Journal of Signal and Information Processing, 2011, 2, 232-237
doi:10.4236/jsip.2011.23032 Published Online August 2011 (http://www.SciRP.org/journal/jsip)
Copyright © 2011 SciRes. JSIP
A Time-Frequency Approach for Discrimination of
Heart Murmurs
Sepideh Jabbari, Hassan Ghassemian
School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Email: ghassemi@modares.ac.ir
Received May 29th, 2011; revised July 18th, 2011; accepted July 26th, 2011.
ABSTRACT
In this paper, a novel fram ework based on a time-frequ ency (TF) approach is proposed for detection of murmurs from
heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose each heart beat
into a series of TF atoms selected from a redundant dictionary. Next, representative components of murmurs were iden-
tified by clustering the selected a toms of all th e beats into a finite numb er of clusters. Then, Wign er-Ville distrib ution of
the representative components was used to generate a set of 8 features which were fed to a classifier. Experiments with
a dataset consisting of heart sounds from 35 normal and 35 pathological subjects showed a classification accuracy of
95.71% in dist i ng ui shing murmur s fr o m n or mal heart sounds.
Keywords: Phonocardiogram (PCG), Murmur, Matching Pursuit (MP), Time-Frequency Atom, Clustering
1. Introduction
Mechanical activity of the heart is evaluated by ausculta-
tion and analysis of phonocardiogram (PCG) signal,
which is a recording of heart sounds. PCG provides val-
uable information on the structural integrity and function
of heart valves. Heart sounds normally consist of two
regularly repeated thuds, known as S1 and S2 for every
heat beat. An underlying pathology such as diseased
valves produces some additional and abnormal sounds
which are called murmurs. The automatic detection of
murmurs has been widely considered for several decades
because of the human auditory limitations in distin-
guishing them from normal heart sounds [1].
PCG is highly nonstationary signal with multicompo-
nent nature and identification of its components can be
performed by a nonstationary signal analysis tool such as
time-frequency (TF) analysis methods [2]. Short-time
Fourier transform (STFT) is one of the existing tech-
niques that tackles the nonstationarity of the PCG signal
by segmenting it into stationary parts [3,4]. Wavelet
transform (WT) was used to obtain TF decomposition of
PCG signals in previous studies [5,6]. It does not require
the fixed data window needed for STFT; however, even
wavelet bases are not well suited to exact TF representa-
tion of PCG components whose localizations in time and
frequency vary widely. Some researchers performed
analysis of PCG by using Wigner-Ville distribution (WV-
D) [7]. A major drawback of the WVD is presence of
cross-term artifacts caused by aliasing when analyzing
multicomponent signals such as PCG. A true nonstation-
ary TF technique would be one that can give an accurate
parametric display of PCG characteristics with satisfac-
tory TF resolution, cross-term suppression, and without
segmentation requirements. The parameters of decom-
posed components could be later used to extract crucial
features for identification of murmurs. In this paper, we
achieve this goal using a cross-term free high-resolution
method, matching pursuit (MP) algorithm. MP is a signal
decomposition method whereby a signal is decomposed
into a linear combination of TF components (atoms) that
are selected from a redundant dictionary [8].
Figure 1 represents the architecture of our proposed
framework for heart murmur detection. PCG signal from
an electronic stethoscope is processed along with a si-
multaneously recorded electrocardiogram (ECG) stream
as a reference signal. First, preprocessing stage is carried
out consisting of filtering, down sampling, and beat seg-
mentation. Next, all the beats are decomposed into a se-
ries of TF atoms using the MP algorithm and a redundant
Gabor-type dictionary. Each selected atom is described
by a set of parameters including the amplitude, position
in time, scale, frequency, and phase. Groups of atoms
with similar morphology of parameters are then isolated
into clusters. For each cluster, atoms are merged together