A Robust Envelope Extraction Algorithm for Cardiac Sound Signal Segmentation Lisha Zhong, Xingming Guo*,An Ji and Xiaorong Ding College of Bioengineering, Chongqing University, China *corresponding author—email:
[email protected],cn Abstract-This paper presented a new method
segmentation method used to partition the heart
of envelope extraction algorithms based on
sound into clinically meaningful lobes, such as the
wavelet for cardiac sound signal segmentation.
S1 and the S2 sound components, systole and
In this paper, a new method based on Morlet
diastole duration should be developed.
wavelet is proposed to extract the energy envelope
of
heart
sound.
Noise
can
be
decomposed to a different frequency channel for smoothing, thereby reducing the impact of noise. And then physiological criteria of time interval, the ratio of systolic and diastolic duration and phonocardiogram (PCG) collecting position are employed to differentiate S1 and S2. The experiment results show that this envelope extraction algorithm is more sensitive (morlet detection rate of 88.29% versus 66.77% of Hilbert method) in processing signals with low
Many researchers have suggested methods for heart sound localization[1-3]. The complex and non-stationary nature of the cardiac sound signal can make it difficult to analyze in an automatic way. Generally the cardiac sound signal needs to be segmented into features for the automatic analysis and classification. Most of these methods have one step in common, which is envelope extraction. Hilbert transform [4-7] is the most popularized and wildly used extraction technique. However, it is prone to noise influence.
ratio of S/N (signal/noise). This indicates that
In this paper, a solution based on Morlet
our method is more robust for heart sound
wavelet is proposed to extract the energy envelope
signal detection.
of heart sound and a comparative study with Hilbert
Keywords-heart sound signal, morlet wavelet, envelope extraction, electrophysiological measurements
transformation approach was taken. The envelope of cardiac sound signal gives possible information on researching intrinsic characteristics of signals, while envelope is poor than sound signal and is just
I. INTRODUCTION The heart sound signal contains much useful information which may help the clinician in diagnoses and the researcher in learning more of the heart’s function. Many heart disorders can be effectively diagnosed using auscultation techniques. Most of lethal heart diseases, such as heart valve
an outline of original ones. Therefore, the approach of envelope extraction is very crucial to successful segmentation.
successful tools for early diagnosis. However, in the
the
methods
that
mentioned above are very sensitive to noise. A new method based on morlet wavelet is been proposed to solve this problem.
dysfunction or even in heart failure, heart sound auscultation is one of the most reliable and
However,
II. METHOD A.
Pre-processing
tele-monitoring heart sound system, auscultation is
We use heart sound detection instrument to
not feasible for its requirement of well-trained
acquire 35 volunteers’ HS recordings. The sounds
doctors. In order to facilitate the work of doctor and
were recorded with 16-bit accuracy and 11025Hz
be suitable for long time monitoring, an automatic
sampling frequency and stored as wav form in
Supported by National Natural Science Foundation of China (No. 30770551), Chongqing Science & Technology Commission (CSTC, 2008AC5103) and Chongqing University Postgraduates’ Science and Innovation Fund(No. 201005A1B0010336).
978-1-4244-5089-3/11/$26.00 ©2011 IEEE
computer.
The
recorded
signals
were
first
preprocessed before performing envelope extraction and segmentation. Heart sound signals were normalized according to (1) as shown below:
xnorm (t ) = ( where
x (t )
x(t ) )2 max( x(t ) )
(1)
is the original signal. The square
operation aims to make peak signal more prominent
Gaussian function[8].Morlet wavelet has a better time-frequency localization feature and smoothes noise interference. 2) Envelope Extraction method using Morlet wavelet: Wavelet function can be regard as a band-pass filter from signal analysis perspective. Morlet wavelet is a complex wavelet and therefore the corresponding filter is a complex one. The envelope amplitude can be obtained by the follow equation: W (a,τ ) =
1 a
∫ S (t ) ∗ ϕ (
t −τ ) dt a
(5)
while weaken the noise. B.
Re( W ( a , τ ) 2 ) + Im(W ( a , τ ) 2
En =
Envelope extraction
1) Morlet wavelet: Wavelet analysis has been put in a wide range use in many fields in recent years. With time-frequency localization features, wavelet analysis is the breaking up of a signal into dilations and translation versions of the original wavelet, referred to as the mother wavelet. The wavelets must be oscillatory, have amplitudes that quickly decay to zero, and have at least one vanishing moment. The Morlet wavelet is the modulated Gaussian function, the family function is built starting from the following complex
C.
(6)
Detection and classification of S1 and S2
sounds First of all need to identify possible peak position of S1 and S2. Energy envelope of S1 and S2 in time domain is a series of positive waves with relatively large amplitude according to Fig.1. The heart sounds S1 and S2 are identified and classified through the following steps:
Figure 1. Energy envelope of S1 and S2 in time domain
1) Set the amplitude threshold and remove the peaks whose amplitude is less than the set one: We
envelope data, the first order difference of 16 points can be obtained by the following equation.
use adaptive method to determine the available
yi = xi − xi −16
threshold. 2) Calculate the first order difference of the above envelope signal and determine differential threshold: We use the first order difference of 16 points. Assume
xi
is one point of energy
(7)
In this paper, self-learning algorithm is been used
to
establish
the
threshold.
Then
the
characteristic parameter obtained by calculation should be remembered and set as the criteria for
determining the threshold. The result of the first
order difference is shown in Fig. 2.
Figure 2. The first order difference
3) Find positive and negative difference pairs: The process of finding difference pairs is the process of looking for S1, S2 energy positive-going wave in time domain. Search from the beginning of the signal and find the first point with positive difference value, then keep searching in which all of the points with positive difference value should be remove until the first point with negative difference value is be identified. Repeat the above procedures. 4) Tag all of S1 and S2 locations: First, All of the difference pairs whose duration exceeds the
5)
S1 and S2 differentiation: We use the
following criteria to differentiate S1 and S2. a)
In the resting state , diastolic duration is
longer than the systolic duration. b) The accumulated energy of S1 is larger than that of S2. c)
When the heart rate increase, diastolic
duration reduces, resulting in systolic and diastolic duration almost is equal. S1 amplitude increase. 6) Find the starting and ending point of S1 and S2
threshold should be discarded, and then the left
Through the steps that mentioned above, S1 and
pairs are S1 and S2’s wave of rising edge and
S2 can be identified. The segmentation result is
falling edge. We tag the points which have the
shown in Fig. 3.
greatest amplitude between the rising edge and falling edge.
Figure 3. The result of segmentation
III.
RESULTS AND DISCUSSIONS
rate with respect to morlet wavelet envelope extraction algorithms. The results of morlet wavelet
Several examples of normal cardiac sounds with
and Hilbert method in energy envelope extraction
low and high noise interference were tested
are shown in Fig.4 and Fig.5, from which, the
experimentally to estimate correct segmentation
envelope extracted after morlet wavelet transform is
smoother than the direct use of Hilbert transform.
is poor. Furthermore, morlet wavelet has band-pass
Fig. 5 shows that morlet wavelet is effective
filtering capabilities. The filter center frequency and
because it is less sensitive to high frequency noise
bandwidth can be adjusted by the appropriate scale
while more sensitive to low frequency signal. The
a selection, so that the filter can cover interested
capability of Hilbert transform in noise suppression
band of signal and highlight useful information.
Figure 4. Comparison of morlet wavelet and Hilbert transformation method (signal with low noise)
Figure 5. Comparison of morlet wavelet and Hilbert transformation (signal with high noise TABLE I.
Results from morlet wavelet method
Sample signal
(N=24)
detected/presented Sensitivity
(N=24) Low noise
S1
S2
150/158
157/158
97.47%
S1
S2
Low noise
151/158
147/158
94.30%
High noise
113/158
98/158
66.77%
In Table 1 and 2, the results obtained for 48 heart sounds samples with low and high noise using
High noise
147/158
132/158
88.29%
different methods are shown. The sensitivity of signals with low noise display little variation
TABLE II.
Results from Hilbert transformation method
between morlet wavelet (97.47%) and Hilbert transformation (94.30%)
Sample signal
detected/presented
Sensitivity
method
while
huge
difference (88.29% VS. 66.77%) comes from the results in the signals with high noise.
IV.
sound reduction in lung sounds recordings” 25th Conf.
CONCLUSION
IEEE Engineering in Medicine and Biology Society, 2003,
A new method of envelope extraction algorithm for cardiac sound segmentation and detection
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Self-adaptive difference method is used to locate S1
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