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Journal of Medical Systems, Vol. 29, No. 3, June 2005 (
DOI: 10.1007/s10916-005-5182-9
Combining Neural Network and Genetic Algorithm
for Prediction of Lung Sounds
İnan Güler,1,3 Hüseyin Polat,1 and Uçman Ergün2
Recognition of lung sounds is an important goal in pulmonary medicine. In this
work, we present a study for neural networks–genetic algorithm approach intended to
aid in lung sound classification. Lung sound was captured from the chest wall of The
subjects with different pulmonary diseases and also from the healthy subjects. Sound
intervals with duration of 15–20 s were sampled from subjects. From each interval,
full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier
Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated
by the spectral analysis are selected by genetic algorithm and applied to neural
network. Multilayer perceptron (MLP) neural network employing backpropagation
training algorithm was used to predict the presence or absence of adventitious sounds
(wheeze and crackle). We used genetic algorithms to search for optimal structure and
training parameters of neural network for a better predicting of lung sounds. This
application resulted in designing of optimum network structure and, hence reducing
the processing load and time.
KEY WORDS: lung sounds; respiratory diseases; auscultation; neural network; MLP; genetic algorithm.
INTRODUCTION
Respiratory diseases pose major medical problems. A large number of all
populations suffer from diseases such as asthma and pneumonia. For this reason,
early detection of respiratory disorders is one of the most important medical
research areas. Fundamental tools in the diagnosis of respiratory diseases are
chest X-rays, computerized tomography (CT), pulmonary function testing and
pulmonary auscultation. Chest X-rays and CT scan provide the physicians and
patients with a clear picture of the lungs and air passages. But these methods are
1 Department
of Electronic and Computer Education, Faculty of Technical Education, Gazi University,
06500 Teknikokullar, Ankara, Turkey.
2 Department of Electrical and Electronic Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyon, Turkey.
3 To whom correspondence should be addresses; e-mail: [email protected].
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0148-5598/05/0600-0217/0 218
Güler, Polat, and Ergün
expensive and with X-rays, patients are exposed to potentially harmful doses of
radiation. Unfortunately, the radiation problem is compounded by the fact that
X-rays are often performed unnecessarily. Repeated X-rays put the patient at risk
for development of cancers and growth disorders. Additionally, misdiagnosis of
respiratory diseases may also occur while using X-rays and CT due to large bulla
and cysts within the lung or pleural space, patient clothing, tubing, skin folds,
and chest wall artifacts. As for function testing provides information about the
mechanical characteristics of the lungs and airway, such as lung volumes or airway
resistance. The tests require cooperation of the patient, and thus cannot be applied
to infants. Thus such methods are rarely used for routine monitoring of at-risk
patients. Their use is chiefly diagnostic, after a problem is suspected.
Among these, auscultation is much less expensive than the other methods, and
is still the most common diagnostic method. Auscultation provides the physicians
with qualitative information concerning the source and location of an abnormal
acoustic noise generated by some disturbance of the lungs or airway. Conventional
auscultation using a simple stethoscope and manual analysis is convenient. The
stethoscope has been in use since the early 1800s, and is currently used twice as
much as all other diagnostic procedures combined. In some circumstances, particularly in remote areas or developing countries, auscultation may be the only method
available.
Although auscultation is the most widely used method of diagnosis, still many
problems exist. Auscultation is a highly subjective technique requiring a lengthy
application of the procedure. Nor does it provide diagnostic information of the
quality of the X-rays, CT and function testing methods. Also, auscultation with a
classical stethoscope has many limitations. It is a subjective process that depends on
the individual’s own hearing, experience and ability to differentiate between different sound patterns. It is not easy to produce quantitative measurements or make a
permanent record of an examination in documentary form. Moreover, the classical
stethoscope has a frequency response that attenuates frequency components of
the lung sound signal above about 120 Hz and the human ear is not very sensitive
to the lower frequency band that remains.(1) It would be very advantageous if the
benefits of auscultation could be obtained with a reduced learning curve, using
computer-based analysis of lung sound analysis that is robust, and easy to use.
In the last decade, the availability of computer technology has prompted
many research efforts in the area of lung sounds and provided knowledge
beyond what has been known on the basis of classical auscultation. Especially, the
knowledge about generation and transmission of both normal breath sounds and
adventitious lung sounds and about their characteristics in diseases of airways and
pulmonary tissue has increased significantly.(2) Findings on characteristic changes
of lung sounds in asthma and other airway diseases(3−7) and in pulmonary tissue
diseases(8−14) have stimulated the interest in clinical and occupational lung sound
studies. Thus, computer-based methods for the recording and analysis of lung
sounds have overcome many limitations of classical auscultation.
Most of the research on lung sound analysis has been concentrated on comparing the sound of a specific pathological condition versus normal lung sounds.
Lung sounds are highly non-stationary stochastic signals due to changing air flow
Prediction of Lung Sounds
219
rate and lung volumes during a breath cycle. This makes the analysis of lung
sounds difficult. Conventional methods (time and frequency analysis) are not highly
successful in diagnostic classification. Therefore, automatic recognition of lung
sounds is useful in providing a computer-aided tool to auscultation and increases
its potential diagnostic value.(17) This will require a classification method that has
the ability to discriminate various classes. Artificial neural networks (ANNs) are
well suited for this task. There has been much excitement in the scientific literature
in recent years regarding ANNs.(18,19) Studies in the field of ANNs and their
application to the detection of specific types of lung sounds have shown promising
results.(20−25)
Despite the advantages, constructing an ANNs model is a complicated process
due to the presence of several training parameters with initially unknown optimal
values. In most cases, they are determined by the trial and error method, thereby
causing a heavy computational burden and low efficiency. In this work, a method of
circumventing the drawbacks of current ANNs is presented. This is accomplished
using genetic algorithms to optimise the training parameters.
Designing neural networks through genetic algorithms has been investigated
for many years and comprehensive reviews can be found in.(26,27) Genetic algorithms have been used with neural network to search for input variables(28−30) or to
determine the number of nodes or connections in the network.(31,32) Analysis of lung
sounds using the neural network requires very large network structure. We used genetic algorithms to search for optimal structure and training parameters for neural
network in order to have better prediction of lung sounds. The genetic algorithm
searches optimal combinations of input units. Elimination of unnecessary inputs
results in designing optimum network structure and in reducing the processing load
and time. Similar genetic algorithms have been used in other medical domains to derive neural network to predict response to warfarin,(29) predict community-acquired
pneumonia,(32) outcome in critical illness,(31) and prostate carcinoma.(30)
In this work we used hybrid genetic algorithm and neural network classification
approach for lung sounds diagnosis. The purposed approach was evaluated using
lung sounds corresponding to three different lung sound types: normal, wheeze and
crackle sounds. In order to predict, normal, wheeze and crackle sounds by interpreting the lung sounds, those same lung sounds are processes by means of Welch
method and PSD estimation calculations have been applied to the hybrid genetic
algorithm and neural networks (GANN).
MATERIALS AND METHODS
Lung Sounds
Lung sounds are generated by the movement of air as it travels through the
bronchial tree. Fluctuations in air flow created by turbulence, vortex shedding, and
oscillations of tissue structures are responsible for our ability to hear air flow within
the lungs. Turbulence occurs when flow reaches a critical velocity and the orderly
arrangement of particles, found in laminar flow, becomes disrupted. Random
movement of these particles results in the transfer of energy between colliding
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molecules, as well as transient pressure fluctuations. Changes in air pressure create
sounds, each possessing specific amplitude and frequency.(15)
The detected sounds from lung are typically classified into normal lung sounds
and adventitious (abnormal) sounds, being the later usually associated to some respiratory disorder. The breathing associated sound heard on the chest of a healthy
person is called the normal lung sound. The normal lung sound is characterized by
larger, louder sounds during inspiration than during expiration.
The adventitious sounds are further divided into two classes: continuous and
discontinuous. Continuous adventitious sounds are wheezes and rhonchi. Wheezes
are produced by obstructions located in the lungs. They sound like a whistle and
can be composed of a single pitch or many harmonic tones. They can occur either
on the expiration or the inspiration. They have many harmonics with fundamental
frequency above 400 Hz, and the event is longer than 250 ms. Rhonchi are also
longer than 250 ms in duration low pitched, with dominant frequency of 200 Hz or
less. Discontinuous adventitious sounds correspond to crackles, which are explained
by two theories. The first hypothesis intends to associate this sound to the sudden
opening of bronchial alveoli, and the second one is the bubbling of air through
excessive secretions in the lung. These sounds are respectively called fine and
coarse crackles. It is evident that these phenomena generate transient signals.(2,16)
Measurement of Lung Sounds
Measurement system of lung sound consists of electret microphone (EK3024 Knowles), amplifier, high-pass filter, low-pass filter, 16-bit sound card (Sound
Blaster compatible), and PIII 550 MHz a portable computer as shown in Fig. 1.
The lung sounds were captured on the chest wall of the subject using an electret
microphone. A classical stethoscope was modified by cutting the tube below the fork
to the earpieces and inserting an electret microphone in the tube, which ensured
accurate detection of lung sounds. The lung sound signal was amplified then high
pass filtered at 80 Hz (to eliminate muscle and heart sounds) and low pass filtered
at 2000 Hz (to avoid aliasing). This signal was digitized with a sampling frequency
of 8000 Hz with 16-bit resolution. Signal acquisition is provided using Sound Blaster
compatible 16-bit sound card interface.
Digitized lung sounds stored as a sound file on the hard disk of the PC. Lung
sounds from the chest were recorded in a quiet room from patients with different
pulmonary diseases. The recording time varied from 15 to 20 s. For comparison,
the sounds were analysed by pulmonary physicians, who based their detection of
adventitious sounds on auditory and visual inspection of the sound signal and its
waveform. From each lung sound record, at least a full breath cycle was randomly
Fig. 1. Block diagram of measurement system.
Prediction of Lung Sounds
221
selected for the computation of spectrum. Spectral variables are then obtained from
this file using spectral analysis software developed in MATLAB R12.
Spectral Analysis of Lung Sounds
The concept behind spectral or Fourier analysis is that any signal in the time
domain can be converted to a series of sine waves with different amplitudes,
frequencies and phases. The conversion of a signal to a sinusoidal representation
is called the Fourier Transform, named after Baron Jean Baptiste Joseph Fourier
who developed this theory in the early 1800s.
FFT methods such as Welch method are defined as classical (nonparametric)
methods. Welch spectral estimator is one of the FFT methods and relies on the definition of periodogram method. If the available information consists of the samples
{x(n)}N
n=1 , the periodogram spectral estimator is defined as
2
N
1 PPER (f ) =
x(n) exp(−j 2πf n)
N
(1)
n=1
where PPER (f ) is the estimation of periodogram.
Lung sound signals were divided into overlapping intervals and windowed using
a Hanning window. Periodograms were first calculated and then averaged. {xl (n)},
l = 1, . . . , K are signal intervals and each interval’s length equals M. In this method,
the overlapping ratio is taken as 50%. The Welch spectral estimator is defined as
2
M
K
1 1 1 Pl (f ) =
v(n)xl (n) exp(−j 2πf n)
and PW (f ) =
Pl (f ) (2)
MP
K
n=1
l=1
where
v(n) is2 data window, P the average of v(n) given as P =
1/M M
n=1 [v(n)] , PW (f ) the Welch power spectral density, and K the number of signal intervals. Welch spectral estimator can be efficiently computed
via FFT and is one of the most frequently used power spectrum density (PSD)
estimation methods.(33)
Lung sound’s (each sample being at least a full respiration cycle) power spectral
density estimates were calculated by using the Welch method for each subject. The
first 129 points of the logarithm of the spectrum were used as network inputs in this
study.
Artificial Neural Network
The neural network structure used in this work is formed by neurons in the
input, hidden and output layers as shown in Fig. 2. Lung sound’s PSD estimates
were calculated using the Welch method for each subject. Then 129 points of the
logarithm of the PSD values obtained from the lung sounds are assigned one-to-one
to the neurons in the input layer. Thus, the sound type is diagnosed from output
neurons by applying the new data obtained from the patients to the input neurons
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Fig. 2. Neural network structure used in this study.
with the utilization of neural network. There are three neurons for predictions of
normal, wheeze and crackle at output layer.
The value of the neuron at the output of neural network (calculated diagnosis)
is compared with the real diagnosis information and the difference between them is
calculated as the error. Mean square error (MSE) is used to decide if the expected
and calculated values of network output are approximate. One of the most important topics that should be applied during the learning process of the neural networks
is to adjust learning rate and momentum term. The momentum coefficient and the
size of the step were taken as 0.7 and 0.1, respectively.
For comparison of the diagnostic accuracy of the different classification methods and groups, the concept of receiver operating characteristic (ROC) analysis was
used. ROC analysis is an appropriate means to display sensitivity and specificity
relationships when a predictive output for two possibilities is continuous. In its tabular form the ROC analysis displays true and false positive and negative totals and
sensitivity and specificity for each listed cutoff value between 0 and 1. The ROC
curves are a more complete representation of the classification performance than
the report of a single pair of sensitivity and specificity values.
Prediction of Lung Sounds
223
Hybrid Genetic Algorithm and Neural Network Approach
Genetic algorithms are stochastic optimization algorithms, which have proved
to be effective in various applications. A typical genetic algorithm maintains a population of solutions and implements a ‘survival of the fittest’ strategy in the search
for better solutions. In this study a genetic algorithm is used to obtain near-optimal
neural network structure.
We have used a supervised network with back-propagation learning rule and
MLP architecture and also re-arrange the weights for minimizing the prediction
errors. Apart from the weights of a network, there are several other unknown parameters of neural network architecture. These include the number of input units,
the optimum combination of input units, the number of hidden neurons for each
layer, the value of the learning rate and the momentum rate parameter (the two
latter parameters are typically for a back-propagation training algorithm). In the absence of any a priori information about these parameters, the choice of this is rather
subjective and depends on the experience of the experimenter. As a result of this
subjective choice of structural parameters of the neural network, there is always a
risk that the solution will be trapped in a local minimum. Ideally, the network architecture for a particular problem has to be optimized over the entire parameter space
of such parameters. We use a genetic algorithm search procedure of the survival of
the fittest for arriving at the optimum values of these neural network parameters.
The chromosome structures consist of both binary and real parts, as it is demonstrated in Fig. 3. The first part of the chromosome structure is used to select the data
applied to the input of neural networks. If the value of the gene, which is coded in binary system, is “0,” then application of these data to the neural network is blocked,
or contrarily, if the value is “1,” then it is applied. Those genes located in the reel
part are used for coding the number of hidden layers, the number of neurons in the
hidden layer(s) (there may be one or two hidden layers), learning rate and momentum parameters. The effect of increased hidden layer numbers over the performance
may be evaluated by changing the hidden layer number [1 , 2]. Number of neurons
at the hidden layer(s) may be in any values between [0 , 20], whereas the learning
rate and momentum may be in values of [0 , 1]. Chromosomes structure may be
re-arranged to allow using single or two hidden layers in the experimental studies.
The encoded chromosomes are searched to optimize a fitness function. The
fitness function is specific to applications. In this study, the fitness function is
the average deviation between expected and predicted values of product costs.
The fitness value of a chromosome is calculated using the mean squared error of
Fig. 3. GANN chromosome structure.
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Güler, Polat, and Ergün
neural network architecture. A fitness value F is given by
F=
1
MSE + 1
(3)
where MSE is the mean squared error of neural network. Thus, the smaller the
network’s MSE, the closer a fitness value to 1. Once fitness values of all chromosomes are evaluated, a population of chromosomes is updated using three genetic
operators: selection, crossover and mutation.
In this proposed GANN algorithm, the roulette wheel method is used as the
selection mechanism to determine which population members are chosen as parents
that will create offspring for the next generation. The crossover strategy determines
how the parents selected by the roulette wheel procedure are combined in order to
produce offspring.
The training and testing data is used to search the optimal or near-optimal
parameters and is employed to evaluate the fitness function. The crossover and mutation probabilities are 0.9 and 0.1, respectively. In addition, we started with a population of 50 random networks, and evolved these networks through 100 generations.
Prediction of Lung Sounds by GANN
The flow chart of proposed GANN diagnostic procedure is submitted in Fig. 4.
The lung sounds (full breath cycle sampled from subjects) have been analyzed by
using Welch method. PSD logarithm values of 129 points, which are found as the
result of spectral analysis grouped into the training and test datasets. Later the training dataset applied hybrid GANN approach to train the neural network. First, the
genetic algorithm selects inputs and parameters to design the neural network structure. Individual fitness values are defined by using MSE values of each individual’s
designed neural network. As a result of processing of individuals by the genetic operators such as selection, crossover and mutation, new generations are established
and finally genetic algorithm-based selection process is completed.
At the end of search process of optimum input combinations and parameters
by genetic algorithm, a neural network for best prediction of medical diagnosis is
designed. Consequently, neural network designed by GANN allows a better prediction of medical diagnosis (normal, wheeze and crackle) than traditional neural
networks.
RESULTS
Lung sounds were obtained from 96 subjects, 56 of them had suffered from
pulmonary diseases and the rest of them had been healthy subjects (Table I). Sound
intervals with duration of 15–20 s were sampled from subjects. From each interval,
full breath cycles were selected. Of each selected breath cycle, PSD values were
calculated by Welch method. Then 129 points of the logarithm of the PSD values
were entered as in the MLP neural networks or selected by genetic algorithm in the
GANN, and applied to the input layer of the neural network. In this study, 48 of 96
Fig. 4. Flow chart of proposed GANN diagnostic procedure.
Prediction of Lung Sounds
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Güler, Polat, and Ergün
Table I. Distribution of Training and Test
Groups
Class
Training set
Test set
Total
20
12
16
48
20
12
16
48
40
24
32
96
Normal
Wheeze
Crackle
Total
subjects were used for training and the remaining of them were used for testing, as
shown in Table I. The output vector, depending on the exits of the three neurons, is
determined as:
[0 0 1] = normal
[0 1 0] = wheeze
[1 0 0] = crackle
We have compared different types of genetic algorithms with back-propagationbased neural network (referred as GANN in the tables) and back-propagationbased MLP neural networks (referred as ANN in the tables). As indicated in the
training results (Tables II and III) and the test dataset results (Tables IV and V),
there have been four different ANN and two different GANN used for the classification of lung sound signals. MLP neural networks are structured as single hidden layer—6 neurons in hidden layer, single hidden layer—12 neurons in hidden
layer, two hidden layers—6 neurons in each hidden layer, and two hidden layers—
12 neurons in each hidden layer, respectively. Consequently, it is clearly seen how
the changes in the number of hidden layers and the numbers of neurons in the
hidden layers affect the classification performances.
There have been two different structures used to examine and evaluate the
effects of genetic algorithms over the classification performance. The GANN inputs
not optimized system may only be optimizing the network parameters (number
of hidden layers, numbers of neuron at the hidden layers, learning rate, and
momentum) however, cannot perform the selection of inputs. Consequently, the
Table II. The Training Performance of Different Types of Neural
Networks
Neural networkstructure
Number of
neurons in
layersa
MSE
AUCb
ANN one hidden layer
ANN one hidden layer
ANN two hidden lay r
ANN two hidden layer
GANN inputs not selected
GANN
129-6-3
129-12-3
129-6-8-3
129-12-12-3
129-10-12-3
61-7-11-3
0.0236
0.0075
0.0115
0.0049
0.0171
0.0034
0.953
0.957
0.963
0.979
0.971
0.982
a In
Training
the one hidden layer structures; the input layer–hidden layer–
output layer, and in the two hidden layers structures; the input
layer–first hidden layer–second hidden layer-output layer.
bAUC: Area under ROC curve.
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227
Table III. The Classification Results of Training
Training
Neural network structure
Na
Wb
Cc
Ad
ANN one hidden layer
ANN one hidden layer
ANN two hidden layer
ANN two hidden layer
GANN inputs not selected
GANN
80
90
90
95
85
95
75
83.3
91.6
83.3
83.3
91.7
87.5
87.5
87.5
93.8
81.2
93.8
81.3
87.5
89.5
91.7
83.3
93.8
a N: Correct classification ratio of normal sounds.
bW: Correct classification ratio of wheeze sounds.
c C: Correct classification ratio of crackle sounds.
d A: Correct classification of all lung sounds.
only performance increase is due by an optimization of network parameters. The
results that have been obtained by using GANN structure (single or two hidden
layers) are given.
Parallel results are seen on examination of classification performances given for
training in Table III, and for test datasets in Table V. Additionally, since the neural
network structures were trained with the training datasets, the performances belong
to training datasets are considerably higher than the test datasets.
Upon examination and comparison of the MLP neural network structures, it is
noted that increase in the number of hidden layers and neurons within the hidden
layer results in increase of performances. While the area under ROC curve is 0.95 in
the single hidden layer, increases to over 0.96 in the two hidden layers. Similarly, the
increase of the numbers of neurons within the hidden layers resulted in increase of
performances. Correct classification rates of all lung sound signals ranges between
81 and 91%.
Comparison of ANN and GANN structures given in Table III indicates that
the classification performance of neural network structures optimized by the genetic
algorithm is more successful (83–93%) than ANN (81–91%).
Table IV. Different Types of Neural Networks Comparison on
Test (Data Set)
Neural network structure
Number of
neurons in
layersa
MSE
AUCb
ANN one hidden layer
ANN one hidden layer
ANN two hidden layer
ANN two hidden layer
GANN inputs not selected
GANN
129-6-3
129-12-3
129-6-8-3
129-12-12-3
129-10-12-3
61-7-11-3
0.0389
0.0132
0.0206
0.0087
0.0325
0.0059
0.932
0.936
0.941
0.946
0.936
0.949
a In
Test data set
the one hidden layer structures; the input layer–hidden layer–
output layer, and in the two hidden layers structures; the input
layer–first hidden layer–second hidden layer–output layer.
bAUC: Area under ROC curve.
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Table V. The Classification Results of Test Data Set
Test data set
neural network structure
Na
Wb
Cc
Ad
ANN one hidden layer
ANN one hidden layer
ANN two hidden layer
ANN two hidden layer
GANN inputs not selected
GANN
75
80
85
85
75
90
75
75
83.3
83.3
83.3
91.7
81.3
87.5
93.8
93.8
81.3
93.8
77.1
81.3
87.5
87.5
79.2
91.7
a N: Correct classification ratio of normal sounds.
bW: Correct classification ratio of wheeze sounds.
c C: Correct classification ratio of crackle sounds.
d A: Correct classification of all lung sounds.
When the GANN structures are examined within their structural designs, it
is seen that selection of inputs directly affects the performances. While, GANN
optimizes only network parameters without input selection yields 83.3% rated
classification performance, and GANN with two hidden layers yields 93.8% rated
classification performances, respectively.
DISCUSSION
There are two outcomes of this study that deserve some discussion. The first
one is successful classification of different neural structures of PSD values in three
categories as normal, wheeze and crackle, which have been obtained by the analysis
of the lung sound signals by Welch method.
The second area of discussion concerns the comparison of neural network
with other approaches to understanding the interactions among performance
improvement strategies. Both input selection and optimizing network parameter
options made the genetic algorithm perform better in classification of performance
over traditional MLP neural networks, as demonstrated in Tables III and V.
The accuracy of neural network may be highly sensitive to the nodal architectures, learning rates, and momentum parameters used to structure and train them.
We tried to improve the success of neural network by optimizing the neural networks with genetic algorithm. While the success rate of lung sounds classifications is
about 81–91% in the traditional neural networks, this rate increases up to 83–93%
when the neural network parameters have been optimized and the selection of
lung sounds data that is to be input of the network. Furthermore, reducing of the
lung sounds data to be applied at the input layer of neural network by selection of
genetic algorithm is resulted in reducing of numbers of neurons both at the input
layer and at the hidden layer(s). Consequently, lung sounds data classifications may
be done by less complicated neural network structures, and processing load and
time reduced.
In this study, changes of the performance by the effects of using single versus
two hidden layers are also observed. The increase in number of hidden layers
and the number of neurons within the hidden layers results in the increase of
Prediction of Lung Sounds
229
classification performances. On the other hand, more complicated neural network
structures and increased calculation times are the disadvantages of the system.
We have used a supervised network with back-propagation learning rule and
MLP architecture and genetic algorithm input selection. For neural network, the
genetic algorithm is popularly used to select neural network topology including optimizing relevant feature subsets, and determining the optimal number of hidden
layers and processing elements. The feature subsets, the number of hidden layers,
and the number of processing elements in hidden layers are the architectural factors
of neural network to be determined in advance for the modeling process of neural
network. However, determining these factors is still a part of the art. These factors
were usually determined by the trial and error approach and the subjectivity of designer. This may lead to a locally optimized solution because it cannot guarantee a
global optimum.
In this study, the neural network is trained by the data obtained from the lung
sound signals of only one group of subjects and it is used to estimate the prediction
of normal, wheeze or crackle. The performance of the system is highly dependent
on size of data and the selected parameters used in training. Neural network can
be trained according to the nature of the problem and medical prediction. The importance of lung sounds in the decision-making module of neural network differs in
various clinical conditions after repeated training of the system.
The testing performance of the GANN diagnostic system is found to be satisfactory. Future research may focus on the impact of network architecture on the
training and predictive performance of genetic algorithm-trained neural network.
Hybrid approaches deserve merit for future investigation as well.
CONCLUSION
The lung sounds are classified as containing crackles, wheezes or normal lung
sounds, using GANN. The approach is tested using a small set of real patient data,
which was also analysed by an expert physician. Spectral analysis of lung sounds
is performed by using Welch method. Once the neural network has learned the
PSD data, it is also found that the test results are classified properly. In order to
increase the prediction success rate of the neural network, hybrid GANN approach,
which selects inputs and parameters by genetic algorithm, is utilized in this study.
After the selection of 129 PSD data and application to neural network, an optimum
neural network structure is reached. The result obtained in this work appears to
indicate that a hybrid GANN approach provides a more accurate classification of
lung sounds than is possible with any neural network. However, more work needs
to be carried out to determine which network models provide the most accurate
classification when used in conjunction with each other.
REFERENCES
1. Sovijarvi, A. R. A., Vanderschoot, J., and Earis, J. E., Standardization of computerised respiratory
sound analysis. Eur. Respir. Rev. 10:77, 585, 2000.
230
Güler, Polat, and Ergün
2. Pasterkamp, H., Kraman, S. S., and Wodicka, G. R., Respiratory sounds: Advances beyond the
stethoscope. Am. J. Respir. Crit. Care. Med. 156:974–987, 1997.
3. Anderson, K., Aitken, S., Carter, R., MacLeod, J. E., and Moran, F., Variation of breath sound and
airway caliber induced by histamine challenge. Am. Rev. Respir. Dis. 141:1147–1150, 1990.
4. Gavriely, N., Palti, Y., Alroy, G., and Grotberg, J. B., Measurement and theory of wheezing breath
sounds. J. Appl. Physiol. 57:481–492, 1984.
5. Malmberg, L. P., Pesu, L., and Sovijärvi, A. R. A., Significant differences in flow standardised breath
sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy
lungs. Thorax 50:1285–1291, 1995.
6. Pasterkamp, H., Consunji-Araneta, R., Oh, Y., and Holbrow, J., Chest surface mapping of lung
sounds during methacholine challenge. Pediatric. Pulmonol. 2;21–30, 1997.
7. Schreur, H. J., Diamant, Z., Vanderschoot, J., Zwinderman, A. H., Dijkman, J. H., and Sterk,
P. J., Lung sounds during allergeninduced asthmatic responses in patients with asthma. Am. J.
Respir. Crit. Care. Med. 153:1474–1480, 1996.
8. Baughman, R. P., Shipley, R. T., Loudon, R. G., and Lower, E., Crackles in interstitial lung disease:
Comparison of sarcoidosis and fibrosing alveolitis. Chest 100:96–101, 1991.
9. Dalmasso, F., Guarene, M., Spagnolo, R., Benedetto, G., and Righini, G., A computer system for
timing and acoustical analysis of crackles: A study in cryptogenic fibrosing alveolitis. Bull. Eur.
Physio-Pathol. Respir. 20:139–144, 1984.
10. al Jarad, N., Strickland, B., Borhamley, G., Lock, S., Logan-Sinclair, R., and Rudd, R. M., Diagnosis
of asbestosis by a time expanded wave form analysis, auscultation and high resolution computed
tomography: A comparative study. Thorax 48;347–353, 1993.
11. Murphy, R. L., Gaensler, E. A., Holford, S. K., Del Bono, E. A., and Epler, G., Crackles in the early
detection of asbestosis. Am. Rev. Respir. Dis. 129:375–379, 1984.
12. Piirilä, P., Sovijärvi, A. R. A., Kaisla, T., Rajala, H. M., and Katila, T., Crackles in patients with
fibrosing alveolitis, bronchiectasis, COPD and heart failure. Chest 99:1076–1083, 1991.
13. Sovijärvi, A. R. A., Malmberg, L. P., Paajanen, E., Piirilä, P., Kallio, K., and Katila,
K., Averaged and time-gated spectral analysis of respiratory sounds. Repeatability of spectral parameters in healthy men and in patient with fibrosing alveolitis. Chest 109:1283–1290,
1996.
14. Sovijärvi, A. R. A., Piirilä, P., and Luukkonen, R., Separation of pulmonary disorders with twodimensional discriminant analysis of crackles. Clin. Physiol. 16:172–181, 1996.
15. Forgacs, P., Lung Sounds, Macmillan, New York, 1978.
16. Sovijärvi, A. R. A., Malmberg, L. P., Charbonneau, G., Vanderschoot, J., Dalmasso, F., Sacco, C.,
Rossi, M., and Earis, J. E., Characteristics of breath sounds and adventitious respiratory sounds.
Eur. Respir. Rev. 10:77,591–596, 2000.
17. Sovijarvi, A. R. A., Vandershoot, J., and Earis J. E., (eds.), Computerized Sound Analysis (CORSA):
Recommended Standards for Terms and Techniques-ERS Task Force Report, European Respiratory
Review, Vol. 10, ERS Journals Ltd, UK, 2000, Review No. 77.
18. Crick, F., The recent excitement about neural networks. Nature 337:129–132, 1989.
19. Tu, J., Advantages and disadvantages of using artificial neural networks versus logistic regression
for predicting medical outcomes. J. Clin. Epidemiol. 49:1225–1231, 1996.
20. Pesu, L., Helistö, P., Ademovic, E., Pesquet, J. C., Saarinen, A., Sovijärvi, A. R. A., Classification of
respiratory sounds based on wavelet packet decomposition and learning vector quantization. Technol. Health Care 6:65–74, 1998.
21. Oud, M., Lung function interpolation by means of neural-network-supported analysis of respiration
sounds. Med. Eng. Phys. 25:309–316, 2003.
22. Rietveld, S., Oud, M., and Dooijes, E. H., Classification of asthmatic breath sounds: Preliminary
results of the classifying capacity of human examiners versus artificial neural networks. Comput.
Biomed. Res. 32:440–448, 1992.
23. Wilks, P. A. D., and English, M. J., A system for rapid identification of respiratory abnormalities
using a neural network. Med. Eng. Phys. 17:551–555, 1995.
24. Malmberg, L., Kallio, K., Haltsonen, S., Katila, T., Sovijarvi, A., Classification of lung sounds in
patients with asthma, emphysema, fibrosing alveolitis and healthy lungs by using self-organizing
maps. Clin. Physiol. 16:115–129, 1996.
25. Waitman, L. R., Clarkson, K. P., Barwise, J. A., and King, P. H., Representation and classification of
breath sounds recorded in an intensive care setting using neural networks. J. Clin. Monit. Comput.
16:95–105, 2005.
26. Yao, X., Evolving artificial neural networks. IEEE Trans. Neural Netw. 87:1423–1447, 1999.
27. Castillo, P. A., Merelo, J. J., Prieto, A., Rivas, V., and Romero, G., G-prop: Global optimization of
multilayer perceptrons using Gas. Neurocompution 35:149–163, 2000.
Prediction of Lung Sounds
231
28. Bath, P. A., Pendleton, N., Morgan, K., Clague, J. E., Horan, M. A., and Lucas, S. B., New approach
to risk determination: Development of risk profile for new falls among community-dwelling older
people by use of a genetic algorithm neural network (GANN). J. Gerontol. A Biol. Sci. Med. Sci.
55:M17–M21, 2000.
29. Narayanan, M. N., and Lucas, S. B., A genetic algorithm to improve a neural network to predict a
patient’s response to warfarin. Methods Inf. Med. 32:55–58, 1993.
30. Potter, S. R., Miller, M. C., and Mangold, L. A., Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy., Urology. 54:791–795, 1999.
31. Dybowski, R., Weller, P., Chang, R., and Gant, V., Prediction of outcome in critically ill patients
using artificial neural network synthesised by genetic algorithm. Lancet 347:1146–1150, 1997.
32. Heckerling, P. S., Gerber, B. S., Tape, T. G., and Wigton, R. S., Use of genetic algorithm for neural
network to predict community-acquired pneumonia. Artif. Intell. Med. 30:71–84, 2004.
33. Güler, I., and Übeyli, E. D., Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Comput. Biol. Med. 33:333–343, 2003.