02-05-2011, 04:03 PM
Abstract—Noise is generally unavoidable during recordings of
heart sound signal. Therefore, noise reduction is one of the important
preprocesses in the analysis of heart sound signal. This
was achieved in joint cycle frequency–time–frequency domains in
this study. Heart sound signal was decomposed into components
(called atoms) characterized by time delay, frequency, amplitude,
time width, and phase. It was discovered that atoms of heart sound
signal congregate in the joint domains. On the other hand, atoms
of noise were dispersed. The atoms of heart sound signal could,
therefore, be separated from the atoms of noise based on fuzzy
detection. In a practical experiment, heart sound signal was successfully
separated fromlung sounds and disturbances due to chest
motion. Computer simulations for various clinical heart sound signals
were also used to evaluate the performance of the proposed
noise reduction. It was shown that heart sound signal can be reconstructed
fromsimulated complex noise (perhaps non-Gaussian,
nonstationary, and colored). The proposed noise reduction can recover
variations in the both waveform and time delay of heart
sound signal during the reconstruction. Correlation coefficient and
normalized residue were used to indicate the closeness of the reconstructed
and noise-free heart sound signal. Correlation coefficient
may exceed 0.90 and normalized residue may be around 0.10 in 0-
dB noise environment, even if the phonocardiogram signal covers
only ten cardiac cycles.
Index Terms—Fuzzy detection, heart sound signal, joint cycle
frequency–time–frequency domains, noise reduction.
I. INTRODUCTION
PHONOCARDIOGRAPHY is a noninvasive technique that
is used to check the functioning of heart valves. It is, therefore,
widely used during medical examinations carried out by
physicians. However, ambient noise and disturbances can corrupt
the recorded heart sound signal. The sliding movements of
the stethoscope diaphragms in contact with the patient’s skin,
Manuscript received January 25, 2010; revised April 1, 2010; accepted April
30, 2010. Date of publication June 10, 2010; date of current version September
15, 2010. This work was supported in part by the National Natural Science
Foundation of China under Grant 30570475 and Grant 60872122. Asterisk
indicates corresponding author.
∗H. Tang is with the Department of Biomedical Engineering, Faculty of Electronic
Information and Electrical Engineering, Dalian University of Technology,
Dalian 116024, China (e-mail: tanghong[at]dlut.edu.cn).
T. Li is with the College of Electromechanical and Information Engineering,
Dalian Nationalities University, Dalian 116600, China (e-mail:
tracyli78[at]yahoo.com.cn).
Y. Park is with the Department of Information and Communication, Yeungnam
University, Gyeongsangbuk-Do 712-749, Korea (e-mail: ywpark@
yu.ac.kr).
T. Qiu is with the Department of Biomedical Engineering, Dalian University
of Technology, Dalian 116024, China (e-mail: qiutsh[at]dlut.edu.cn).
Digital Object Identifier 10.1109/TBME.2010.2051225
lung sounds, the muscular activity controlling lung movements,
and surround speech sounds represent a few of the sources of
noise and disturbance affecting the accuracy of data being acquired.
These noises usually have high amplitude and last for
only a short period of time. Furthermore, there is considerable
overlap in the time–frequency domains of heart sound signal
and noises. As heart sound signals are transient, they are easily
contaminated with the noises. Consequently, it may become
difficult for physicians to obtain the correct diagnostic information
by auscultation when noise and disturbances are important.
Noise reduction would allow a quantitative analysis of heart
sound signal and lead to a more reliable diagnosis. The disturbance
addressed in this paper is the impulsive noise interference,
which is typically characterized by noise pulses of short time
duration.
Over the years, various techniques of noise reduction have
been proposed for different purposes. Some techniques like
adaptive noise cancellation and filtering can be applied to reduce
noise from heart sound recordings [1], [2]. In particular, it
has been found that noise reduction performed in a time and/or
frequency domain may not be effective for non-Gaussian, nonstationary,
and colored noises. More specifically, it is considered
inappropriate because heart sound signal and noise overlap in
both time and frequency domains. On the contrary, techniques of
cyclostationary signal processing can reduce noise in the cycle
frequency domain. Beyar et al. [3] proposed to divide recordings
of heat sound signal into a sequence of repetitive cycles. Noise
was reduced simply by summations. However, heart sounds and
murmurs tend to occur with different timings from cycle to cycle
and cannot be totally preserved while noise is suppressed. In our
earlier paper [4], the timings of heart sounds and murmurs were
aligned from one cycle to next cycle by the nonlinear time scaling
(NTS).Noise and disturbance were subsequently reduced by
averaging. The results were promising, although segmentation
of the heart sound signal into first heart sound (S1) and second
heart sound (S2) is fundamentally needed to determine the parameters
for the NTS. This preprocess can have a detrimental
effect on the efficiency of the noise reduction. Its performance
degrades, if segmentation is inaccurate, or if the assumption that
heart sounds are consistent in consecutive cycles is not valid.
To avoid these commonly encountered limitations, we propose
a new noise reduction in this paper that is performed in the joint
cycle frequency–time–frequency domains based on fuzzy detection.
Comparing with the earlier study [4], one more domain,
frequency domain, is exploited. This proposed noise reduction
can accommodate variations in both time delay and waveform
of heart sounds, murmurs. No segmentation is needed. On the
other hand, it can be operated in a somewhat automated manner.
The paper is organized as follows. Section II outlines
the decomposition of heart sound signal into atoms.
Section III focuses on the quasi-cyclostationarity of the atoms. In
Section IV, we propose a fuzzy-detection method to detect atoms
of heart sound signal in the joint plane. Practical experiments
and various computer simulations are described in Section V.
In Section VI, we discuss the results, and in Section VII, observations
regarding performance comparisons are presented.
Finally, Section VIII summarizes our main conclusions.