STUDY OF TWO FEATURE EXTRACTION METHODS TO

DISTINGUISH BETWEEN THE FIRST AND THE SECOND

HEART SOUNDS

Ali Moukadem

, Alain Dieterlen

and Christian Brandt

MIPS Laboratory, University of Haute Alsace, 68093, Mulhouse Cedex, France

University Hospital of Strasbourg, CIC, Inserm, BP 426, 67091, Strasbourg Cedex, France

Keywords: Heart sounds, Singular value decomposition, Time-frequency analysis, Feature extraction, Empirical mode

decomposition, s-Transform.

Abstract: Most of the existing methods for the segmentation of heart sounds use the feature of systole and diastole

duration to classify the first heart sound (S1) and the second heart sound (S2). These time intervals can

become problematic and useless in several clinical real life settings which are particularly represented by

severe tachycardia or in tachyarrhythmia. Consequently with the objective of development of a robust

generic module for heart sound segmentation we propose to study two methods of extraction based on

Singular Value Decomposition (SVD) technique to distinguish S1 from S2. A K-Neirest Neighbor (KNN)

classifier is used to estimate the performance of each feature extraction method. The study uses a database

with 80 subjects, including 40 cardiac pathologic sounds which contain different systolic murmurs and

tachycardia cases. The first and the second proposed method reached 96 % and 95% correct classification

rates, respectively.

1 INTRODUCTION

One of the first and most important phases in the

analysis of heart sounds, is the segmentation of heart

sounds. Heart sound segmentation partitions the

PCG signals into cardiac cycles and further into S1,

systole, S2 and diastole. In the classic approach

(Ahlstrom, 2008), the segmentation algorithms can

be divided into 3 parts; the first one is the

localization method which consists of finding S1 and

S2 without distinguishing the two from each other,

the second part consists of estimating the boundaries

of located sounds and the third part aims at

distinguishing between S1 and S2 which is the main

purpose of this paper.

Most of the existing methods in the literature use

the systole and diastole duration (systole regularity)

as a criterion to discriminate between S1 and S2

(Liang et al., 1997), (Dokur et al., 2007) and (Yan et

al., 2009), to name a few. These methods do not

perform well for all types of heart sounds, especially

in the presence of high heart rate or in the presence

of arrhythmic pathologies (figure 1). To deal with

this problem, an unsupervised method for the

discrimination of S1 and S2 using the high

frequency information obtained from the Shannon

energy of the detail coefficients of wavelet analysis

was proposed (Kumar et al., 2011) which uses the

fact that S2 in general contain higher frequency than

S1. However this criterion cannot be generalized on

all real life cases because some medical even normal

conditions are characterize by S2 frequency content

lower than S1 frequency content.

With the objective of development a generic

auto-analysis module, and without any previous

information about the subject, we present in this

paper a supervised approach to classify S1 and S2.

Two feature extraction methods are presented.

Both of them are based on the Singular Value

Decomposition (SVD) technique. The first method

applies the SVD technique on the S-matrix

calculated by the S-Transform time-frequency

method. The second one proposes the use of the

Empirical Mode Decomposition (EMD) technique

and the Shannon energy of the intrinsic mode

functions (IMF) before applying the SVD technique.

The K-Neirest Neighbor classifier is used to estimate

the performance of each feature extraction method.

346

Moukadem A., Dieterlen A. and Brandt C..

STUDY OF TWO FEATURE EXTRACTION METHODS TO DISTINGUISH BETWEEN THE FIRST AND THE SECOND HEART SOUNDS.

DOI: 10.5220/0003743703460350

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2012), pages 346-350

ISBN: 978-989-8425-89-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Example of an arrhythmic subject.

This paper is organized as following: sections 2.1

and 2.2 describe the dataset used in this study and

the acquisition and pre-processing of PCG signals,

respectively. In section 2.3 the localization and the

boundaries detection algorithms are presented

briefly, section 2.4 describes the feature extraction

methods. The results and discussion are presented in

section 3, while section 4. consists in conclusions.

2 MATERIAL AND METHODS

2.1 Data Set

Different cardiologists equipped with a prototype

electronic stethoscope with a Bluetooth standard

communication module, have contributed to a

campaign of heart auscultation in the Hospital of

Strasbourg. In parallel, 2 prototypes have been

dedicated to the MARS500 project, promoted by

ESA, in order to collect signals all two months from

6 volunteers (astronauts) in a confinement

experience lasting 520 days and transmitted to IBMP

station in Moscow as a real telemedicine

investigation. The use of prototype electronic

stethoscopes by different cardiologists makes the

database rich in terms of qualitative diversity for the

collected sounds and create the condition for a real

life database.((.n more realistic.))

The dataset contains 80 subjects, including 40

cardiac pathologic auscultation sounds which

contain different systolic murmurs and tachycardia

and or arrhythmia cases. Each recorded auscultation

corresponds to one patient. The length of each

recording lasts between 8 and 12 seconds which

represents generally the time of tolerated apnoea.

2.2 Acquisition and Pre-processing of

PCG Signals

The sounds are recorded with 16 bits accuracy and

8000Hz sampling frequency in a wave format, using

the software “Stetho” developed under Alcatel-

Lucent license. The original signal is decimated by

factor 4 to 2000 Hz sampling frequency and then the

signal is filtered by a high-pass filter with cut-off

frequency of 30 Hz to eliminate the noise collected

by the prototype stethoscope. The filtered signal is

pre-filtered reverse direction so that there is no time

delay in the resulting signal. Then, the

Normalization is applied by setting the variance of

the signal to a value of 1.

2.3 Localization and Boundaries

Detection of Heart Sounds

In this study, the localization of heart sounds is

established by using the SRBF method based on S-

transform and radial basis functions (RBF) neural

network (Moukadem et al., 2011). The boundaries of

the heart sounds are determined by the first local

minima before and after the located sound.

The results were visually inspected by a

cardiologist and erroneously extracted heart sounds

were excluded from the study.

2.4 Classification of S1 and S2

The initial component of S1 is related mitral and

tricuspid valve closure, due to contraction of

ventricles, thus identifying the onset of ventricular

systole and the end of mechanical diastole. The S2 is

produced by the aortic and pulmonic valves closing

when left ventricular pressure decreases under

diastolic aortic pressure. The vibrations of S2 occur

at the end of ventricular contraction and identify the

onset of ventricular diastole and the end of

mechanical systole (Felner, 1990). These

physiological differences lead to different time and

frequency content behaviour between S1 and S2.

Two feature extraction methods for

distinguishing between S1 and S2 are examined.

The feature extraction process extracts a feature

vector per extracted sound H

and each of these

vectors is averaged across available extracted sounds

from each subject. So from each subject in the

database, we obtain one S1 feature vector and one

S2 feature vector to use in the training and

classification process.

2.4.1 Feature Extraction using the

s-Transform

The SVD technique is a powerful tool to represent

the time-frequency matrix in a compact manner.

Hassanpour et al. proposed a feature extraction

method based on SVD technique to classify EEG

seizures (Hassanpour et al., 2004). The advantage of

this approach, that it incorporates information from

the eigenvectors, which contains relevant

STUDY OF TWO FEATURE EXTRACTION METHODS TO DISTINGUISH BETWEEN THE FIRST AND THE

SECOND HEART SOUNDS

347

information about signal. Following this approach,

this study proposes a feature extraction method for

S1 and S2 classification.

The time-frequency analysis is performed by the

S-Transform (Stockwell et al., 1996). The S-matrix

of the extracted heart sound H

is decomposed by

the SVD technique as follows:

UDVS 

(1)

Where U(M×N) and V(M×N) are orthonormal

matrices so their squared elements can be considered

as density function (Hassanpour et al., 2004) and

D(M×N) is a diagonal matrix of singular values. The

columns of the orthonormal matrices U and V

contains in this case the time and frequency domain

information, respectively. The eigenvectors related

to the largest singular values contain more

information about the structure of the signal. The

first left eigenvector and the first right eigenvector

that correspond to the largest singular values are

used for the feature extraction process. The

histogram (10 bins) for each related distribution

function is calculated based on the density function.

Five feature vectors obtained by this method are

tested in the classification process; the eigentime

histogram vector U

(T-Features), the

eigenfrequency histogram vector V

(F-Features), the

singular values vector D

(SV Features) and the

time-frequency vector U

(TF Features). All

vectors have a length of 10 features except the time-

frequency vector that has a length of 20.

2.4.2 Feature Extraction using the EMD

In this study, a new feature extraction method based

on EMD technique and Shannon energy is proposed

for S1 and S2 classification. The EMD method

decomposes a time series signal into IMF

modulating both in amplitude and frequency (Huang

et al., 98).

The initial signal H

(t) can be represented as

follows:

rtIMFtH 



1

)()(

(2)

Where r(t) is the residual signal. The feature

extraction method consists to calculate the Shannon

Energy of each IMF vector, as follow:







iii

kIMFkIMFSE

))(log().(

(3)

Where

4,...,1i and N is the number of samples of

IMF

the Shannon energy is smoothed by using a

median filter, and the feature vector is obtained by

applying the same SVD approach used in section

2.5.1 at each calculated IMF (Figure 2). For each

extracted heart sound the first four IMF is

calculated. The others IMF don’t contain relevant

information about S1 and S2. Five feature vectors

obtained by this method are tested in the

classification process; FV1 (that correspond to IMF1

signal), FV2, FV3, FV4 and FV (that correspond to

the average of calculated FVs). The length of each

vector is 10.

Figure 2: Feature vector (FV) of Heart Sounds (Hi)

extracted using EMD and Shannon Energy (SE) before

applying the SVD technique.

3 RESULTS AND DISCUSSION

A 3-Neirest Neighbor (KNN) classifier is used to

evaluate the performance of the nine feature vectors

obtained by the two methods and the 5-fold

approach is used for cross validation. The choice of

KNN classifier was based on its simplicity of and its

robustness to a noisy training data. Other classifiers

can be tested and compared but it is not the main

goal of this study.

Figure 3: Average of TF Feature vectors for S1 (solid line)

and S2 (dashed line) obtained by the S-transform based

method.

The eigenfrequency feature values (first ten

values, figure 3) of S2 are slightly higher than S1 in

all of the cases except the 2 last. However, S1

eigentime feature values (last ten values, figure 3)

are significantly higher than S2 eigentime feature

values. This explains why we obtain a higher

classification rate with eigentime feature compared

to eigenfrequency feature when they are tested

separately (Table1). The singular values are almost

BIOSIGNALS 2012 - International Conference on Bio-inspired Systems and Signal Processing

348

Table 1: Sensitivity and specificity for the nine extracted feature vectors evaluated by a KNN classifier.

KNN T-

Features

F-

Features

FV1 FV2 FV3 FV4 FV

Sensitivity 92% 81% 60% 95% 88% 81% 82% 65% 94%

Specificity 92% 88% 65% 97% 91% 97% 94% 95% 97%

indistinguishable from each other and it is shown by

the low classification rate for the SV features

(Table1). In most cases seen in the medical field, S2

has a higher frequency than S1. This is due to the

fact that S2 is the heart sound associated with the

closure of the aortic valve in a context of high left

ventricular pressure, the mitral closing occurring at

low left ventricular pressure (S1). However, as we

mentioned before, this criterion cannot be

generalized on all real life cases because some

medical conditions are characterized by S2

frequency content lower than S1 frequency content.

Hence, the importance of time-frequency based

features approach, especially in a generic module.

Figure 4: Average of feature vectors (FV) for S1 (solid

line) and S2 (dashed line) obtained by the EMD based

method.

For the EMD based method, the feature vector of

S1 is always higher than the feature vector of S2

(figure 4). This can be explained by the fact that

EMD technique performs a multi resolution analysis

which reflects the richness of the signal at different

frequency bands. Moreover, it is known from a

physiological point of view, that S1 in general is

more complicated than S2, so it is not surprising that

the average of the first four IMF gives higher values

for S1. We note here that FV1, the feature vector

that correspond to the IMF1 gives the best results, in

term of sensitivity, compared to other IMFs when

they are tested separately (Table 1).

4 CONCLUSIONS

Two feature extraction methods based on the SVD

technique are presented in this study for the

classification of S1 and S2. Before applying the

SVD technique, the first method calculates the time-

frequency matrix of segmented heart sound by

applying the S-transform and the second method

calculates the Shannon energy of the first four IMF

obtained by the EMD algorithm. Each feature vector

extracted by these methods is evaluated by applying

a KNN classifier. These methods are tested on a

dataset that contains 80 subjects, including 40

cardiac pathologies sounds which contain different

systolic murmurs and tachycardia cases.

The objective of this paper is to find suitable

features for classification of S1 and S2 without using

the systole regularity criterion. The results obtained

by the proposed approaches are very promising; the

TF Feature vector obtained by the S-transform based

method reaches 96 % correct classification rate, and

the FV feature vector obtained by the EMD based

method reaches 95% correct classification rate. Both

methods are suitable for the main purpose of this

study. More robustness tests against noisy signals,

algorithms complexity, facility of implementation

and more signals, would contribute to choosing the

adequate method in the aim of developing a generic

tool for the automatic heart sounds analysis.

ACKNOWLEDGEMENTS

The authors would like to thank Mr. SIMON Alban

from the University Hospital of Strasbourg, for his

contributions to this study.

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