2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, Dec. 18-20,2015, Sri Lanka
Crackle Separation and Classification from Normal
Respiratory Sounds Using Gaussian Mixture Model
Syed Osama Maruf, M. Usama Azhar, Sajid Gul Khawaja, M. Usman Akram
College of Electrical and Mechanical Engineering,
National University of Sciences and Technology, Pakistan.
maru(jr1991@hotmail.com, usamaazhar32@gmail.com, sajid.gu1.2009@gmail.com, usmakram@gmail.com
Abstract-Analysis of Respiratory sound signal is helpful in
detection of adventitious lung sound which are an indication
of disease. This helps in classification of normal respiratory
sounds from abnormal respiratory sounds and this can be used to
accurately diagnose respiratory diseases as is done by a medical
practitioner via auscultation. This process has subjective nature
and that is why simple auscultation cannot be relied upon. A
computer aided diagnostic system which analyzes respiratory
sounds can be very helpful in detection of various respiratory
diseases such as pneumonia, asthma, bronchitis and tuberculosis.
In this paper we present a novel method for automated detection
-0.3
of crackles which indicate severity of a respiratory disease. The
proposed system consists of four modules i.e., pre-processing in
which noise is filtered out, followed by feature extraction. The
proposed system then performs feature selection based on rank
tests and finally classification to separate crackles from normal
breath sounds.
I.
0.6
I N T RODUCTION
Millions of people die due to respiratory diseases all over
the world and according to World Health Organization (WHO)
Chronic Obstructive Pulmonary Disease (COPD) will become
the third leading cause of death worldwide by 2030. In Europe,
chronic obstructive pulmonary disease effects about 10% to
25% of the adult population [1]. Latest studies also show
that Pneumonia alone accounts for about a million deaths in
children annually. This represents 18% of all annual under­
five worldwide mortality [2] and about 98% of these deaths
occur in developing countries. The economic cost of asthma,
COPD, and pneumonia was $106 billion in 2009: $81 billion
in direct health expenditures $25 billion in indirect cost of
mortality [3]. The high mortality rate can be attributed to
the fact that there is a scarcity of trained medical health
professionals as compared with the number of patients and also
due to lack of awareness about health facilities in people of
backward areas. The diagnosis of these diseases is conducted
by recording of a detailed history of the patient followed by a
physical exam in which detection of adventitious lung sounds
as well as abnormal breath sounds is undertaken with the help
of a stethoscope; a process called auscultation. Adventitious
lung sounds are additional respiratory sounds superimposed on
breath sounds [4] and are divided into two types 1) stationary
and 2) non-stationary. Stationary adventitious lung sounds are
wheezes and rhonchi whereas non-stationary sounds include
crackles. Crackles are discontinuous explosive sounds which
occur usually during inspiration [4]. Figure 1 shows the
waveforms for normal breath and crackles.
978-1-4799-1876-8/15/$31.00 ©2015 IEEE
0.8
267
X
10!o
Fig. I.
Breath sound waveforms. Top: Normal breath sound waveform;
Bottom: Crackle sound waveform
The occurrence of crackles is an indication of the severity of
the pulmonary disease [5] and simple auscultation cannot be
relied upon as auscultation with a stethoscope is a subjective
process since it depends on the individual's own hearing, ex­
perience and ability to distinguish different sounds patterns[l].
Thus there is a need for a system which accurately detects the
presence of crackles in the respiratory sound of patient. This
system would not be able to replace the doctor but would be
valuable for countries where the doctor to patient ratio is low.
This article consists of five sections. Section 2 highlights
existing methods and related work for respiratory sound anal­
ysis. Section 3 describes a brief overview and all steps of
the proposed system. The results are presented in Section 4
followed by conclusions in Section 5.
2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, Dec. 18-20,2015, Sri Lanka
II.
RELAT ED WORK
Respiratory Sound Analysis is a relatively new area of
interest for researchers and not many methods of detection
and classification of crackles have been formulated. Presence
of crackles is strong indicator of pathological illness and it
is imperative that methods should be devised for its detec­
tion to help in clinical diagnosis. The existing methods are
broadly categorized as the non-linear separation stationary
non-stationary filter (ST-NST) [6] and its several modified
version [7], [8], the wavelet transform-based stationary-non­
stationary filter (WTST-NST) [9] and the generalized fuzzy
rule-based stationary non-stationary filter (GFST-NST)[10].
Mohammed Bahoura and Xiaoguang Lu [11], [12] pre­
sented a model WPST- NST method based on double thresh­
holding in wavelet domain by using time domain features
which separates crackle's coefficients. Unlike simple wavelet
transform, wavelet packet transform is obtained by applying
wavelet transform at every level which is equivalent to multi­
channel filtering. Using this model an accuracy of 93.9%
is achieved [12].Fatma Ayari et al. [13] has discussed two
methodologies to classify crackles and their extraction from
the lung signal. The first one is statistics based methodology
and the second is fuzzy nonlinear classifiers. Nine features
have been selected to enhance behavior of crackle. These
features relate to amplitude, time and waveform. Sensitivity
of 98.34% and positive predictive value of 97.88% have been
achieved. Martinez-Hernandez et al. [14] used lung sounds
which are acquired by multi-channel microphone array on
which feature extraction is done by multivariate AR model
and after dimensionality reduction techniques like PCA and
SVD are applied, the classification was done by SNN (Super­
vised Neural Network) using back-propagation method and
Levenberg-Marquardt rule a.k.a. damped-least square. They
classified DIP (diffuse interstitial pneumonia) characterized by
crackles (100-2000 Hz). SNN is then applied and gave best
results at 20 nodes for SVD and 25 nodes for PCA. MAR
in conjunction with PCA is superior it gave an accuracy of
92.86% in the testing phase.
III.
N o rm al
Fig. 2.
A.
PROPOSED M ET HOD
268
Flow diagram of proposed system
Preprocessing
Pre-Processing of the lung sound is done with two objectives
in mind, i.e., reduction of background noises and the other is
to enhance the quality of the recorded sound. To cater for
the former objective pre-filtering using a band-pass filter with
cut-off frequencies at 100 Hz to reduce heart sounds and 2500
Hz to eliminate high- frequency noise is applied [15] as it is
within these frequency ranges that the lung sounds are present
[16].
B.
Over the past few years computerized methods of respi­
ratory sound acquisition and analysis have overcome many
shortcomings of simple auscultation [l].Computer aided di­
agnostic systems have brought new horizons in detection and
treatment of many diseases. Thus, many different techniques
have been used to acquire and then use respiratory sounds for
diagnosis of pulmonary diseases. Figure 2 shows a complete
flow diagram for all the phases of the proposed system start­
ing from sound acquisition to classification of crackles. The
proposed system is divided into four phases, i.e., acquisition
of the lung sound and preprocessing, feature extraction and
wavelet decomposition with removal of the silent phase, and
finally feature selection and classification. A Gaussian Mixture
Model based on a Bayes decision rule is used to differentiate
crackles from normal respiratory sounds.
Cra c k l e s
Feature Extraction
A feature set is formed to distinguish between normal
respiratory sounds and crackles. The formation of the feature
vector was done by the following method: The pre-processed
sound files are used to calculate three spatial-temporal features
namely, pitch, energy and spectrogram. These features are
extracted from the entire spectrum after which the wavelet
decomposition is done using Daubechies-8 wavelet for 5th
level decomposition tree. The levels containing silent phase
are then discarded and the level with the most information,
i.e., the last level is then used to calculate other 9 features.
The entire feature vector contains 12 features which are of
the form Iv
Xl, X2, X3, ........., X12. The features which we
have used are Pitch(xl), Spectrogram(x2), Energy(x3), Shan­
non Entropy(x4), Mean(x5), Range (x6), IQR(x7), MAD(x8),
Moment(x9), Skewness (x10), Kurtosis(x11) and PSD(x12).
=
2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, Dec. 18-20,2015, Sri Lanka
The description of the features in the feature vector is given
below:
1) Pitch(Xl) is associated with the frequency of sound
wave and allows its ordering based on it [17], i.e., high
pitch is attributed to sound having a high frequency
and low pitch is associated with sound having a low
frequency.
2) Spectrogram(x2) is a visual representation of the spec­
trum of frequencies in a sound or other signal as they
vary with time.
3) Energy(x3) is the energy of wavelet or wavelet packet
decomposition.
4) Shannon Entropy(x4) is measure of diversity or ran­
domness of the sound wave [18].
5) lvIean(xs) of the sound is calculated for each candidate
region in order to differentiate between crackles and
normal respiratory sounds.
6) Range(x6) is the difference between the maximum and
minimum of the sound sample.
7) IQR(x7) is the inter-quartile range of the sound in time­
domain.
8) !vI AD(xs) is the median absolute deviation of the
sound. Moment(x9) central sample moment of sound
specified by the positive integer order.
9) Skewness(xlO) is a measure of the asymmetry of the
probability distribution of a real-valued random variable
about its mean.
10) Kurtosis(Xll) is a descriptor of the shape of the prob­
ability distribution of the real- valued random variable
[19].
11) PSD(X12) the power spectrum density is the average
power of the sound spectrum.
TABLE I
ANALYSIS OF FEATURES FOR FEATURE SELECTION USING RANKSUM
TEST
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Ansari Bradley Test
P Value
Score
292
0.9305
0.397
275
0.567
280
252
0.034
0.422
305
260
0.09
252
0.034
0.0517
255
314
0.19
341
0.005
324
0.062
0.051
255
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C. Feature Selection
269
Test
Score
219
228
215
295
302
361
293
278
323
342
441
278
R nSt
Feature extraction phase extracts different features out of
which best features are selected by feature selection phase
using rank tests.
Feature selection phase selects a feature set from complete
feature vector using two rank sum tests i.e. Wilcoxon Rank­
Sum and Ansari-Bradley Tests. Wilcoxon Rank-Sum test is a
non-parametric test of the null hypothesis that two populations
are the same against an alternative hypothesis that the two dis­
tributions differ only with respect to the median. It has higher
efficiency on non- normal distributions such as a mixture of
normal distributions [20]. Ansari-Bradley test compares two
independent samples which come from the same distribution
against the alternative that they come from same distributions
having the same median and shape but different variances [21].
Table 1 shows the results obtained when Wilcoxon Rank-Sum
and Ansari-Bradley tests are applied to different features.
The features showing the least P -value are selected based
on the results of rank sum tests. Top four common features
with minimum p - value are selected which are Range(x6),
IQR(x7), !vI AD(xs) and Kurtosis(Xl1). They are then used
to classify crackles from normal respiratory sounds. Figure 3
shows the plots for top four features
Wilcoxon
P Value
0.0405
0.0717
0.0309
0.989
0.8366
0.0675
0.989
0.67
0.433
0.191
0.00005
0.67
Features
Pitch
Spectrogram
Energy
Entropy
Mean
Range
IQR
Mad
Moment
Skewness
Kurtosis
PSD
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Fig. 3. 3 dimensional scatter plots. Top plot shows the scatter plot for range,
MAD and IQR. Bottom plot shows the scatter plot for kurtosis, MAD and
IQR.
D.
Classification
For the purpose of classification, we use a Bayesian classi­
fier using Gaussian functions known as the Gaussian Mixture
Model [22]. The Gaussian Mixture Model offers fast and
accurate performance in the testing phase while being trained
extensively in the training phase. In our context, each sound
class is modeled by a Gaussian Mixture Model obtained
by training [23]. The training is done by using a training
data set with the respiratory sounds labeled as crackles and
normal after which the Bayes decision rule is used in the
2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, Dec. 18-20,2015, Sri Lanka
estimation of a decision criteria from the training set. Two
classes Xl
cr-ackles and X2
nor-mal have been defined.
The parameters for GMM are optimized using Expectation
Maximization (EM) which is an iterative method and it
chooses optimal parameters by finding the local maximum
value of GMM distributions for training data [24].
Bayes decision rule is stated as [24]
=
=
Choose
iJ,p(vIRl)P(Rl)
Rl
other-wise
choose
R2
>
accuracy as figures of merit. These parameters were calculated
using equations 1-4 respectively.
Sensitivity(Sen)
SpeciJicity(Spec)
p(vIR2)P(R2)
PPV
ACCUTacy(Acc)
=
p(vIRi)
=
L N(vlJ.Lj, �j)Wj
j=l
(3)
Expectation Maximization is done to select an optimum value
of k where in the estimation step PE of each point of the
Gaussian is calculated using
PE(n,J ) .
_
wjN(vnI/Lj, �j)
"
L... i=l N(vnIJ.Li, �i)Wi
"",
1
=
NTo!."l
�j
�
PE(n,j)(vn -,Lj)(Vn - J.Ljf
Wj -
�j
---
NTotal
(6)
(7)
L;:�'(Ol PE(n,j) and NTotal are the total number
where �j
candidate regions.
=
IV.
(10)
(TP+FP)
(TP+TN)
=
(9)
(TN+FP)
(TP+TN+FN+FP)
(11)
•
•
•
TP are true positives, meaning crackles are correctly
classified.
TN are true negatives, meaning normal respiratory sounds
are correctly classified.
FP are false positives, meaning normal respiratory sounds
are wrongly classified as crackles regions.
FN are false negatives, meaning crackles are wrongly
classified as normal respiratory sounds.
Table-2 shows the evaluation results of proposed system and its
comparison with other classifiers like support Vector Machine
(SVM) and Artificial Neural Network (ANN) consisting of 10
nodes in the input layer.
TABLE II
PROPOSED SYSTEM EVALUATION RESULTS
Classifier
GMM
SVM
ANN
TP
13
13
10
TN
27
25
25
FN
I
1
4
FP
0
2
2
Sen
92.85
92.8
85.3
Spec
100
92.5
7l.2
PPV
100
86.6
92.5
Acc
97.56
92.6
85.3
(4)
In the final step maximization of the likelihood function is
achieved by re-calculation of covariance matrix, mean and
weight of the jth Gaussian by using the equations given below:
�j
TN
=
where
•
where v and J.L are feature vector containing m number of
features and mean vector containing mean of each feature
respectively. � is a m x m covariance matrix. In our case
m
3. Then the likelihood function of the Gaussian is
calculated as
(8)
(TP+FN)
TP
=
(I)
where p(vIRi) is the class conditional Probability Density
Function (pdt) also known as likelihood and P(Ri) is the prior
probability of class Ri which is calculated as the ratio of class
Ri samples in the training set. The conditional probability
density function of the feature vector for the two classes is
calculated using
TP
=
EXPERIMENTAL RESULT S
The evaluation of proposed system is done using respiratory
sound files. We use a sound repository of 41 files out of which
14 have been categorized as crackles by a human grader. The
performance of the proposed system has been evaluated using
sensitivity, specificity ,positive predictive value (PPV) and
270
Gaussian Mixture Model out performs the other classifiers
and is thus selected as the classifier of choice in our proposed
method. The effectiveness of GMM can be ascertained from
the high accuracy shown in the testing phase.
For performance comparison with existing methods, we
present the values of sensitivity, specificity, PPV and accuracy
of the methods of Xiaoguang Lu et al. [11] , Martinez­
Hernandez et al. [15] and Fatma Ayari et al. [13]. The results
of these comparisons are shown in table-3.
TABLE III
PERFORMANCE COMPARISON OF PROPOSED SYSTEM WITH EXISTING
METHODS
Method
Lu, Xiaoguang et al. [11]
Martinez-Hernandez et al. [15]
Fatima Ayari et al. [13]
PM
Sen
92.9
91.38
98.34
92.85
Spec
PPV
94. 4
94.34
100
97.88
100
Acc
93.9
92.86
97.56
The table shows that the proposed system outperforms in
terms of accuracy of classification. The reason for the improve­
ment can be accredited to the fact that in the proposed method
a detailed feature set and classifier have been employed which
is not done by the above mentioned authors.
2015 IEEE 10th International Conference on Industrial and Information Systems, ICIIS 2015, Dec. 18-20,2015, Sri Lanka
V.
CONCLUSION
Digital signal processing techniques can be applied to study
lung sounds as an aid to clinical diagnosis [23]. New methods
for identifying and measuring adventitious lung sounds are
being developed in various research institutes [25]. We have
introduced a new classification tool for Crackles analysis as
their detection is important for the evaluation of the severity
of the respiratory disease. The proposed system consisted of
four phases i.e. preprocessing, feature extraction, feature se­
lection and finally the classification. We have used sensitivity,
specificity, PPV and accuracy to evaluate our proposed system.
The system has achieved specificity of 100%, sensitivity of
92.85%, PPV of 100% and accuracy of 97.56% which are
better than the recently published methods. It is evident
from the comparison with previous systems that the proposed
method has outperformed them and has classified crackles
form other respiratory sounds with good accuracy.
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