for augmenting quality of thesegmenting signal. A Daubechies wavelet of order
4 (Db4) with a decomposition level of 5 was
picked.
2.2.2 Segmentation
The segmentation regarding a cardiac cycle wasa prominent stage in analyzing of the heart sounds.
Identification the location and intervals of S1 and S2
could be very beneficial information about the

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mechanical event of the cardiac condition. The
native signal would be declared in more modestly
and effortlessly seen by exploiting of Shannon
envelope based on the S1 as well as S2 [2].
The Shannon envelope or the normalized
average Shannon energy was competent to diminish
the effect of low noise and to create the low
intensity sounds more effortless to be located [19].
The algorithm was described as follows.
First of all, normalization of a native heart soundwas performed in the form Eq. (1).

xnorm (i ) =x (i )max ( x ( ⋅ ) )(1)

where max x ( ⋅ ) was absolute maximum of signal.
Second, as in Eq. (2),Ex = −

1 N 2
2
xnorm (i) ⋅ log xnorm(i)∑
N i =1(2)

the average Shannon energy was computed where
xnorm(i) was the ith normalizing signal and N was
amount of points within 0.02 sec. segment and
0.0165 sec. segment for normal and murmur
subjects, respectively, which corresponds to aframe, for normal, i.e., N = 20, and for murmur, i.e.,
N = 33. Third, normalization of Ex thru all of theframes as in Eq. (3),P(t ) =E x (t ) − M (E x (t ))S (E x (t ))(3)where M(Ex(t)) and S(Ex(t)) were mean andstandard deviation of average Shannon energy offrame t, so P(t) as Shannon envelope [19]. Fourth,P(t) was filtered reverse direction. The resulting
sequence had precisely zero phase filter, thus
therein no time delay.
Identification for the amplitude peaks of S1, S2,
and S1 of the following cycle from filtering of P(t)was visual inspection based. The systole is shorter
than the diastole as fundamental knowledge of theS1 and S2 peaks selection [20]. The peak adjacent tothe systole was recognized as S1. The peak adjacentto the diastole was recognized as S2 [19]. Therefore,
pmax as maximum among the three peaks was
acquired. Additionally, pmin as minimum of intervalthat involved the three peaks was attained.
Determination of a thresholding value, i.e., pmin +cenv× pmax, where cenv was a thresholding coefficientof P(t) was performed [2]. Picking of cenv was set
manually and diverse (0.00 < cenv < 0.74). Its

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picking based on onset until ending incidents of S1and S2, respectively, as well S1 onset of thefollowing cycle.

and scale factors as of 1.0 × 10 up to 1.0 × 10-1.
The feature extraction results would be an input atclassification stage.

Systolic is interval of time of S1 onset until S2
onset. Diastolic is interval of time of S2 onset until
S1 onset of the next cycle. Furthermore, single cycle
is interval of time of S1 onset of a cycle and S1 onsetof the following cycle [2].

2.2.4 Feature reduction

2.2.3 Feature extraction
CWT as a feature extractor is appropriate foranalyzing of non-stationary signals especially heartsound signals. Because the wavelet may be
stretched or suppressed, distinct features of thesignal were extracted. While a dilated wavelet takeson the lower frequency constituents, a narrow
wavelet extracts high-frequency constituents of thesignal. CWT was calculated by relating the signalx(t) with set of time frequency atoms f(t); it
produced a collection of coefficients C(b,s) statedby Eq. (4),1∞

* t − b -2Principal component analysis (PCA) was
conducted to decrease the dimension of the heartsound feature vectors by picking only the vectorsthat had great variances. The aim of PCA was toreplace a set of m variables x1, x2, …, xm, which
may be correlated, by another set f1, f2, …, fm,
called the principal components or principal factors.
These factors were independent (uncorrelated)variables [25], [41].
The algorithm started with the correlation
matrix R which was an m × m symmetric matrix
such that rij was the correlation coefficient between
variable xi and variable xj. The correlation matrix R
using Eq. (6):1
R = XXT(6)n(4)

where X = [x1, x2, …, xm] and xi as the i-th column
vector.

where b was the time location, s was the scale
factor and it was inversely proportionate to thefrequency (s > 0), f(t) was the mother wavelet, andsymbol * indicates a complex conjugate.

Jacobi iterative method was a procedure fornumerical determination of eigenvalues andeigenvectors of correlation matrix R. The method
looked for an orthogonal matrix Φ such that as in
Eq. (7),C (b, s ) =∫ x(t ) f  s dts−∞

The most important properties of the motherwavelet f(t) consisted of integrability, square
integrability, continuity, progressivity, also it has
no d.c. component [21]. Moreover, f(t) has to befocused in both time and frequency as much as
possible. It is renowned that the smallest time
bandwidth product is achieved by the Gaussianfunction [22]. Morlet wavelet as in Eq. (5),f (t ) = e−2i π f0 t

−t 2
e 2

(5)

was a complex exponential modulated Gaussianfunction, where f0 was 0.849 Hz [23]. The mosttrustworthy wavelet for the time-frequency of theheart sound signals was the Morlet wavelet [24].
The result of CWT, in the matrix feature form,
was a large amount of wavelet coefficients that wasfunction of position and scale (or b and s,
respectively: see Eq. (4)). In color spectrum, thecoefficients were depicted as magnitude
normalization value between 0.0 until 1.0, where
red and purple indicated the highest and the lowest
intensity, respectively. The feature extraction of
wavelet coefficients was 200 × 100 (time × scale)ΦT R Φ = Λ(7)yielded a diagonal matrix Λ, where Λ = diag[λ1, λ2,…, λm] and Φ = [φ1, φ2, …, φm] were eigenvalues
and eigenvectors, respectively. The method wasbased on a series of rotations (called Jacobi orGivens rotations) which were chosen to eliminate
off-diagonal elements while preserving the
eigenvalues. A Givens rotation was represented bya matrix of the form
1



O




b
v



1



Φ(i, j , α ) = 
O



−v
b


1



O 


1 

where all diagonal elements were unity from twoelements b in rows i and j, and all off-diagonal

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ISSN: 1992-8645

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elements were zero apart from the elements v and –v (in rows and columns i and j). thus, if φi and φjwere the i-th and j-th vectors of an orthonormal
basis and if b = cos α and v = sin α then Φ
represented a rotation of angle α in the (oriented)(φi, φj)-plane, which leaves all other basis vector
unchanged.
The procedure started with correlation matrix
R0 = R, and produced a sequence of
matrices R k = ΦTk R k −1Φ k , where at the k-th step
Φ k was a plane rotation constructed to annihilate
the maximal off-diagonal entry in Rk-1. In
particular, if rij was the entry of maximal magnitudein Rk-1, then Φ k was the rotator in the (φi, φj)-plane
define by setting
φii − φ jj
1β
, where β =and b =
v=
2
2
2φij
1+ β
1+ β

(

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where F = [f1, f2, …, fm] , fi as the i-th column
vector, and Z denoted the matrix of scaled originalvariables of the form Eq. (12):Z ij =

X ij − a j
sj

(12)

where ai and si were the mean and standard
deviation of the i-th variable.

)

For m > 2, as in Eq. (8),

R 'k

k

2
2 

'
≤ 1 − 2 R k → 0 as k → ∞FF m −m2

where R 'k

2
F

(8)

was the Frobenius matrix norm.

Therefore, if Φ (k ) was the orthogonal matrix definedby Φ ( k ) = Φ1Φ 2 LΦ k , then in the form Eq. (9),Tlim Φ ( k ) RΦ( k ) = lim R k = Λ

k →∞

k →∞

was a diagonal matrix.
The eigenvalues were ordered (λ1 > λ2 > … > λm)and were the variances of the principal factorsm(eigenvectors). Their sum∑ λi was equal to m.
i =1

Therefore, the percentage of variance associatedλi.
m
If Φ was the matrix of eigenvectors of R, thecorrelation coefficient between variable xi andfactor fj (sometimes called loading) was calculatedusing Eq. (10):with the i-th factor was equal toRφij = φij λ j(10)

The coordinates of the principal components(sometimes called scores) were specified in Eq.
(11):
F = ZΦ

Figure 2: An example of segmentation. (a) A heartsound signal of normal subject and its Shannon
envelope (red curve). (b) Single cycle, systolic, anddiastolic were obtained from the segmentation result

(9)

(11)

Assuming that i-th principal component fi, andthe ordering of all the principal components ofinterest was such that the first principal component
was associated with the largest variance in the input
data, the second principal component was
associated with the second-largest variance in the
input data, and so on. Furthermore, discard any
components that only account for a small
proportion of the variation in the data.
In this study, the n × m matrix X was acollection of wavelet coefficients (matrix featureform) as shown in Eq. (4), where m = 200 was
amount of time location (variables) and n = 100
was amount of scale factor (samples). A set ofvariables x1, x2, …, x200,which may be correlated.
From Eq. (11), principal components f1, f2, …, f200
were obtained. These variables were uncorrelated.
Using PCA as an orthogonal linear transformationthat transform the collection of wavelet coefficientsto a new coordinate system such that the greatest

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eigenvalue by any projection of the collection of
wavelet coefficients comes to lie on the firstcoordinate, the second greatest eigenvalue on thesecond coordinate, and so on. Furthermore, discard
any components that only had small percentage ofvariance,i.e.,

λiassociated
with
the
i-th
200
component fi. Hence, feature vectors that had great
variances would be input to classifier network.
2.2.5 Classifiers

Each of three methods utilized a unique signal
processing technique to analyze a certain
characteristic of the heart sounds for classification
purposes.
The FCM was a clustering algorithm that
proposed by Bezdek as one of the greatest usedmethods in fuzzy clustering [26]. The objective of
FCM was to locate cluster centers (centroids) thatminimize an error function. The FCM utilized
fuzzy partitioning such that a data point could bepermissible belongs to all groups with distinct
membership values between 0.0 and 1.0. FCM wasa recurrent algorithm. For instance, the FCM
algorithm minimized the least-squares function thatwas specified by a generalized among groups sumof square errors function where n was amount of
individuals to be clustered, c was amount ofclusters, uik was the degree of membership ofindividual k in cluster i, xk was the vector of hcharacteristics for individual k, vi was the vector of
the cluster means of the h characteristics for cluster
i, and w was the weighting for exponent. The sumof squared errors was calculated using Eq. (13):∑∑ (uik )w ( xk − vi )
n

c

2

(13)

k =1 i =1

and FCMs objective was to minimize Eq. (13).
The HCM was one of the simplest
unsupervised learning algorithms to solve theclustering problem [26]. The method followed aplain and easy manner to classify a given data set
thru a certain number of clusters fixed a priori. The
main idea was to define k centroids, one for eachcluster. Each point belonging to the given data set
was related to the nearest centroid. When no point
was pending, the first step was completed and an
early group was done. At this point, to recalculate k
new centroids as centers of the clusters resulting
from the previous step was required. After these k
new centroids have been acquired, a new binding
has to be finished between the nearest new centroid
and the same data set points. A loop has been

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yielded. To inspect that the k centroids changed
their location step by step until no more changes aredone.
In this study, clustering criterion was the sumof the squared Euclidean distances between thecentroid and each data point, as in Eq. (14):N

M

E (m1 , m2 ,..., mM ) = ∑∑ xi − mk

2

(14)

i =1 k =1

where X = {x1,x2, . . . ,xN}, xN ∈ ℜd, M was thecluster numbers, {m1,m2, . . . ,mM} was thecentroids. The criterion was termed clustering error
and depended on the centroids.
The MLP-BP was a supervised learningalgorithm. The algorithm uses feed-forward
networks which utilizes of target values or teacher
signals [27]. The algorithm was principally agradient descent method and its purpose was tominimize the mean squared error between theactual output of the network and the target values.
The MLP-BP algorithm mainly involved twostages. Firstly, the forward propagation stage wasno learning and the network would certainly yieldan erroneous value at its output before learning.
Secondly, the backward propagation stage waslearning network. The value of the actual output
was then compared to a target value and the error
yielded was then back propagated thru the layers of
the network. The whole process was then iterated as
far as the requisite convergence or when the error
limit was achieved. In this study, the algorithm wasutilized to classify the features into normal, systolicmurmur, diastolic murmur, as well as continuous
murmur. Each feature vector was classified using athree layer neural network. The input layer
consisted of 20.000 neurons and 300 neurons,
respectively, one hidden layer that contained 50
hidden neurons, whereas the output layer was
specified to 4 neurons for the four classes. The
target output for the trained pattern of the fourclasses was of Boolean type where [1, 0, 0, 0]
representing normal class, [0, 1, 0, 0] as systolicmurmur class, [0, 0, 1, 0] as diastolic murmur class,and [0, 0, 0, 1] as continuous murmur class,respectively. Bipolar sigmoid activation function
was conducted. Training was performed as far as
the mean error falls below 0.001 and learning rate
was 0.5.
2.3 Performance Evaluation of Classification
Methods

Evaluation
methods
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utilized. The methods were sensitivity, specificity,
and accuracy measurements [28]. If the systemtried to classify subjects to see if they had a certain
disease, there were four possible outcomes.
Subjects with the disease could be classified tohave the disease (true positives) or they could beclassified not to have the disease (false positives).
Subjects who did not have the disease could beclassified to either had the disease (false negative)
or not (true negative). Thus, amount of true
positives (TP), true negatives (TN), false positives(FP) and false negatives (FN) covered the wholeset.
Furthermore, the sensitivity was a measure ofpercentage of unhealthy subjects (who had thedisease) that were recognized as unhealthy, of theform Eq. (15),Sensitivit y =TP%TP + FN(15)

The specificity was a measure of percentage ofhealthy subjects (who had no the disease) that wererecognize healthy, as in Eq. (16),Specificit y =TN%TN + FP(16)

Additionally, the accuracy was a measure ofratio percentage of amount of samples correctlyclassified to the total amount of samples (N), as in
Eq. (17),

Accuracy =3.

TP + TN%N(17)

RESULTS

Figure 2 describes an example of heart soundnormal from segmentation result that associatedwith the cardiac cycle by utilizing Shannon
envelope. Determination of a thresholding value(dotted horizontal line on Fig. 2(b)) based on onset
until ending incidents of S1 and S2, respectively, as
well S1 onset of the following cycle, so that thesingle cycle, systolic, and diastolic were acquired.
From the segmentation results, 269 signals of thenormal and murmur subjects for single cycle,
systolic period, and also diastolic period wereobtained, respectively. In this study, the single
cycles of 70 subjects were allocated into twogroups. First group consisted of 44 subjects or 214
single cycles (87 N, 92 systolic murmurs, 17
diastolic murmurs, and 18 continuous murmurs)
and was utilized for classification training of MLPBP network. Second group was conducted for

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classification testing (MLP-BP, FCM, and HCM
networks) and contained 26 subjects or 155 single
cycles (66 N, 68 systolic murmurs, 11 diastolicmurmurs, and 10 continuous murmurs).
In our experimental results, each single cycleof the 269 cardiac cycles was extracted by utilizing
CWT method. Figure 3(a) depicts an example ofsingle cycle of the normal subject that consisted ofsystolic period (which comprises S1) and diastolicperiod (which contains S2). Furthermore, Figure3(c) shows artifacts in systolic period whichindicates the presence of systolic murmur at SA
subject. The contours of the artifacts are depicted
clearly in Fig. 3(d). Furthermore, Fig. 3(e)describes MS subject that contain murmur atdiastolic period. The contours of the diastolicmurmur are showed obviously in Fig. 3(f). Figure3(g) depicts an example of continuous murmur of
PDA subject that contain the artifacts in systolicperiod as well as diastolic period. The time-scale
map of this murmur is depicted at Fig. 3(h). All ofeach feature extraction results (i.e., 100 × 200
matrix form), would be input for classification as
20,000 features in a vector feature form.
The PCA was used to reduce dimension offeature vectors of CWT coefficients by picking
only the vectors that had great variances. In our
experimental results, high correlations existed
between the original variables (i.e. 200 variables),which were therefore not independent. According
to eigenvalues (Eq. (9)), the last 197 of 200
principal factors might be neglected since they
represented with average less than 12.55% of thetotal variance. The original variables depended
mainly on the first three factors. The PCA reduced
the 20,000 element feature vector into 300 element
feature vector. Therefore, each single cycle in any
the signal has 300 features as input to classifier
network.
Three classifiers, i.e., NN MLP-BP, FCM, andHCM were conducted to make their classification
decisions of a heart sound’s class based on signs
important and symptoms for heart diagnosis inknowledge of auscultation experts. Each classifierof the heart sounds was performed for classificationof normal, systolic murmur, diastolic murmur, as
well as continuous murmur. In our experimental
results, the classification results of the threeclassifiers from input of 20,000 and 300 features
are depicted in Table 1 and Table 2, respectively.
From the diagnosis of auscultation experts in eachclass as many as 155 single cycles (26 subjects), thethree classifier systems were calculated the input of
the feature vector. The classification results were229

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number of testing for each class. For 20,000
features (Table 1), 2 normal and 3 normal of single
cycles were classified incorrectly by FCM andHCM clustering, respectively. Furthermore, 1
continuous murmur of single cycle was classified
incorrectly by HCM clustering. For 300 features(Table 2), 2 normal of single cycles were classified
incorrectly by HCM clustering. Additionally, 3systolic murmurs, 9 systolic murmurs, and 11
systolic murmurs of single cycles were classified
incorrectly by NN MLP-BP, FCM, and HCM,
respectively.
The classification performances of threeclassifiers were evaluated with statistical validation
method. Table 3 shows classification performance
results from 20,000 and 300 features, respectively.
The ANN MLP-BP achieved the best performance(i.e. sensitivity, specificity, and accuracy) as an
automated classifier rather than FCM and HCM
methods. Moreover, the sensitivity, specificity, andaccuracy values, respectively, of each classifier
from 20,000 features were greater than 300
features. Moreover, decreased performance fromreduction of the features is described in Table 3.
The performances of three classifier systems weredecreased due to the feature reduction of PCA.
4.

DISCUSSIONS

Selecting the thresholding (Fig. 2(b)) would cut
off the signal at certain points to acquire thebeginning until the ending occurrences of S1 and S2,
respectively, also onset of S1 of the next cycle. Its
thresholding be conditional on cenv value. Variationof cenv of normal and pathological subjects
powerfully depended on the individual variationand measurement condition. Hence, we werearranged its cenv to have an appropriate thresholding
for consistent yields manually. In our study, thepathological heart sound recordings were very
complex and the phases of the heart sounds varylargely. Two of four times the recording on the MR
subject were omitted. The sudden release for a shorttime of the stethoscope from the subject caused in
imprecise detection. Additionally, the matters fortaking out the peaks, i.e., many extra peaks andartifact that look like apparent peaks both in timeinterval and amplitude. Large intensity murmur
overlapped with S1 or S2 would make the proper
identification and segmentation improbable.
Feature extraction was one of the mostimportant stages in classification, since even thebest classifier would perform poorly if the features
were not chosen well. In our previous study, signal

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processing and extensive characterization method
wavelet-based analysis were considered precious todescribe mechanisms of heart valves functions [2].
Time-scale maps yielded by CWT calculation wereresolved using magnitude thresholding operationand center of gravity to confine time-scale ofvalvular events. Duration of S1 and S2, S1-S2 spaced
time, events of closuring mitral and tricuspid valvesduring systole, closuring aortic and pulmonalvalves during diastole, and physiological split time
between valves activities in each cardiac sub-cycle
could be characterized exactly. The method wascapable to distinguish objectively and clearly
between normal and pathological heart sounds.
Therefore, in this study, the CWT method was
performed to extract the heart sound essential
characteristics. The mechanical events of the heartsounds relating to a given normal and murmur wereconceived in a single cycle of the heart sound signal(e.g. Fig. 3(b), (d), (f), (h), respectively). Hence, themethod could provide detailed information of theheart sound during the cardiac cycle. Furthermore,
the calculation results of feature extraction would
be input for classification.
In this study, PCA was conducted to reduce thedimension of the heart sound feature vectors by
picking only the vectors that had great variances,i.e., the first three factors of 200 principal factors.
Therefore, the PCA reduced the 20,000 element
feature vector from coefficients of wavelet into 300
element feature vector. Using PCA could be easily
calculated and had less computational load (i.e.
input 300 features) that compared to before feature
reduction (i.e. input 20,000 features) duringclassification process. However, input 300 features
resulted in declining classification performance ofthe three classifiers as shown in Table 3. Moreover,each decreasing maximum for sensitivity,
specificity, and accuracy, respectively, is 2.55%(FCM method), 1.77% (HCM method), and 2.90%(HCM method). This showed trade-off between
computational load and classification performance.
However, a decrease in the classification
performance as a result of the feature reduction was
not significant, because a very small percentage.
Therefore, overall of the proposed method, input of
300 features for classification more potential as ascreening for the heart diagnosis, because it had
less computational load.
Sensitivity and specificity as shown in Eq. (15)and Eq. (16), respectively, are measurements of
how good of a medical test result. In other words,
both equations ((15) and (16)) state that if thesubject’s disease status is known, then sensitivity

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means that “I know my subject has the disease.
What is the chance that the test will show that my
subject has it?” Furthermore, specificity means that
“I know my subject doesn’t have the disease. Whatis the chance that the test will show that my subject
doesn’t have it?” For the system performance,
higher sensitivity decreases the number of subjects
with an unhealthy heart that are told they arehealthy. On the other side, higher specificity
decreases the number of subjects with healthy heartthat are told they are unhealthy. In our experimental
results, both sensitivity and specificity of the MLPBP, as well as FCM and HCM, were better thanVos et al. [29]. They experimental results were90% and 96.5%, respectively, less than the values
are shown in Table 3.
In previous studies, Chebil et al. [16] andGupta et al. [17] have efficiently used the Artificial
Neural Network (ANN) for detecting andclassifying the heart sounds. From research of
Chebil et al., 21 signals for training (i.e. 7N and 14SA) and 61 signals for testing (1N, 32 systolicmurmurs, and 28 diastolic murmurs) were used[16]. The study utilized discrete wavelet transform
to identify S1, S2 and murmurs. Energy of the signal
within the systole and the diastole was conducted.
If the energy exceeds a specified threshold in eachinterval, then the heart sound signal was classified
as having systolic murmur or diastolic murmur.
Otherwise, the signal was classified as normal.
However, the accuracy of the classification results
was 92.3%. Its accuracy value was less than of ourexperimental results, as shown in Table 3.
Furthermore, Gupta et al. have classified a three
way for normal, systolic murmur, and diastolicmurmur using MLP-BP [17]. The architecture of
network consisted of 12 input nodes, 1 hidden layer
with 12 nodes, 3 output nodes. Moreover, 41
subjects (340 single cycles) consisted of 135 cycles
for training (45N, 45 systolic murmurs, and 45
diastolic murmurs) and 205 for testing (65N, 79
systolic murmurs, and 61 diastolic murmurs) wereconducted. A classification accuracy of 94.5% wasachieved [17]. However, in our study (Table 3), theaccuracy was better than Gupta et al., i.e. 100%(from input 20,000 features) and 99.03% (frominput 300 features). Therefore, increment of ourclassification performances achieved into a moresensitive and could be more rigorous in analyzing
abnormalities that appear in essential characteristicsof the heart sounds for heart diagnosis.
Cardiac auscultatory was regarded as aprominent tool for medical doctors in acquiring aprecise cardiovascular evaluation and confirming a

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cost-effective approach in outpatient-based
medicine. Several recent researches evaluating theperformance of clinicians in training have shown
unsatisfactory results for cardiac auscultatory [3034]. Expert cardiologists had an auscultatory
accuracy of roughly 80% [30]. Furthermore, theauscultatory accuracy of primary care doctors was
20% up to 69% [31-34]. In this study, all the threeclassifiers systems clearly had far better accuracy
than the primary care doctors as depicted in Table
3. Our proposed method would improve theaccuracy of auscultation by providing objective
additional information and could be assistance forphysicians to make their ultimate diagnoses.
Role of screening technique for the early
detection of heart diseases was very important toprevent sudden cardiac death. Phonocardiogram
screening of detection and classification of thesystolic murmur using a neural network forelementary school children was performed in Japan[35], [36]. Furthermore, Yoshinaga has studied
about school-based cardiovascular screening tochildren and adolescents who life-threatening
cardiovascular diseases [37]. In the first of threestages of screening method, phonocardiography as
the early detection and diagnosis of heart disease
was performed. In this research, our proposed
method was considered significant potential as ascreening alternative technique. The proposed
method could aid general doctors to arise with moreaccurate and trustworthy diagnosis at early stages
for massive examination.
In previous study, Cholkraisuwat et al. havedetermined prevalence of unrecognized congenital
heart disease among elementary school students
and study the types and frequency of congenital
heart disease in rural Thailand using trained nurses
and other health care personnel [38]. Five hundred
forty eight students were suspected to have heartdiseases and 102 students were diagnosed ascongenital heart disease. Simple acyanotic lesions(VSD, ASD, and pulmonary stenosis) werecommon in this study (32.35%, 16.67%, and11.76%, respectively). Qualified nurses or healthcare workers could be useful for identifying
patients with congenital heart disease. Therefore,
the role of these personnel in the national heart
screening program should be further clarified. In
Indonesia, many rural clinics were run by nurses,
because the limited availability of physicians. The
nurses often work in communities where they werecalled on to perform duties usually done byphysicians and auscultation may be the only means
available. Our proposed method could be benefit

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for computer-assisted diagnosis of cardiac disorders
and very useful as an alternative diagnostic tool thatcomplies with the requirements for rural clinics.
Investigation to clarify cardiac auscultatory
expertise for 18 family practice residents thru a
Cardionics CardioSim Digital Heart Sound
Simulator® (Cardionics, Inc., Houston) in the studyof auscultatory technique was performed [39]. The
recording of simulated heart sounds was utilized in
the educational intervention and evaluation,
including SA (systolic murmur), MS (diastolicmurmur), and PDA (continuous murmur) from 14
cardiac auscultation findings. The participants wereencouraged to listen with their stethoscopes to eachsound over the speaker of a standard commercial
portable home stereo system. Although theparticipants in small-group discussion sessions andrepetitive auscultation could improve resident's
ability to identify heart sounds audiotaped, but
study of Horiszny was only restricted in teaching
auditory and undertaken a simulator [39]. Whereas,in our research, systolic murmur, diastolic murmur,
and continuous murmur could be clarified more
simply recognized visually (e.g. Fig. 3), in theusage of temporal-frequency related parameters(such as our previous studies [2], [11]), also
auditory inspection. Additionally, in our previous
research, surrogate data modeling of normal heartsounds was created to extract knowledge aboutcharacteristics and mechanisms of generation of thehuman heart sound signals. The model described
more objectively how the normal heart sounds werereproduced by utilizing of random phases [40].
Furthermore, our exploration was addressed tomodeling of surrogate pathological heart soundsignals for further research in related toclassification the signal and very useful for analternative tool of the heart sound simulator. Thus,
our study was considered beneficial to aid in the
study of auscultatory method from the signal
processing manner viewpoint.
Our proposed method was suitable to classify
of normal and pathological heart sound signals ofhuman subjects due to signs important andsymptoms for heart diagnosis based on knowledgeof auscultation experts. The wavelet analysis could
provide detailed information of the heart soundduring the cardiac cycle as input for classification.
Although the heart sound data were collectedpartially from cassette of literature [13], the noisefrom the circumstance and wires could still affect
the quality of recording. This could affect theclassification of the signals. From the high ofclassification result, it was denoting that the

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wavelet analysis was adequate for classifying into
normal or pathological occurrences. Additionally,
the three classifiers (ANN MLP-BP, FCM andHCM) that showed significant potential in their use
as an alternative diagnostic tool were compared.
The ANN achieved the best performance as an
automated classifier rather than FCM and HCM
methods. The proposed method was consideredvery important for objective screening and very
useful as an alternative diagnostic tool thatcomplies with the requirements for rural clinics.
Moreover, the method would be helpful in study of
auscultation technique for medical students.
Surrogate data modeling of pathological heartsounds signals as an alternative tool of the heartsound simulator and for classification purpose wasfurther study.
5.

CONCLUSIONS

We have developed a screening technique thatsuitable to classify of normal and pathological heartsound signals of human subjects due to signs
important and symptoms for heart diagnosis basedon knowledge of auscultation experts. Threecomputerized algorithm classifiers that showedsignificant potential in their use as an alternative
diagnostic tool were compared. The ANN MLP-BP
achieved the best performance as an automatedclassifier rather than FCM and HCM methods. The
performance of ANN was 100% for sensitivity,
specificity, and accuracy, respectively, of input
20,000 features. Furthermore, for input 300
features, the performance of ANN was 98.90%,
99.37%, and 99.03% for sensitivity, specificity, andaccuracy, respectively. The proposed method wasconsidered very important for objective screening
and useful as an alternative diagnostic tool thatcomplies with the requirements for rural clinics.
We hoped that the method would be beneficial instudy of auscultatory technique for medical
students. Next topic of our study was modeledsurrogate data of pathological heart sound signals
as an alternative tool of the heart sound simulator.

ACKNOWLEDGMENT

We express our deepest gratitude to Somali,
MD for his assistance and permission to collectdata at Kebonjati Hospital, Indonesia.

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Table 1: Classification Percentage Of 155 Single Cycles (26 Subjects) Before Using PCA
MethodDiagnosis / Testing Class (%)
Systolic
Diastolicmurmurmurmur68 / 68 (100.00)
11 / 11 (100.00)
68 / 68 (100.00)
11 / 11 (100.00)
68 / 68 (100.00)
11 / 11 (100.00)

NormalNN MLP-BP
FCM
HCM

66 / 66 (100.00)
66 / 64 (96.97)
66 / 63 (95.45)

Continuous
murmur10 / 10 (100.00)
10 / 10 (100.00)
10 / 9 (90.00)

Table 2: Classification Percentage Of 155 Single Cycles (26 Subjects) After Using PCA
MethodNN MLP-BP
FCM
HCM

Normal66 / 66 (100.00)
66 / 66 (100.00)
66 / 64 (96.97)

Diagnosis / Testing Class (%)
Systolic
Diastolicmurmurmurmur68 / 65 (95.59)
11 / 11 (100.00)
68 / 59 (86.76)
11 / 11 (100.00)
68 / 57 (83.82)
11 / 11 (100.00)

Continuous
murmur10 / 10 (100.00)
10 / 10 (100.00)
10 / 10 (100.00)

Table 3: Classification Performance Of The Classifier Network
20,000 features
MethodNN MLP-BP
FCM
HCM

Sensitivity(%)
100.0099.24
96.36

Specificity(%)
100.0099.54
99.20

Decreased performance fromreduction of the features

300 features
Accuracy(%)
100.0099.35
98.71

Sensitivity
(%)
98.90
96.69
95.20

235

Specificity
(%)
99.37
98.12
97.43

Accuracy
(%)
99.03
97.10
95.81

Sensitivity
(%)
1.10
2.55
1.17

Specificity
(%)
0.63
1.42
1.77

Accuracy
(%)
0.97
2.26
2.90

Journal of Theoretical and Applied Information Technology
15th August 2016. Vol.90. No.1

© 2005 - 2016 JATIT & LLS. All rights reserved.

ISSN: 1992-8645

www.jatit.org

E-ISSN: 1817-3195

Figure 3: (A), (C), (E), And (G) Example Of Single Cycle Of The Normal, Systolic Murmur (SA),Diastolic Murmur (MS), And Continuous Murmur (PDA) Subjects, Respectively. (B) Coefficients Of CWT
For (A) In Color Spectrum. The Color Spectrum Is Magnitude Normalization Of Wavelet Coefficient
Between 0.0 And 1.0, Where Purple And Red Represent The Lowest And The Highest Energy,
Respectively. (D). Coefficients Of Wavelet For (C). A Format Alike With Fig. 3(B). (F) Coefficients Of
Wavelet For (E), Where The Format Alike With Fig. 3(B). (H) Coefficients Of Wavelet For (G), Where
The Format Alike With Fig. 3(B)236