International Review of Electrical Engineering (I.R.E.E.), Vol. 11, N. 1
ISSN 1827- 6660
January – February 2016

Signal Processing and Extensive Characterization Methodof Heart Sounds Based on Wavelet Analysis
R. Hendradi1, A. Arifin1, 5, H. Shida2, S. Gunawan3, M. H. Purnomo1,
H. Hasegawa4, H. Kanai2
Abstract – One of the valuable methods of cardiac valve diagnosis can be performed by
auscultation. We proposed a signal processing and extensive characterization method based on
wavelet analysis to investigate important characteristics of heart sounds of normal and pathologic
systolic murmur human subjects. Time-scale maps yielded by wavelet transform calculation weresolved using magnitude thresholding operation and centre of gravity to restrict temporal andfrequency-related of valvular activities. From our experimental results, temporal and frequencyrelated parameters of S1, S2, and their components could be characterized precisely. Applicationof our method was adequate to characterize the heart sounds objectively, clearly, systematically,
and comprehensively. The method was considered valuable to explain mechanisms of cardiac
valves functions. We expected that the method would be helpful for clinical diagnosis as well as
developing of heart sound modelling and educational purpose. Next topic of our study wasaddressed for classification of the heart sounds. Copyright © 2016 Praise Worthy Prize S.r.l. - Allrights reserved.

Keywords: Centre of Gravity, Heart Sound, Thresholding Method, Wavelet Transform

S(Ex(t))Nomenclature
A2
C(b,a)
ccon
cenv
Ex(t)
g
mmax
mmin
M1
M(Ex(t))
pmax
pmin
P2
P(t)
sCOG
sh
sl
srg
S1
S2
tCOG
tdur
th
tl
T1

Aortic component
Wavelet coefficients of time location b andscale factor aThresholding coefficient of contour
Thresholding coefficient of the Shannon
envelope
Average Shannon energy for frame tMother wavelet
Maximum of the normalized magnitude
Minimum of the normalized magnitude
Mitral component
Mean of average Shannon energy for frame t
Maxima of Shannon envelope insegmentation with respect to a cardiac cycle
Minima of Shannon envelope in segmentation
with respect to a cardiac cycle
Pulmonic component
Shannon envelope for frame t
Scale-coordinate of Centre of Gravity
Higher scale
Lower scale
Frequency range in scaleFirst heart soundSecond heart soundTime-coordinate of Centre of Gravity
Duration in s
Higher time
Lower time
Tricuspid componentx(i)xnorm(i)Standard deviation of average Shannon
energy for frame t
ith original heart sound signal
ith normalized signal of heart soundI.

Introduction

Heart disease is the second most lethal disease after
cerebrovascular accident [1]. An early, non-invasive
detection of the heart diseases, with particular proceduresand follow-up treatments can prevent increasing of fatalrisks of the heart attacks [2]-[5]. One of the proceduresfor early detection of heart disease can be conducted
through diagnosis of heart sounds [1], [6].
Heart sounds are the sounds yielded by somemechanical activities of the heart during a cardiac cycle.
The factors that may be connected with the productionof the heart sounds, i.e., cardiac muscles contraction,
movements of the cardiac valves, movements of cardiac
wall, and flows of the blood through the chambers andblood vessels of the heart. The cardiac cycle has twophases: systole and diastole. During systole, an active
full contraction of ventricles that forces blood out fromthe heart; during diastole, a relaxation of the ventricles
and the heart is filled with blood. A normal functioning
heart comprises of two basic heart sounds: S1 as a firstheart sound and S2 as a second heart sound [7].
Generation of S1 sound coincides with contraction of
the ventricles, thus identifying the onset of ventricularsystole and the end of mechanical diastole.

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reserved

DOI: 10.15866/iree.v11i1.8138

55

R. Hendradi et al.

The S1 sound is produced of compound in nature by
three stages [8], [9]: firstly, low-frequency beginning,
due to myocardial tension. Secondly, higher-frequency
central stage, due to valvular events; the central stage
contains at least four vibrations that correlate with the
motion of the four valves in the following order: mitral(M1) closing, tricuspid (T1) closing, pulmonic (P2)
opening, and aortic (A2) opening.
Only the first two are normally audible. Thirdly, lowfrequency final stage, due to vascular phenomena that
produced by rapid ejection of blood. The vibrations of
the S2 identify the onset of ventricular diastole and occur
at the end of mechanical systole.
The S2 sound is generated of three stages as well as the
S1 sound [8], [9]: firstly, low-frequency beginning, due toeddies preceding the valvular closure. Secondly, central
stage of higher-frequency, due to closing of thesemilunar valves (A2 and P2). Thirdly, final stage of lowfrequency, because of final vibrations and opening of theatrioventricular valves (T1 and M1).
If opening of heart valve is not perfect or stenosis
which force blood through a narrow opening, or
regurgitation caused by valve closure is not perfect andresulted in backflow of blood, then will be resulted
additional sound. An Abnormal heart sound arises as aresult of turbulence flow of blood that passes throughnarrow openings is called murmur [7].
One of the valuable methods of cardiac valve
diagnosis can be performed by auscultation [1], [6].
The auscultation is a traditional technique in clinicaldiagnosis that is performed by listening to the heartsounds using a stethoscope. The heart sounds analysis by
auscultation technique is a difficult skill. Accurateness of
the analysis relies greatly on the expertise and practicalknowledge of a listener that takes years to acquire andrefine [10]. Because this skill is highly tough to be taught
in a structured way, so that hearing of a cardiologist withanother one who cannot be copied, although thistechnique has been studied both in educational andclinical aspects in medicine through standard literatures[7], [11], [12]. Therefore, the above problems andsubjectivity of a cardiologist become deteriorations ofauscultation technique.
Limitations of traditional auscultation have motivatedus to develop a signal processing method that exploredessential characteristics of the heart sounds. In previous
research, Fast Fourier Transform (FFT) presented
valuable frequency information about M1 and T1 (the S1components) also A2 and P2 (the S2 components) of anormal heart sound [13]. Nevertheless, the FFT did notallocate frequency content evidence localized in time, so
it could not reveal which of two valves were closed
firstly. Moreover, Lee et al. [14] have investigated theexact features of heart sound using Short Time Fourier
Transform (STFT). However, STFT could not perceive
M1 and T1 also imprecisely identified A2 and P2.
This problem was caused by selection of length of atime window and a trade-off between time and frequency
resolutions.

Debbal and Bereksi-Reguig [13], [15] and Vikhe et al.
[16] analysed the A2-P2 split using Wavelet Transform
method. However, the method did not clearly explain
how to measure their split time. Furthermore, there werelack of measuring data only at adjacent condition in timeof their time-scale maps of A2 and P2, whereas both timescale maps could also be appeared as a single component
and overlapping conditions.
We proposed a signal processing and extensive
characterization method based on wavelet analysis toinvestigate essential characteristics of the heart sounds ofnormal and pathologic systolic murmur human subjects.
Time-scale maps yielded by wavelet transform
calculation were solved using magnitude thresholding
operation and Centre of Gravity (COG) to restrict
temporal and frequency-related evidence of valvular
activities.
The temporal and frequency-related parameters are
duration of S1 and S2, S1-S2 spaced time, events of M1 andT1 during systole, A2 and P2 during diastole, andphysiological split time between valve events in eachcardiac sub-cycle. The COG of each thresholding
wavelet transform contour was used to acquireappropriately the S1-S2 spaced time, the M1-T1 split, theA2-P2 split, respectively. Hence, characteristics of eachpattern of the heart sounds can be extracted more
precisely. The proposed method would be useful for
diagnosis and for understanding about mechanisms ofcardiac valves function. Moreover, the method promises
an important role in development of heart soundmodelling and study of auscultation technique for
medical students.

II.
II.1.

Methods
Data Collection

The heart sound recordings were consisted of twoparts. One was collected from an acoustic transducer of
ALOKA ProSound II SSD-6500 SV and the recordings
were digitized with 1 kHz sampling frequency.
The other was from 3M Litmann Model 4100
electronic stethoscope and the recordings were digitizedwith 8 kHz sampling frequency.
Two subjects (men, age: 23 and 39 years, body height:
172 and 170 cm, body weight: 57 and 76 kg) without amurmur (normal) and two other subjects (man andwoman, age: 54 and 54 years, body height: 160 and 160
cm, body weight: 63 and 67 kg) of pathologic systolicmurmur, i.e., mitral valve regurgitation (MR) andtricuspid valve regurgitation (TR), respectively, wereparticipated in this study. Purpose of an experiment was
explained to each subject and subject’s consent was
obtained.
The heart sound recording was performed at four
different auscultation areas, i.e. mitral, tricuspid, aortic,
and pulmonary areas for both normal subjects to acquire
best sounds. Moreover, both murmur subjects wereachieved at mitral area for MR subject and tricuspid area
for TR subject by a cardiologist.

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reservedInternational Review of Electrical Engineering, Vol. 11, N. 1

56

R. Hendradi et al.

(a)Fig. 1. Auscultation sites. AO = aortic area; PA = pulmonic area; RV =right ventricle area; LV = left ventricle area; 1 = right second
intercostal space; 2 = left second intercostal space; 3 = mid-left sternalborder (tricuspid area); 4 = fifth intercostal space, midclavicular line
(mitral area)

Time (s)(b)
Figs. 2. Illustration of mechanism of segmentation. (a) Envelope curveand heart sound signal. (b) Definition of systolic, diastolic, and single
cycle from its envelope curve

The aortic area sited in the second intercostal spacealong the right sternal border, as shown in Fig. 1.
The pulmonic area sited in the second intercostal
space at the left sternal border. The tricuspid area locatedin the fifth intercostal space along the left sternal border.
The mitral area sited in the fifth intercostal space near
the midclavicular line. The recording was conducted in
two different conditions, inspiratory and expiratory
apnea, to reduce noise from breathing [17].
The recording was obtained in a quiet recording
environment from the sitting upright subject (for thenormal subjects) and from the supine subject on a bed(for the murmur subjects).
II.2.

II.4.

Segmentation with respect to the cardiac cycle was animportant step in analysing of the heart sounds. Detection
the location and intervals of S1 and S2 could be very
helpful information about the condition of themechanical activity of the heart. Mechanism ofsegmentation is described on Figs. 2 using normalized
average Shannon energy. The original signal would be
expressed in more simply and easily seen by utilizing of
the envelope curve based on the S1 and the S2 (Fig. 2(a)).
Shannon envelope, i.e., the normalized average
Shannon energy is the popular envelope technique.
The method was capable to attenuate the effect of low
value noise and to make the low intensity sounds easier
to be found [19]. The algorithm was depicted infollowing. Firstly, an original heart sound was
normalized by absolute maximum of the signal,
max x   , as in Eq. (1):Signal Processing

Signal processing of the heart sounds consisted ofthree steps as follows: pre-processing for filtering,
segmentation with respect to the cardiac cycle, andfeature extraction to restrict temporal and frequencyrelated of valvular events. In this study, the signal
processing method was performed using Pascal
programming language.
II.3.

Segmentation

xnorm  i  x i 



max x  (1)Pre-processing
Secondly, the average Shannon energy was calculatedusing Eq. (2):

Two phases filtering to decrease interference caused
by background noise were performed. Firstly, a bandpass
IIR filter of eighth-order Butterworth with cut-off 20 Hz
and 330 Hz was used from an original signal.
The original signal of 8 kHz sampling frequency wasdecimated by factor 4 to 2 kHz. Picking of bandwidthadequate with [12]. Furthermore, segmentation of thesignal based on envelope curve that would be explainedlater. Secondly, algorithm of wavelet denoising of the
fixed threshold depicted in Hall et al. [18] wasimplemented for enhancing quality of the signal.
A Daubechies wavelet of order 4 (Db4) with fifth
decomposition level was selected.

Ex  

1 N 2
2
xnorm  i   log xnorm
i 
N i 1(2)

where xnorm(i) was the ith normalized signal and N was thenumber of points in 0.02 s segment (normal subject) and
0.0165 s segment (murmur subject), which corresponds
to a frame, here N = 20 (normal subject) and N = 33
(murmur subject). Thirdly, the average Shannon energy
was normalized over all of the frames, so the Shannon
envelope became Eq. (3):Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reservedInternational Review of Electrical Engineering, Vol. 11, N. 1

57

R. Hendradi et al.

P t  

Ex  t   M  Ex  t  S  Ex  t  (3)where Ex(t) was the average Shannon energy for frame t,M(Ex(t)) was the mean of Ex(t), and S(Ex(t)) was thestandard deviation of Ex(t) [19]. Fourthly, the Shannon
envelope was then filtered reverse direction.
The resulting sequence had precisely zero phase filter,so there was no time delay.
The amplitude peaks of S1, S2, and S1 of the next cyclefrom filtering of Shannon envelope were identified thatbased on visual inspection. Basic knowledge of the S1and S2 peaks identification that the diastole is longer than
the systole [11]. The peak adjacent to the longest interval(diastolic) was identified as S2. Furthermore, the peakadjacent to the shortest interval (systolic) was identified
as S1 [19]. Maximum among the three peaks, namely
pmax, was obtained. Then, minimum, pmin, of interval that
contained the three peaks was acquired. Furthermore, thethresholding (horizontal dotted line on Fig. 2(b)) was
done equal to pmin + cenv× pmax, where cenv was athresholding coefficient of the Shannon envelope.
Selection of cenv was set manually and varied (0.01 < cenv
< 0.50). Its selection based on onset until ending eventsof S1 and S2, respectively, also beginning of S1 of the next
cycle as shown in Fig. 2(b). The single cycle is timeinterval between beginning of S1 of a cycle andbeginning of S1 of the next cycle. The systolic is timeinterval between beginning of S1 until beginning of S2.
The diastolic is time interval between beginning of S2
until beginning of S1 of the next cycle (Fig. 2(b)).
II.5.

(a)

Time (s)(b)
Figs. 3. An example of segmenting signal. (a) A filtered normal signal
and its Shannon envelope. (b) The values for single cycle, systolic,and diastolic of the envelope curve

Function g(t) should fulfil the most crucial properties,i.e., continuity, integrability, square integrability,
progressivity and that it has no d.c. component [20].
Additionally, the mother wavelet g(t) has to beconcentrated in both time and frequency as much as
possible. It is acknowledged that the smallest time
bandwidth product is attained by the Gaussian function[21]. As pointed out of [22], Morlet wavelet was acomplex exponential modulated Gaussian function of theform Eq. (5):g t   eFeature Extraction

A feature extractor, i.e., Continuous wavelet transform(CWT) is suitable for the analysis of non-stationary heartsound signals. CWT uses a variable sized window
region. Because the wavelet may be compressed ordilated, different features of the signal are extracted.
While a stretched wavelet picks up on the lower
frequency components, a narrow wavelet extracts highfrequency components. The method is a multiresolution
analysis that can simultaneously represent time andfrequency information of the signals by using a flexible
window modulation. Hence, characteristics of eachpattern of the signals can be extracted more precisely.
Computation of CWT was related to the signal x(t)with families of time frequency atoms g(t); it formed aset of coefficients C(b,a) specified in Eq. (4):C  b,a  1* t b x t  g 
 dt
a 
 a 

i0t

t 2
e 2

(5)

2
 5.336 rad/s (f0 = 0.849 Hz).
ln 2
The most reliable wavelet for the time-frequency ofheart sound signals is the Morlet wavelet [23].
The wavelet coefficients C, in the matrix form, werefunction of both b and a (Eq. (4)). The wavelet
coefficients C, in colour spectrum, were depicted as
normalized magnitude [0,1], where the lowest and thehighest intensity correspond to purple and red,respectively. The pattern was 200  100 (time  scale)with scale factors 1.0  10-2 up to 1.0  10-1. In thisresearch, an extensive characterization method based on
wavelet analysis consisted of magnitude thresholding
operation and Centre of Gravity (COG). The magnitude
thresholding operation was conducted to acquire timescale parameters that correspond to characterization of
the heart sounds. When the normalized magnitude value
greater than or equal to a predetermined value, one ormore thresholding contours would be obtained. The
thresholding was done equal to mmin + ccon × mmax, where
mmin was minimum of the normalized magnitude, ccon
was thresholding coefficient of contour, and mmax was
maximum of the normalized magnitude.
where 0  

(4)



where b was the time location, a was the scale factor andit was inversely proportional to the frequency (a > 0),
symbol * represents a complex conjugate, and g(t) was
the mother wavelet.

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reservedInternational Review of Electrical Engineering, Vol. 11, N. 1

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R. Hendradi et al.

Selection of ccon was set manually and varied (0.01 <ccon < 0.99). Its selection depended on onset until ending
event as well as lowest to highest scale. The time-scale
maps of the thresholding method gave more simply andeasily seen. Therefore, low of contour time (tl), high ofcontour time (th), contour duration (tdur), low of contour
scale (sl), high of contour scale (sh), and contour scale
range (srg) could be obtained.
These parameters were associated with duration (tdur =th - tl) and frequency range (srg = sh - sl) of S1, S2, S1 andS2 components, respectively. Furthermore, COG is apivotal point that associated with the thresholding
contour. If COG coordinate of a collection of wavelet
coefficients was (tCOG, sCOG), time-coordinate of COG,
tCOG, was absis and scale-coordinate of COG, sCOG, wasordinate. The tCOG of each thresholding contour of S1, S2,
S1 and S2 components, was utilized to acquire the S1-S2
spaced time, the M1-T1 split, the A2-P2 split, respectively.
The S1-S2 spaced time was difference between tCOG of
S1 and tCOG of S2. The M1-T1 split was difference between
tCOG of M1 and tCOG of T1. The A2-P2 split was difference
between tCOG of A2 and tCOG of P2.
II.6.

of subjects. Moreover, the S1-S2 spaced time of thenormal subject is less than the murmur subject.
Additionally, S1 has wider srg and higher sh than S2 forthe three groups.
Two basic components of the S1 sound are the M1component and the T1 component. In this study, we haveacquired 81, 4, and 2 systoles that contained M1 and T1
from normal, MR, and TR data, respectively. Fig. 4(c)shows an example of the normal subject that tdur of M1 is
0.0347 sec., where tl is 0.0120 sec. (localized at 4.5% ofsystole position) and th is 0.0467 s (localized at 17.5% ofsystole position). Similarly, all calculations of tdur of M1
are shown in Table I. Fig. 4(c) also shows that tdur of T1 is
0.0788 s., where tl is 0.0254 s (localized at 9.5% ofsystole position) and th is 0.1041 s (localized at 39.0% ofsystole position).
All similar calculations of tdur of T1 are shown in Table
I. Furthermore, T1 has longer tdur than M1 for the normal
and MR except the TR subject. Moreover, M1 and T1
valves close at the beginning of ventricular systole. The
M1 valve usually closes slightly before the T1 valve (e.g.
Fig. 4(b)). Thus, both components of S1 can be
distinguished separately by time delay. The time delay
referred as physiological split time [9] or normal split
time. The time delay between them plays very vital rolein medical diagnosis. Fig. 4(c) shows that the M1-T1 split
is 0.0387 s, where tCOG of M1 is 0.0267 s (localized at
10.0% of systole position) and tCOG of T1 is 0.0654 s(localized at 24.5% of systole position) (in red crosses).
The similar result is shown in Table I for the threegroups of subjects. Furthermore, Fig. 4(c) shows that srgof the thresholding M1 contour is 0.0070, where sl is
0.0110 and sh is 0.0180. Whereas, srg of the thresholding
T1 contour is 0.0130, where sl is 0.0190 and sh is 0.0320.
Table I shows the similar result that T1 has wider srg andhigher sh than M1 for the three groups.
Two basic components of the S2 sound are the A2
component and the P2 component. Both of these valvesclose at the end of ventricular systole. Normally, the A2
valve closes slightly ahead of the P2 valve because
closing pressure is higher in the aorta than in thepulmonary artery. Therefore, A2 usually occurs earlier
and is louder than P2 (e.g. Fig. 4(e)), contrarily in somepathological cases A2 and P2 may be reversed in timeorder [7]. From the experimental data, we have acquired
15, 2, and 7 diastoles that contained A2 and P2 fromnormal, MR, and TR data, respectively. Fig. 4(e) shows
an example of the normal subject that tdur of A2 is 0.0259
s, where tl is 0.0022 s (localized at 0.5% of diastole
position) and th is 0.0281 s (localized at 6.5% of diastole
position). Similarly, all calculations of tdur of A2 areshown in Table I. Fig. 4(e) also shows that tdur of P2 is
0.0864 s, where tl is 0.0130 s (localized at 3.0% of
diastole position) and th is 0.0994 s (localized at 23.0%
of diastole position). All similar calculations of tdur of P2
are shown in Table I. Table I shows that tdur of P2 islonger than A2 except for MR subject. Fig. 4(f) showsthat srg of the thresholding A2 contour is 0.0050, where sl
is 0.0120 and sh is 0.0170.

Statistical Analysis

Results of the experimental data were expressed asmean ± SD. Student’s t-test (significance level: 0.001)
for normal distribution variables was used to compare
difference data between inspiratory apnea and expiratory
apnea groups in time domain and frequency domain (inscale). For each single cycle, S1, and S2, hypothetical test
was calculated based on the duration and the range scaleof each group. Whereas, M1 and T1 also A2 and P2 were
calculated based on tCOG and sCOG, respectively.

III. Results
Three groups of subjects: normal, MR and TR, weresegmented. From the segmentation algorithm based on
Shannon envelope, 137 signals of the normal, 9 signalsof the MR and 22 signals of the TR for single cycle,
systole, and diastole were obtained, respectively. Figs. 3
depict an example of segmenting signal that relates to thecardiac cycle using Shannon envelope of the normal
subject. Table I shows tdur of diastolic is longer thansystolic for the three groups. In this study, exploration ofimportant characteristics of the heart sounds, each single
cycle that contained both systole and diastole wereextracted by utilizing extensive characterization method
based on wavelet analysis. Figs. 4, Figs. 6, and Figs. 7
show example of normal, MR, and TR subjects,
respectively. These figures have S1 and S2 components atoverlapping conditions. Figs. 5 depict S1 components at
adjacent condition (Fig. 5(a)) and S2 appears like a single
component condition (Fig. 5(d)).
In our experimental results, from these time-scalemaps, the temporal and frequency-related parameters
could be localized. Table I shows that tdur of S1, in
systole, is longer than S2, in diastole for the three groups

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R. Hendradi et al.

TABLE ITEMPORAL AND SCALE PARAMETERS OF THE NORMAL, THE MR, AND THE TR SUBJECTS, RESPECTIVELY
Conditions of Normal
Conditions of MR
Conditions of TR
Parameter
Inspiratory apnea
Expiratory apnea Inspiratory Napnea Expiratory apnea Inspiratory apnea Expiratory apnea
tdur of single cycle (s)0.749±0.089
0.782±0.066
0.869±0.053
0.857±0.072
0.782±0.029
0.745±0.021
tdur of systolic (s)0.275±0.027
0.277±0.021
0.311±0.015
0.326±0.022
0.311±0.012
0.323±0.012
tdur of diastolic (s)0.475±0.072
0.505±0.055
0.559±0.042
0.531±0.058
0.471±0.022
0.422±0.015
tdur (s)0.123±0.033
0.125±0.030
0.141±0.017
0.176±0.049
0.137±0.026
0.126±0.013
sl
0.013±0.002
0.012±0.002
0.020±0.004
0.034±0.010
0.019±0.003
0.018±0.002
S1sh
0.037±0.007
0.037±0.007
0.050±0.002
0.068±0.003
0.065±0.013
0.057±0.006
srg
0.024±0.009
0.025±0.007
0.030±0.004
0.034±0.013
0.046±0.015
0.040±0.006
tdur (s)0.096±0.020
0.091±0.018
0.118±0.021
0.137±0.022
0.095±0.017
0.095±0.015
sl
0.012±0.001
0.012±0.002
0.025±0.007
0.023±0.001
0.026±0.003
0.026±0.007
S2
sh
0.034±0.005
0.033±0.005
0.048±0.004
0.060±0.003
0.048±0.006
0.044±0.006
srg
0.022±0.005
0.021±0.005
0.023±0.011
0.037±0.003
0.023±0.005
0.018±0.004
tdur (s)0.045±0.023
0.037±0.026
0.046±0.009
0.134±0.003
sl
0.015±0.002
0.013±0.002
0.028±0.002
0.023±0.000
M1sh0.024±0.005
0.021±0.003
0.042±0.005
0.043±0.001
srg
0.009±0.004
0.009±0.004
0.013±0.003
0.020±0.001
tdur (s)0.054±0.029
0.065±0.035
0.115±0.006
0.118±0.047
sl
0.018±0.006
0.019±0.004
0.044±0.006
0.053±0.001
T1
Sh
0.029±0.010
0.031±0.006
0.066±0.003
0.067±0.004
Srg
0.011±0.007
0.013±0.008
0.021±0.009
0.014±0.005
tdur (s)0.035±0.021
0.013±0.002
0.074±0.050
0.062±0.031
0.041±0.051
sl
0.012±0.001
0.014±0.001
0.023±0.003
0.024±0.003
0.017±0.003
A2sh
0.020±0.006
0.020±0.002
0.042±0.008
0.041±0.006
0.027±0.012
srg
0.007±0.006
0.005±0.002
0.019±0.005
0.017±0.008
0.010±0.011
tdur (s)0.071±0.016
0.068±0.016
0.038±0.026
0.040±0.004
0.085±0.020
sl
0.020±0.006
0.024±0.002
0.032±0.008
0.059±0.009
0.035±0.019
P2sh
0.033±0.005
0.033±0.002
0.040±0.016
0.074±0.005
0.051±0.018
srg
0.012±0.003
0.009±0.004
0.008±0.007
0.015±0.004
0.016±0.003
tCOG of S1 (s)0.064±0.011
0.061±0.012
0.062±0.008
0.111±0.019
0.062±0.015
0.054±0.007
tCOG of S2 (s)0.315±0.028
0.318±0.031
0.350±0.023
0.374±0.016
0.338±0.016
0.356±0.022
S1-S2 spaced time (s)0.251±0.029
0.257±0.024
0.287±0.021
0.264±0.010
0.276±0.015
0.302±0.021
tCOG of M1 (s)0.038±0.011
0.030±0.013
0.031±0.008
0.068±0.001
tCOG of T1 (s)0.072±0.016
0.068±0.014
0.128±0.034
0.105±0.012
M1-T1 split (s)0.034±0.017
0.040±0.016
0.096±0.027
0.037±0.011
tCOG of A2 (s)0.021±0.006
0.016±0.006
0.039±0.012
0.026±0.013
0.011±0.003
tCOG of P2 (s)0.055±0.013
0.068±0.001
0.087±0.031
0.024±0.006
0.056±0.018
A2-P2 split (s)0.034±0.013
0.052±0.006
0.049±0.018
0.011±0.004
0.045±0.015

Figs. 4. An example of S1 and S2 components at overlapping condition (in time) of the normal subject. (a) Example of well separated M1 and T1.
(b) Wavelet coefficients of (a) in colour spectrum. The colour spectrum is normalized magnitude of wavelet coefficient [0, 1], where the lowest
and the highest intensity correspond to purple and red, respectively. (c) The thresholding M1 and T1 contours of (b) and their COG coordinates (redcrosses). (d) Example of well separated A2 and P2. (e) Wavelet coefficients of (d). A format similar to that of Fig. 4(b)(f) The thresholding A2 and P2 contours of (e) and their COG coordinates (red crosses)

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R. Hendradi et al.

Figs. 5. An example of S1 components at adjacent condition and S2 appears like a single component (in time) of the normal subject. (a) Example ofseparated M1 and T1. (b) and (e) are wavelet coefficients for (a) and (d), respectively. A format similar to that of Figs. 4 is used.
(c) The thresholding M1 and T1 contours of (b) and their COG coordinates (red crosses). (d) S 2 appears like a single component.
(f) The thresholding S2 contour of (e) and its COG coordinate (red cross)

Whereas, srg of the thresholding P2 contour is 0.0130,
where its sl is 0.0160 and sh is 0.0290.
Table I shows the similar result that P2 has wider srg
and higher sh than A2 for normal and TR except MR
subject. For significance level 0.001 by paired sample ttest that there was no statistically significance difference
between inspiratory apnea group and expiratory apnea
group of each time-scale parameter, i.e. single cycle(except for the normal subject in tdur), S1, S2, also M1, T1,
A2, and P2 components (except for MR and TR subjects,
respectively, because each subject has no paired sample
group). For the time parameter of single cycle, there wastdur of its expiratory apnea longer than its inspiratory
apnea. This indicates that the recording can be conducted
on expiratory apnea or inspiratory apnea condition.

Figs. 6. An example of S1 and S2 components at overlapping condition (in time) of the MR subject. (a) and (d) are example of well separated M1
and T1, A2 and P2, respectively. (b) and (e) are wavelet coefficients for (a) and (d), respectively. A format similar to that of Figs. 4 is used. (c) The
thresholding M1 and T1 contours of (b) and their COG coordinates (red crosses). (f) The thresholding A2 and P2 contours of (e) and their COG
coordinates (red crosses)

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R. Hendradi et al.

Figs. 7. An example of S1 and S2 components at overlapping condition (in time) of the TR subject. (a) and (d) are example of well separated M1
and T1, A2 and P2, respectively. (b) and (e) are wavelet coefficients for (a) and (d), respectively. A format similar to that of Figs. 4 is used. (c) The
thresholding M1 and T1 contours of (b) and their COG coordinates (red crosses). (f) The thresholding A 2 and P2 contours of (e) and their COG
coordinates (red crosses)

IV.

subjects strongly depended on the measurement
condition and individual variation. Therefore, we were
set its cenv to have a proper thresholding for consistentoutcomes manually.
In our experimental results, the abnormal recordings
were very complicated and the patterns vary largely fromrecording to recording. Two of four times the recording
on the MR subject were neglected. The sudden release
for a short time during the recording of the stethoscopefrom the subject had resulted in incorrect detection.
Furthermore, the problems for picking up the peaks, i.e.,many extra peaks and artefact that as though real peaks
both in amplitude and time interval. Large intensity
murmur overlapped with S1 or S2 would make the proper
identification and segmentation improbable.
The heart sounds are non-stationary and complex
signals. Therefore, thresholding operation of wavelet
coefficients should be performed to obtain temporal andfrequency-related parameters that associated with theircharacterizations. The time-scale maps of thethresholding method gave more simply and easily seen.
Picking the thresholding value depended on ccon value.
Variation of ccon of the subject highly depended on themeasurement condition and individual variation such as
variation cenv in segmentation with respect to the cardiac
cycle. Therefore, we were set its ccon manually to have aproper thresholding value for consistent outcomes. For
example, as shown in Fig. 4(b), the contours of M1 and T1
components of the normal subject well separated.
However, the contour of the MR subject appears like
only having a single component (Fig. 6(b)).
Furthermore, by setting ccon manually, thethresholding contours of M1 and T1 components at

Discussions

In early part of the experiment of this study, allrecording data were inspected and explained in Methods
Section. The heart sound recordings from all subjects
apparently did not have different characteristics for
obtaining S1, S2, and their components in relation to the
auscultation locations. Furthermore, no statisticallysignificance different (significance level: 0.001) of all
subjects between inspiratory apnea group and expiratory
apnea group for tdur of S1 and S2, S1-S2 spaced time, tdur of
M1 and T1, M1-T1 split, tdur of A2 and P2, and A2-P2 split.
Also for srg of S1 and S2, and sCOG of component M1, T1,
A2, and P2, respectively. Additionally, the auscultationsite of pulmonic area in capturing A2 and P2 componentsof all subjects at both inspiratory apnea and expiratory
apnea conditions could not be obtained.
The split S2 may be absent because it is influenced by
the amount of tissue between the source of the sound andouter chest wall. Fat, muscle, and air tend to reducesound transmission [7]. Moreover, sensitivity of theacoustic transducer also affected the recordings. Hencethe placement of the acoustic transducer could not be
specified in one point. Thus, the same statistical analysis
to capture characteristics of the heart sounds was applied.
The segmentation with respect to the cardiac cycle
was an important step in analysing of the heart sounds.
Picking the thresholding (horizontal dotted line on Fig.
3(b)) would truncate heart sound signal at certain points
to obtain the onset until the ending events of S1 and S2,
respectively, also beginning of S1 of the next cycle. Its
thresholding depended on cenv value. Variation of cenv of
the normal and pathologic systolic murmur (MR and TR)

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R. Hendradi et al.

percentage of S1 towards the tdur average percentage ofsingle cycle for the three groups of subjects (Figs. 8).
The result of Table I for the normal subject was similar
to Luisada et al. [8]. Their experimental results that the
average tdur of S1 was 0.146 s (at the apex) and 0.140 s (atthe aortic area) of the subjects with the age above 10
years old, also the average tdur of S2 in the same
conditions was 0.097 s and 0.104 s, respectively.
Earlier studies [13], [15], [16] did not define clearly
how to determine the S1-S2 spaced time in their wavelet
transform algorithms for the normal subject. In our
experimental results, the spaced time (Table I) was
slightly different from Debbal and Bereksi-Reguig [13],0.312 sec., and Vikhe et al. [16], 0.3 sec. In ouralgorithm, the spaced time was difference between tCOG
of S1 and tCOG of S2 in relation to the thresholding contourof S1 and S2, respectively.
Moreover, the spaced time of the MR and TR subjects
were longer than the normal subject (Table I). These
were caused incompetence of M1 valve, at the left side of
the heart, and T1 valve, at the right side of the heart,respectively, to close perfectly and resulted in blood
backflow from left ventricle to left atrium and from rightventricle to right atrium [11]. Furthermore, for thenormal subject, Table I shows that S2 has higher
frequency content than S1. The result was similar to [13]and [16]. However, [13] and [16] did not explain
obviously how to acquire sl and sh of both S1 and S2
contour plots. Moreover, frequency content of S1 ishigher than S2` for MR and TR subjects (Table I).

overlapping conditions were obtained (Fig. 4(c) and Fig.
6(c)). Fig. 6(e) shows that the contour of S2 (the MR
subject) looks like just having a single component andoccurs other contour after the event of S2 contour. In thesame way, to identify the components of S2, thethresholding contour of A2 and P2 components atoverlapping condition were obtained (Fig. 6(f)). The TR
subject seemed only single dominant of S2 contour (Fig.
7(e)). However, after the magnitude thresholding
operation, the thresholding contours of the A2 and P2
components at overlapping condition were obtained (Fig.
7(f)). Moreover, although setting its thresholding
operation has been performed (e.g. Fig. 5(e)), thecontours of the S2 components often were not visible (e.g.
Fig. 5(f)). Also for identification of the contours of S1
components. The magnitude thresholding method may beless accurate when the A2 and P2 components or the M1
and T1 components have very short duration (i.e., when
they look like a single contour).
Additionally, if both the thresholding contours at
overlapping condition, e.g. Figs. 4(c) and 4(f),localization of the split time S1 and S2 components
became difficult. It is affected by the heart valve closureof the left side (M1 and A2) almost simultaneously withthe right side (T1 and P2). Therefore, tCOG of each thethresholding contour should be performed to specify the
split time of the S1 and S2 components. However, if thecontours of the S1 and S2 components, respectively,
appeared as a single contour component, determinationof the correct split time would be impossible.
We found in our experimental results that S2 hadshorter tdur than S1 for the three groups of subjects. Its tdurcaused by closing rapidly of semilunar valves (A2 and P2)
and vibrating of the fluid of blood that surrounds thevalves in a shorter interval [7], [24]. Additionally, the tdur
average percentage of S2 is shorter than the tdur average

IV.1. Measurement of the S1 Components
Splitting of S1 into its two audible components, M1
and T1, is a normal finding on cardiac auscultation [24].

Figs. 8. Temporal analysis of S1 and S2 components relative to the single cycle in percentage (%) of normal (a), MR (b)and TR (c) subjects, respectively.

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R. Hendradi et al.

The normal S1 split time heard when contraction of theright ventricle is delayed. Such a delay causes delayed T1
valve closure, thus causing a widening interval between
M1 and T1 [7]. Therefore, in this study, tdur of T1 is longer
than M1 for the normal subject as shown in Table I.
The same result also for the MR subject was obtained,
except for the TR subject. Furthermore, as shown in Figs.
8 the tdur average percentage of T1 is longer than the tdur
average percentage of M1 towards the tdur average
percentage of single cycle for normal and MR except TR
subject. The split time between M1 and T1 of the normal
subject is similar to data of Stein and Delman [12], i.e.
0.020 to 0.040 s. However, the result was different from
Debbal and Bereksi-Reguig [13], 0.005 s. Debbal andBereksi-Reguig [13] did not explain systematically andclearly of their split time measurement. The fact that the
S1 split time may be helpful in certain disease states [24].
In our study, the S1 split time for pathological subjects
was longer than the normal subject. This is due to theheart sounds of a split lengthwise (heavier) [11].
However, M1 and T1 are occasionally perceived as asingle sound, called S1. Most times, only S1 is heard
because M1 and T1 are separated by less than 0.020 s [7].
The absence of S1 split was shown of our experimental
data. Additionally, the M1 valve and myocardialsurrounding of its valve, at the left side of the heart, arelarger and thicker than the T1 valve and its supporting
cardiac muscles, at the right side of the heart.
The left side develops a higher pressure than the rightside. Thus, rate of myocardial contraction of the left side
is higher and turbulent velocity of blood flow more
rapidly to close M1 valve [7], [11]. These events cause
the frequency content of M1 is higher than T1 (e.g. Fig.
4(b)). The similar results are shown in Table I, for thethree groups of subjects.

[15], 0.006 s and Vikhe et al. [16], 0.009 s Because
earlier studies [13], [15], [16] did not explain
systematically of their split time measurement. The timedelay between A2 and P2 is highly crucial clinically to
specify the presence and degree of respiratory splitting
and the relative intensities of A2 and P2 [25].
Nevertheless, S2 normal split may as well be absent if theA2 sound masks the P2 sound or conversely [3]. It was
revealed of our experimental data (e.g. Fig. 5(e)). Thus,
the S2 frequently emerges as a single component [26].
Additionally, when the left ventricle relaxes, its pressure
falls more rapidly (below the pressure in the aorta) than
the right ventricle (below the pressure in the pulmonary
artery). These occurrences cause rate of a slightly
backflow of blood and recoiling of the elastic fibres in
the wall of the aorta are higher to close the A2 valve. The
vibrations associated with these events produce the S2
sound, which the A2 closure sound is heard louder than
the P2 closure sound [7], [11], [27]. Thus, the frequency
content of A2 is higher than P2 (e.g. Fig. 4(e)). The
similar results are shown in Table I for the three groupsof subjects.
Earlier studies [13], [15], [16] did not clearly explain
how to measure the A2-P2 split using wavelet coefficients.
Moreover, their experimental results concerning two
contours of A2 and P2, only presented at adjacentcondition in time. However, in our experimental results,
both contours could also be appeared as a single
component (such as Fig. 5(d)) and overlapping (such as
Fig. 4(d)) conditions. The split time was calculated basedon the difference between tCOG of A2 and tCOG of P2 fromthe thresholded contours of A2 and P2, respectively.
The similar results were shown in our previous
research, about cardiac valvular hemodynamic point ofview [28]. Most of the split time results were similar todata of Felner [25], 0.02 to 0.08 s. A simultaneous
decrease in blood flow toward the left side of the heart
results in a shorter left ventricular ejection time [7].
Thus, closing pressure of A2 valve slightly ahead of P2
valve, contributes to the S2 split time.

IV.2. Measurement of the S2 Components
In order to be differentiated and heard as two distinct
sounds, the A2 closure sound and the P2 closure soundmust be separated by more than 0.020 s [6]. A presenceof S2 split time is caused by a small time delay between
the aortic component and the pulmonic component.
Closing of A2 and P2 valves coincides with thetermination of left ventricular ejection and rightventricular ejection, respectively. Normally, the closing
P2 valve occurs after the closing A2 valve.
Since right ventricular ejection terminates after left
ventricular ejection [25]. Thus, in our experimental
results, tdur of P2 is longer than A2 of the normal and TR(Table I), except for the MR subject. This was cause ofsystolic murmur of the pathological subject (MR).
Additionally, as shown in Figs. 8 the tdur average
percentage of P2 is longer than the tdur average
percentage of A2 towards the tdur average percentage ofsingle cycle for the normal and TR except the MR
subject. The A2-P2 split for two conditions of the normal
subject (Table I) was similar to [25]. However, the result
was different from Debbal and Bereksi-Reguig [13],IV.3. Impact of the Heart Sound Characterization for
Modelling and Study in Medicine
The heart sound is indicated by fast changes andtransients in frequency as the time associated withintracardiac valvular events. The closure of M1 and T1
valves (S1 components) also A2 and P2 valves (S2components) show dominant frequency phenomenon in
the central stage of the three stages of the generation of
S1 and S2, respectively [8], [9]. In our experimental
results, as shown in Table II, from the contour of two
dimensional representations for the three groups ofsubjects, the average time position as well as thepercentage of occurrence of the early valve closure, themagnitude achieves a peak, and the ending valve closureof each S1 and S2 components relative to the cardiac cycle
associated with each their frequency content gave more
simply and easily seen.

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R. Hendradi et al.

TABLE IIPOSITION OF S1 AND S2 COMPONENTS AND EACH THEIR FREQUENCY CONTENT RELATIVE TO THE SINGLE CYCLE OF THE NORMAL,
THE MR, AND THE TR SUBJECTS, RESPECTIVELY
S1 components
Closuring M1 valve (left side of the heart)
Closuring T1 valve (right side of the heart)
Magnitude of contour
early
peak
end
early
peak
end
average time position (s)0.013-0.018
0.030-0.038
0.050-0.063
0.035-0.043
0.068-0.072
0.099-0.100
Normalposition of single cycle (%)
1.7-2.4
3.8-5.1
6.4-8.4
4.5-5.7
8.7-9.6
12.8-13.2
average freq. content (in scale)
0.017-0.020
0.013-0.024
0.017-0.020
0.024-0.025
0.018-0.031
0.024-0.025
average time position (s)
0.010
0.031
0.056
0.068
0.128
0.183
MR
position of single cycle (%)
1.2
3.6
6.5
7.9
14.9
21.3
average freq. content (in scale)
0.034
0.028-0.042
0.034
0.055
0.044-0.066
0.055
average time position (s)
0.011
0.068
0.145
0.050
0.105
0.168
TR
position of single cycle (%)
1.3
8.7
18.5
6.4
13.4
21.5
average freq. content (in scale)
0.034
0.023-0.043
0.034
0.060
0.053-0.067
0.060
S2 components
Closuring A2 valve (left side of the heart)
Closuring P2 valve (right side of the heart)
Magnitude of contour
early
peak
end
early
peak
end
average time position (s)0.282-0.288
0.293-0.296
0.301-0.317
0.292-0.312
0.330-0.345
0.364-0.380
Normalposition of single cycle (%)
36.8-37.7
37.5-39.5
38.5-42.3
39.0-39.9
44.1
47.2-48.6
average freq. content (in scale)
0.016-0.017
0.012-0.020
0.016-0.017
0.026-0.029
0.020-0.033
0.026-0.029
average time position (s)
0.317
0.350
0.391
0.379
0.398
0.418
MR
position of single cycle (%)
36.5
40.2
45.0
43.7
45.8
48.1
average freq. content (in scale)
0.034
0.023-0.042
0.034
0.037
0.032-0.040
0.037
average time position (s)0.314-0.327
0.340-0.344
0.368-0.376
0.320-0.334
0.339-0.375
0.361-0.419
TRposition of single cycle (%)
40.1-43.9
43.4-46.2
48.0-49.4
41.0-44.8
43.3-50.3
46.1-56.3
average freq. content (in scale)
0.023-0.033
0.017-0.041
0.023-0.033
0.043-0.067
0.035-0.074
0.043-0.067

For the early valve closure of each S1 and S2components, the contour has narrow frequency content
with the ascending magnitude. Furthermore, themagnitude achieves a peak with the fairly wide
frequency content. Moreover, the magnitude gradually
falls and most of the frequency contents slowly narrowedat the ending valve closure. Most of the early closure of
the T1 valve is simultaneous with the M1 magnitude peak
event for the normal subject. However, for the MR andTR, time delay between early closure of the T1 valve andthe M1 magnitude peak was occurred. Additionally, mostof the early closure of the P2 valve is simultaneous with
the A2 magnitude peak occurrence for the normal subject.
However, time delay between early closure of the P2
valve and the A2 magnitude peak of the MR and TR wasensued.
In previous researches, the basic models of theanalysis and synthesis of S1 and S2 have been performed.
Chen et al. [29] have been developed the exponentiallydamped sinusoid model of S1. However, their research
only involved the mitral valve in valvular component.
Additionally, the matching pursuit method presented asum of Gaussian sinusoids located with high resolution
in the time-frequency plane [6]. However, the study did
not yet address each component of S1 and S2,
respectively.
Furthermore, Xu et al. [26], [30] have been modelled
A2 and P2 components through narrow-band nonlinear
transient mono-component chirp signals of short duration
with energy distribution concentrated along theirinstantaneous frequency. However, they proposed
method had limitation when the splitting interval in very
short duration (< 20 ms) and appear as mono-component
signal in the time-frequency map.
In our study, the splitting interval could be easily
obtained by using the COG of each the component
contour. Meanwhile, in our proposed method, thecharacterization of duration percentage of S1 and S2components relative to the single cycle (Figs. 8) could bebetter described variations of the closing duration of theheart valves. Additionally, the percentage of the averagetime position of the early valve closure, the magnitude
reaches a peak, and the ending valve closure of each S1and S2 components relative to the cardiac cycle
associated with each their frequency content of a contourin the time-scale domain, more ease understanding
visually (Table II).
Thus, the comprehensive model of S1 and S2 that more
represented the components of the heart sound can beeasily obtained. Therefore, the characterization wasconsidered very helpful in development of the heartsound modelling.
In the study of auscultation technique, Horiszny [31]has developed a program to teach cardiac auscultation
skills for 18 family practice residents through a
Cardionics CardioSim Digital Heart Sound Simulator®
(Cardionics, Inc., Houston). He was only limited inteaching auditory and carried on a simulator and notpatients. Meanwhile, in our study, the physiologic
splitting of S2 could be explained more easily realized
both visually (e.g. Figs. 4) and in the form of temporalscale parameters.
Thus, our proposed method was expected valuable toassist in the study of auscultation technique from thesignal processing method point of view. Application of
our proposed method was adequate to characterizeobjectively, clearly, systematically, and comprehensively
in capturing temporal and frequency-related parametersof the heart sound signals.
The duration of the diastole is longer than the systolein each cardiac cycle. During systole, the duration of T1
valve closure is longer and occurs after M1 valve closure(except for TR subject). In diastole, the duration of P2
valve closure is longer and ensues after A2 valve closure(except for MR subject). The mechanical activities of theleft side of the heart (M1 and A2) need more force to close

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65

R. Hendradi et al.

their valves, while the frequency content of M1 and A2
are higher than mechanical activities of the right side (T1and P2).
Our method could overcome difficulties of traditional
auscultation and could distinguish between normal andpathologic systolic murmur (MR and TR) subjects.
Interpretation of our experimental study could not be
generalized to all cases of normal, MR, and TR subject,
respectively.
It was caused limitation of experimental data,measurement condition, and individual variation.
Additionally, the method did not work, if the contours of
the S1 and S2 components, respectively, appeared as asingle component. However, we believed that thecharacterization provided very valuable and important
information for clinical diagnosing and for understanding
about mechanisms of cardiac valves functions.
Moreover, the characterization contributes ondeveloping of heart sound modelling and study about
auscultation technique of medical students foreducational purpose. For further research, classificationof both normal and pathological heart sounds based on
their essential characteristics is important for cardiac
diagnosis.

V.

Acknowledgements
This study was supported by Directorate General ofHigher Education, Ministry of National Education ofIndonesia under Graduate Education Scholarship andSandwich Program. The authors would like to thank
Somali, MD for his assistance and permission to collectdata at Kebonjati Hospital, Bandung.

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[12] E. Stein, A.J. Delman, Rapid Interpretation of Heart Sounds andMurmurs (2nd ed., EGC, Jakarta, Indonesian Version, 1994).
[13] S.M. Debbal, F. Bereksi-Reguig, Wavelet Transform Analysis of
the Normal Cardiac Sounds, Biomedical Soft Computing andHuman Sciences, vol. 12 n. 1, 2007, pp. 53 - 58.
[14] J.J. Lee, S.M. Lee, I.Y. Kim, H.K. Min, S.H. Hong, Comparison
between short time Fourier and wavelet transform for feature
extraction of heart sound, Proceedings of the IEEE Region 10
Conference, TENCON 99, vol. 2, December 1999, pp. 1547 1550.
[15] S.M. Debbal, F. Bereksi-Reguig, Automatic Measure of the Splitin the Second Cardiac Sound by Using the Wavelet Transform
Technique, Computers in Biology and Medicine, vol. 37, 2007,
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[16] P.S. Vikhe, S.T. Hamde, N.S. Nehe, Wavelet transform basedabnormality analysis of heart sound, Proceedings ofInternational Conference on Advances in Computing, Control,and Telecommunication Technologies, December 28-29, 2009,

Conclusion

Characterization of the heart sounds using signal
processing and extensive characterization based on
wavelet analysis has shown ability to overcome
difficulties of traditional auscultation. Furthermore, themethod was more capable in capturing temporal andfrequency-related parameters while compared with theprevious research, such as FFT and STFT methods.
The characteristics of each pattern of the heart sounds
can be extracted more precisely by using a flexible
window modulation.
The magnitude thresholding operation and COG of the
wavelet contour were performed adequately to restrict
temporal and frequency-related evidence of valvular
activities. Hence, our proposed method is very useful for
diagnosis and for gaining our understanding aboutmechanisms of cardiac valves functions. Moreover, theimportant characteristics in the form temporal andfrequency-related parameters would be impact on
development of heart sound modelling and study of
auscultation technique for medical students. We believedthat all these important characteristics could be used for
detection of healthy subject as well as certain diseasestates.
Thus, the application of our method was consideredvaluable to explain mechanisms of cardiac valves
functions. We expected that the method would be helpful
for clinical diagnosis as well as very useful fordeveloping of heart sound modelling and for educational
purpose of medical students in auscultation technique.
Next topic of our research was addressed forclassification of heart sounds based on their essential
characteristics.

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reservedInternational Review of Electrical Engineering, Vol. 11, N. 1

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R. Hendradi et al.

pp. 367 - 371.
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[18] L.T. Hall, J.L. Maple, J. Agzarian, D. Abbott, Sensor System forHeart Sound Biomonitor, Microelectronics Journal, vol. 31,
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Algorithm Based on Heart Sound Envelogram, Computers in
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[20] M.S. Obaidat, Phonocardiogram Signal Analysis: Techniques andPerformance Comparison, Journal of Medical Engineering &Technology, vol. 17 n. 6, November/December 1993, pp. 221 227.
[21] R. Polikar, The Wavelet Tutorial Part III Multiresolution Analysis
and the Continuous Wavelet Transform (2nd ed., June 5, 1996).
[22] G. Strang, T. Nguyen, Wavelets and Filter Banks (WellesleyCambridge Press, 1997).
[23] B. Ergen, Y. Tatar, H.O. Gulcur, Time-Frequency Analysis of
Phonocardiogram Signals Using Wavelet Transform: A
Comparative Study, Computer Methods in Biomechanics andBiomedical Engineering, vol. 15 n. 4, 2012, pp. 371 - 381.
[24] J.M. Felner, The first heart sound, In H.K. Walker, W.D. Hall,
J.W. Hurst (Eds.), Clinical methods: the history, physical, andlaboratory examinations (3rd ed., Butterworth Publishers,
Boston, 1990, 117-121).
[25] J.M. Felner, The second heart sound, In H.K. Walker, W.D. Hall,
J.W. Hurst (Eds.), Clinical methods: the history, physical, andlaboratory examinations (3rd ed., Butterworth Publishers,
Boston, 1990, 122-125).
[26] J. Xu, L.G. Durand, P. Pibarot, Extraction of The Aortic andPulmonary Components of the Second Heart Sound Using A
Nonlinear Transient Chirp Signal Model, IEEE Transactions on
Biomedical Engineering, vol. 48 n. 3, March 2001, pp. 277 - 283.
[27] A.G. Tilkian, M.B. Conover, Understanding Heart Sounds andMurmurs (Binarupa Aksara, Tangerang, Indonesian Version,2008).
[28] R. Hendradi, A. Arifin, M.H. Purnomo, S. Gunawan, Explorationof cardiac valvular hemodynamics by heart sound analysis of
hypertensive cardiac disease background patients, IEEE
International Conference on Computational Intelligence andCybernetics (CyberneticsCom), July 12-14, 2012, pp. 153-157.
[29] D. Chen, L.G. Durand, H.C. Lee, Time-Frequency Analysis of theFirst Heart Sound. Part 1: Simulation and Analysis, Medical &Biological Engineering & Computing, vol. 35, July 1997, pp. 306- 310.
[30] J. Xu, L.G. Durand, P. Pibarot, Nonlinear Transient Chirp Signal
Modeling of the Aortic and Pulmonary Components of The
Second Heart Sound, IEEE Transactions on Biomedical
Engineering, vol. 47 n. 7, July 2000, pp. 1328 - 1335.
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R. Hendradi received his B.Sc. and M.Sc.
degrees in Mathematics Department, Universityof Gadjah Mada (UGM), Yogyakarta, Indonesia,
in 1995 and 2002, respectively. He has beenworking as an Educational Staff in Mathematics
Department, Universitas Airlangga (UNAIR),
Surabaya, Indonesia. He is currently pursuing
Ph.D. degree at Electrical Engineering
Department, Institut Teknologi Sepuluh Nopember (ITS), Surabaya,
Indonesia. His present research interests include the biomedical signal
processing and particular in time-frequency phonocardiogram analysis.
He is a student member of IEEE--Engineering in Medicine and Biology
Society (EMBS).
A. Arifin received his B.E. degree in Electronic
Engineering from Institut Teknologi Sepuluh
Nopember (ITS), Indonesia, in 1996. Since thenhehasjoinedElectricalEngineering
Department, ITS, as an Educational Staff. He
received M.E. and Ph.D. degrees in Electronic
Engineering from Tohoku University in 2002
and 2005, respectively, with major in
Biomedical Electronic Engineering. Since 2015 he serves as Head of
Biomedical Engineering Department, ITS. His research activities are infields of human movement measurement and analysis, human
movement restoration by functional electrical stimulation (FES),
biomedical signal and system modelling and analysis, and fuzzy control
system. He is a member of IEEE-Engineering in Medicine and Biology
Society (EMBS).
H. Shida was born in Sendai, Japan, on
February 19, 1987. He received a B.E. degreefrom Tohoku University, Sendai, Japan in 2008
in Electrical Engineering, and M.E. degrees,
also from Tohoku University, in 2012 in
Biomedical Engineering. He has been working
as a Broadcast Engineer in Broadcast
Engineering Department, Japan Broadcasting
Corporation (NHK), Tokyo, Japan. His present interests are broadcast
engineering to prepare for emergency disaster and development of
ultra-high sensitivity camera using HARP image pickup tubes andapplication examples.
S. Gunawan received his M.D. from Maranatha
Christian University, Bandung, Indonesia in
1984. From 1992 to 1994 he was trained as a
Clinical Fellow in Cardiology at National Heart
Centre, Singapore. From 1985 to 2012 he has
been working in Internal Medicine Department
at Immanuel Hospital Bandung. He is also a
lecturer in the Department of Medicine, Facultyof Medicine, Maranatha Christian University. He is currently aphysician at Kebonjati Hospital Bandung.
M. H. Purnomo received his B.E. degree from
Institut Teknologi Sepuluh Nopember (ITS),
Surabaya, Indonesia in 1985. He received his
M.Eng. and Ph.D. degrees from Osaka City
University, Osaka, Japan in 1995 and in 1997,
respectively. He has joined ITS Surabaya in1985 and has been a Professor since 2003. His
current interests include intelligent system incontrol biomedical signal and image processing. He is a member ofIEEE and International Neural Network Society.

Authors’ information
1

Graduate Program of Electrical Engineering, Department of ElectricalEngineering, Institut Teknologi Sepuluh Nopember Surabaya 60111,
Indonesia.
2

Graduate Schools of Engineering and Biomedical Engineering,Tohoku University, Sendai 980-8579, Japan.
3

H. Hasegawa was born in Oyama, Japan, in
1973. He received the B.E. degree from Tohoku
University, Sendai, Japan in 1996. He received
the Ph.D. degree from Tohoku University in
2001. He is presently a Professor at theGraduate School of Science and Engineering for
Research, University of Toyama. His main
research interest is medical ultrasound,especially diagnosis of cardiovascular system by measurement of its

Kebonjati Hospital Bandung 40181, Indonesia.

4

Graduate School of Science and Engineering for Research, Universityof Toyama 3190 Gofuku, Toyama 930-8555, Japan.
5

Biomedical Engineering Department, Institut Teknologi Sepuluh
Nopember Surabaya 60111, Indonesia.

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R. Hendradi et al.

dynamic properties. Dr. Hasegawa is a member of the IEEE, theAcoustical Society of Japan, the Japan Society of Ultrasonics in
Medicine, and the Institute of Electronics, Information andCommunication Engineers.
H. Kanai was born in Matsumoto, Japan, on
November 29, 1958. He received a B.E. degreefrom Tohoku University, Sendai, Japan in 1981,
and M.E. and the Ph. D. degrees, also fromTohoku University, in 1983 and in 1986, both inElectrical Engineering. From 1986 to 1988 he
was with the Education Centre for Information
Processing, Tohoku University, as a research
associate. From 1990 to 1992 he was a lecturer in the Department ofElectrical Engineering, Faculty of Engineering, Tohoku University.
From 1992 to 2001 he was an associate professor in the Department ofElectrical Engineering, Faculty of Engineering, Tohoku University.
Since 2001 he has been a professor in the Department of Electronic
Engineering, Graduate School of Engineering, Tohoku University.
Since 2008 he has been also a professor in the Department of
Biomedical Engineering, Graduate School of Biomedical Engineering,Tohoku University. From 2012 to 2015 he was a dean of Graduate
School of Engineering, Tohoku University. From 2015 he has been avice president (for Research Consolidation and University Reform),
Tohoku University. His present interests are in transcutaneous
measurement of the heart wall vibrations and myocardial response topropagation of electrical potential and cross-sectional imaging ofelasticity around atherosclerotic plaque with transcutaneous ultrasoundfor tissue characterization of the arterial wall. Dr. Kanai is a member ofthe Acoustical Society of Japan, a fellow of the Institute of Electronics
Information and Communication Engineering of Japan, a member of
the Japan Society of Ultrasonics in Medicine, Japan Society of Medical
Electronics and Biological Engineering, and the Japanese Circulation
Society. Since 1998, he has been a member of Technical Program
Committee of the IEEE Ultrasonic Symposium. Since 2008, he has
been an International Advisory Board of International Acoustical
Imaging Symposium. Since 2011, he has been a Board Member ofInternational Congress on Ultrasonics. Since 2012, he has been an
editor of Journal of Medical Ultrasonics and Japanese Journal of
Medical Ultrasonics. Since 2013, he has been an associate editor of theIEEE Transaction on UFFC.

Copyright © 2016 Praise Worthy Prize S.r.l. - All rights reservedInternational Review of Electrical Engineering, Vol. 11, N. 1

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