International Journal of Computational Intelligence and Bioinformatics
Volume 3, Numbers 1, January-June 2011, pp. 43– 70
© International Science Press
ISSN: 2229-3108
A REVIEW AND COMPARATIVE ANALYSIS OF TECHNIQUES FOR DIAGNOSIS
OF PULMONARY DISEASES
*
N.K. Gautam and S.B. Pokle
Abstract: According to the statistics of world health organization (WHO) lung diseases
are one of the major causes of deaths worldwide. To control these casualties due to
pulmonary diseases, the preliminary diagnosis should be more effective and accurate.
The common monitoring and diagnostic techniques for the pulmonary diseases are
lung auscultation, spirometry, roentgenography and bronchoscopy. In this paper these
techniques are discussed with their brief principle of operation indicating their
advantages and limitations. Significant studies and research work for these techniques
have been briefly reviewed so that they can be compared. Various papers and articles
from national and international journals, conferences, books, and websites were used
as the resource for this paper. A comparative analysis has been carried out based on
this study to find out simple yet accurate diagnosis technique for pulmonary disease
which will also be helpful in home and long term monitoring. The possibility of distant
monitoring and diagnosis is also investigated. The result of this analysis shows that the
lung auscultation technique in phonopulmonography configuration is a better option
for the diagnosis of the pulmonary disease. This study will invite and encourage more
research in the field of detection and diagnosis of pulmonary diseases.
Keywords: Pulmonary disease, lung auscultation, spirometry, roentgenography,
bronchoscopy, phonopulmonography.
1. INTRODUCTION
World Health Organization has begun compiling and presenting the statistics known as
‘Burden of Diseases’ since 1990. From this data, it has been found that the human lung
infections, which are responsible for lung diseases (pulmonary diseases), have continuously
resulted in more burden than any other diseases [1, 2]. To detect the pulmonary diseases
*1
2
Electronics Engineering Department, M.I.E.T., Gondia, (M.S.), India, E-mail: nageshk gautam@
gmail.com.
Electronics Engineering Department, R.K.N.C.E., Nagpur, (M.S.), India, E-mail: sanjaypokle@
yahoo.in.
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International Journal of Computational Intelligence and Bioinformatics
various techniques such as lung auscultation [3, 4, 5], spirometry [6], roentgenography [7],
bronchoscopy [8] etc., are used. The purpose of this paper is to study the common techniques
for the diagnosis of pulmonary diseases, their advantages and the limitations so that a
conclusion can be drawn to find out a non-invasive, cost effective, simple to use and
automated technique which can also be used for home monitoring, long term and distant
monitoring. Therefore the common techniques for the diagnosis of pulmonary diseases
with their advantages and the limitations are briefly discussed here. A comparative analysis
amongst these techniques has been carried out to find out a suitable technique for the
diagnosis of pulmonary diseases.
The paper is organized as follows. Next section gives the brief working principle and
review of major research work carried out for the common techniques for the detection and
diagnosis of the respiratory system [9] diseases with their advantages and limitations. A
comparative analysis based on some vital parameters related to these techniques has been
carried out and its result is summarized in section 3. Finally the conclusion from this analysis
has been drawn in section 4 to signal out the best possible technique for the detection and
diagnosis of the pulmonary diseases.
2. MONITORING AND DIAGNOSTIC TECHNIQUES
The common monitoring and diagnostic techniques for the pulmonary diseases are
spirometry, roentgenography, bronchoscopy and lung auscultation. The brief working
principle of these techniques has been explained in this section. Further these techniques
are reviewed and their advantages and limitations are briefly explained here.
A. Spirometry
Spirometry is a medical screening trial that measures various features of breathing and lung
function. It is performed by a spirometer. Spirogram is a tracing or recording of the
information obtained from the test. There are two types of spirometers:
(1) Volume spirometers that records the amount of air exhaled or inhaled within a certain
time and
(2) Flow spirometers that measures how fast the air flows in or out as the volume of air
inhaled or exhaled increases. A typical example of a spirogram obtained from the
spirometry test is shown in Figure. 1.
The general spirometric test requires that the subject exhale fully after taking in a full,
deep breath. The subject’s effort is called the forced expiratory maneuver. The parameters
recorded in the course of spirometry include forced vital capacity (FVC) and forced
expiratory volume in one second (FEV1), from which the FEV1/FVC ratio is derived.
These are key measures for diagnostic and treatment decisions. The indices derived from
this forced exhaled maneuver have become the most accurate and reliable way of supporting
a diagnosis of chronic obstructive pulmonary disease (COPD). When these values are
compared with predicted normal values determined on the basis of age [10], height, sex,
and ethnicity, a measure of the severity of airway obstruction can be determined [6].
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
45
maximal inspiration
Volume, liters
INSPIRATORY RESERVE VOLUME
INSPIRATORY
CAPACITY
VITAL CAPACITY
TIDAL
VOLUME
EXPIRATORY RESERVE VOLUME
maximal expiration
time, s
Figure1: A Typical Spirogram
Spirometry procedure using flow spirometers gives the graph between the air flows
and the volume of air inhaled or exhaled. Typical graph obtained from spirometry test for
normal case and for pathological case as compared to normal case is shown in Figure 2.
(a)
(b)
Figure 2: (a) Normal Spirogram with Good Efforts. (b) Case with COPD with Low Peak Flow and
Delayed Expiration, Volume is Low
Volume spirometers gives the graph between the amount of air exhaled or inhaled and
time. Typical graph of volume spirometer for normal case and pathological case as compared
to normal case is shown in Figure 3. Normal case is shown by dotted graph whereas abnormal
case is shown by continues graph.
International Journal of Computational Intelligence and Bioinformatics
46
(a)
(b)
Figure 3: (a) Volume/Time Graph for Normal Case. (b) Volume/Time Graphs for Pathological Case (6).
Bellows or rolling seal spirometers are large and not very portable, and are used
predominantly in lung function laboratories. Electronic desktop spirometers are compact,
portable, and usually quick and easy to use. They have a real-time visual display and paper
or computer printout. Small, in expensive hand-held spirometers provide a numerical record
of blows but no printout. It may be necessary to look up predicted values in tables, but
some include these in their built-in software. Table 1 explains different stages of COPD
and typical spirometric data associated with these stages.
TABLE 1
Typical Spirometric Criteria for COPD Severity [11]
Mild COPD
• FEV1/FVC< 0.7
• FEV1 ≥ 80% predicted
At this juncture, the patient may not
be knowing that their lung function is
abnormal.
Moderate COPD
• FEV1/FVC < 0.7
• 50% ≤ FEV1 < 80% predicted
Indications usually develop at this stage,
with shortness of breath typically on
exertion.
Severe COPD
• FEV1/FVC < 0.7
• 30% ≤ FEV1 < 50% predicted
Shortness of breath worsens at this
stage and often restricts patient’s day
to day activities.
Very severe COPD
• FEV1/FVC < 0.7
• FEV1 < 30% predicted or
FEV1 < 50% predicted plus
chronic respiratory failure
At this stage, quality of life is appreciably impaired and exacerbations may
be life threatening.
Lot of efforts has been taken for improvement of spirometer and research work is
going on over spirometry. Some significant works has been discussed here. Steltner H. et
al, (2000), have suggested the use of multi-exponential models for describing the exhaled
volume as a function of time in spirometry particularly for the patients with respiratory
complaints and facing difficulties in completing the forced expiration maneuver [12]. P.L.
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
47
Enright et al., (2005), have observed that the office spirometry in primary care setting has
the utility in the detection and supervision of asthma and chronic obstructive pulmonary
disease (COPD). They also observed that the ratio FVC1/VC × 100% changes with age [13].
Kalicka, R. et al, (2007), have developed a new model of spirometry based on spirometry
measurements to distinguish between healthy and diseased subjects [14]. In their paper,
Carta R.et al, (2007), presented the design, implementation and measurements of a spirometer
based on differential pressure sensing using the Venturi tube principle with data acquisition
software and user interface [15]. Lay-Ekuakille A. et al, (2008), have developed a complete
spirometric instrumentation to produce spirometric parameters of a probable pathologic
curve and to predict pathology using a genetic algorithm [16]. Alejos-Palomares R. et al.,
(2008), have designed and developed a low cost, portable, easy to handle and computerized
digital spirometer as a basic tool for evaluation of the respiratory capacity and to display
the volume-time and flow-volume graphs [17]. In their work, Jafari S. et al, (2010) have
designed a system for detecting normal and abnormal pulmonary system functions using
spirometry data and multilayer perceptron neural networks [18]. D Cramer et al., (1984),
has concluded that the body temperature pressure saturation (BTPS) conversion is valid
and necessary for measurements made in routine spirometry [19]. Finkelstein, S.M. et al.,
(1993), used a paperless electronic spirometer system in a home monitoring program with
distant monitoring facility for subjects who have undergone lung transplantation to compare
the reliability of forced vital capacity (FVC) maneuvers recorded at home with similar
measures recorded in the clinic pulmonary function laboratory and found that home
spirometry can provide quality data for a home monitoring program after lung transplantation
[20]. Finkelstein, S.M. et al, (1997) have suggested that home monitoring with the help of
a paperless, electronic spirometer can provide early and consistent indication of pulmonary
decline prior to chronic rejection in the patients with transplanted lungs, and should be
considered as a future component of post-transplant follow-up care [21]. Jannett T.C. et al,
(2002) have suggested an intelligent telemedicine system for remote monitoring of lung
function using a home spirometer. In this system parameters useful in assessing lung function
were transmitted over a telephone line to a database residing on an institutional computer
system [22].
Advantages of Spirometry
Pulmonary function tests can be performed most accurately by the spirometry. Following
are the advantages of spirometry test.
• It is a safe diagnostic technique for pulmonary disease.
• Spirometry is a non-invasive technique except a mouth piece for blowing air.
• Electronic desktop spirometers and hand-held spirometers are compact, portable and
easy to handle.
• Home and distant monitoring and diagnosis are possible by spirometry.
• It is a very good confirmation test for chronic obstructive pulmonary disease after its
initial diagnosis.
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International Journal of Computational Intelligence and Bioinformatics
Limitations of Spirometry
Although spirometry can provide useful diagnostic and screening information for the
diagnosis of pulmonary diseases, it has a few limitations which are listed here.
• Initial setup is required to conduct spirometry.
• The spirometry setup has nasal clip to airtight the nasal path of air and a mouthpiece
to blow air. Hence the procedure of spirometry test is neither comfortable nor
convenient for the patient.
• Long term monitoring is not possible with spirometer.
• Spirometry requires a clinical environment or the supervision of the experienced
physician for interpreting the results.
• Spirometry cannot be the sole screening tool of a respiratory disease.
• Spirometry test results can show restrictive or obstructive disease patterns, but they
are not disease-specific. For example, a person’s spirogram may show a low FEV 1,
but a physician may not be able to determine whether the cause is from asthma,
emphysema, or some other obstructive disease. Additional information, such as a
physical examination, chest x-rays, and health and occupational histories, are needed
to make a diagnosis.
• Spirometry often can detect obstructive diseases in their early stages, but for some of
the restrictive diseases, it may not be sensitive enough to show abnormalities before
extensive, and in some cases, irreversible damage has been done. For example, signs
of silicosis and coal worker’s pneumoconiosis may be found on chest x-rays while
spirometry results are still normal.
• The skill of the operator which results in human error factor plays an important role in
spirometry and is an important limitation of it.
• The measurement of inhaled and exhaled air should be accurate during sprometry for
correct diagnosis. Paul L Enright, (2003), has explained some factors responsible for
error and guidelines to minimize them [23].
• Air leak from mouth while blowing during spirometry introduces an error in the
measurement.
• In case of children, elders where the occurrence of Chronic obstructive pulmonary
disease (COPD) in subjects aged over 65 years is 6–15% in women and 7–34% in
men, and the occurrence of asthma is 7% and 3% respectively [24] and in case of
mentally challenged people, this test can be difficult to perform. In a study by Licia
Pezzoli et al., (2003), around 18.2% old age people were unable to perform spirometry
test properly [25].
• The American Thoracic Society recommends that spirometry should not be performed
if the patient has experienced a myocardial infarction in the last four weeks. Test
should be avoided in patients with recent abdominal surgery [26].
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
49
• Enthusiastic training for correct breathing maneuvers for spirometry test remains vital
to reduce the risk of false diagnosis and it is significant in the primary care setting.
• In spirometry there is always a possibility of error in obtained data if ambient
temperature is different from body temperature [19].
• The reference values of the spirometry test for normal and pathological case depends
highly on the age of the patient [11, 25, 27].
B. Roentgenography
The roentgenograph is named after German physicist Wilhelm Conrad Rontgen, who
discovered X-ray in 1895. Roentgenograph is the photograph of internal organs of the body.
It is obtained by passing a small dose of ionizing radiation through the body to create a
shaded image on specifically sensitized film. It is a non-invasive checkup test that helps
physicians in diagnosing. In the diagnosis and treatment of lung related diseases, the
roentgenogram is generally used to see actual physical situation of the lung. It is predominantly valuable in emergency diagnosis and treatment [28].
Important research work in roentgenography is reviewed here as follows. Chien Y.P.
et al, (1974) used a class of pictures of medical importance, namely, chest X-ray images, to
test the proposed pre-processing and feature-extraction technique. The classification results
presented in this paper show the feasibility of the proposed pictorial pattern recognition
system in effectively screening out the abnormal pictures without human intervention [29].
In their work, Devan K.S. et al, (2005), have developed a rule-based expert system with an
embedded imaging module to help physicians for early detection of pulmonary diseases
from chest x-ray [30]. A fast piecewise heuristic follower of the boundaries of difficult-tonotice non overlapping blobs in digitized X-ray images is described by Sankar P.V. et al,
(1982), to detect the boundaries of lung nodules with good fidelity of the detected boundaries
[31]. M Bombino et al., (1991), have concluded that as compared to computed tomography
consistent reading of portable chest roentgenograms by proposed scoring tables is an
important tool in approximating the level of pulmonary edema in a patient with adult
respiratory distress syndrome [32]. Patil S.A. et al., (2009), have presented a computer
algorithm for nodule detection in chest radiographs for lung cancer and TB [33]. Peichun
Yu et al., (2009) have suggested a texture analysis method on digital chest radiograph to
distinguish pneumoconiosis chest from normal chest [34]. An automatic system was
implemented by Lampotang S. et al., (1998), for synchronizing beam exposure with peak
lung inflation during chest radiography to enhance clinical diagnostic data in a chest
radiograph [35]. The spectral property of singular value decomposition is used by WeiChunLin et al., (2010), for enhanced interpretability of information in low contrast images
for human viewers while the noise and blurring were reduced [36]. Seunghwan Kim et al.,
(2000), have suggested the nonlinear warping to eliminate the image of rib from chest
x-ray images [37]. It is very important to locate and recognize the lung region accurately in
chest X-ray images in clinical application and research. Different methods for the segmentation of lung in X ray images have been suggested by many researchers [38-40]. Iakovidis
International Journal of Computational Intelligence and Bioinformatics
50
D.K.(2008), proposed a innovative methodology that copes with lung field detection in
both stationary and portable chest radiographs by combining statistical grey-level intensity
information and directional edge maps particularly for patients with lung consolidation [41].
In their work Jeanne V. et al., (2009), have proposed X ray image classification and retrieval
[42]. The compact X-ray tube design utilizing carbon nanotubes cold cathodes with brazing
technique developed by Dae-Jun Kim et al., (2010), may be a significant advance in X-ray
technology development and could lead to portable and miniature X-ray sources for medical
and industrial applications [43]. Some roentgenographs are given here for illustration. The
typical roentgenographs for normal and diseased cases are shown in Figure 4.
(a)
(b)
Figure 4: (a) Frontal Chest Roentgenograph of a Normal Male Patient. (b) Posteroanterior Chest
Roentgenograph with Pneumonia.
Advantages of Roentgenography
The advantages of the roentgenography are as follows.
• It gives directly the internal image of the chest.
• Actual image of the lung states the physical condition of it.
• It is very handy in emergency diagnosis and treatment.
Limitations of Roentgenography
The roentgenography has many drawbacks and limitations.
• It is a complex diagnostic technique.
• Initial setup is required for roentgenography.
• Roentgenography is a non-invasive technique but X-ray radiations passes through the
body during the process. Frequent exposure to this radiation during roentgenography
is harmful for the body. There is always a probability of cancer from intense exposure
to radiation.
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
51
• This exposure is very harmful for the children and particularly for the foetus. Therefore
pregnant women should avoid roentgenography.
• Instrument for the roentgenography is neither compact nor portable.
• The diagnostic value of a chest radiograph is improved if chest movement is minimal
and X-ray is taken at or near peak lung inflation.
• Image of rib cage hides some portion of lung which cannot be seen in roentgenograph.
• Roentgenogram is mostly used during emergency for the diagnosis to see the actual
physical condition like liquid deposition in lung, shrinking of lung, foreign body in
lung etc.
• Home monitoring, long term monitoring and distant monitoring and diagnosis cannot
be done by roentgenography.
• The level of human error factor during roentgenography depends upon the skill of the
operator who is performing it.
• It requires a clinical environment and the supervision of the experienced physician
for interpreting the results.
• There is a possibility of error in the interpretation of chest roentgenograms by a
physician and it depends on his or her knowledge and experience. Moreover it has
been demonstrated that physicians are likely to disagree with each other over the
interpretation of roentgenogram [44].
• In case of children and mentally challenged people, this test can be difficult to perform.
Roentgenography gives the image of the lung. Thus there is lot of scope for image
processing and synthesis techniques to minimize the limitations of this technique.
C. Bronchoscopy
Bronchoscopy is a process that enables the physician to inspect the major air passages of
the respiratory system and allows him to evaluate the lungs. Small samples of tissue or
fluid can also be taken from respiratory system if required. It is an invasive process and is
used to view the nasal passages, pharynx, larynx, vocal cords, and bronchial tree of respiratory
system. In addition to the diagnosis, it is used for treatment of lung disorders. Bronchoscopy
can be performed by either rigid or flexible bronchoscope and thus known as rigid bronchoscopy or flexible/fibereoptic bronchoscopy respectively. In their review Andrew R. Haas et
al., (2010), have observed that the flexible bronchoscopy has given improved access to the
trachiobronchial tree as compared to the rigid bronchoscopy and the emerging technologies
have significantly enhanced the diagnostic capabilities of flexible bronchoscopy [45]. In
rigid bronchoscopy the bronchoscope is inserted through the mouth of the patient while in
flexible bronchoscopy the bronchoscope can be inserted through nose or mouth. The advantages of flexible bronchoscopy over rigid bronchoscopy is that it can be done under topical
anaesthesia, have less chances of trauma and have ability to visualize and sample more
peripheral bronchial pathologies than with rigid bronchoscope. Though the diagnostic
International Journal of Computational Intelligence and Bioinformatics
52
capabilities with flexible bronchoscopy have improved, but the view obtained by it is inferior
to that seen through rigid bronchoscope [46]. Some bronchoscopy images are shown here
for illustration. Figure 5 shows the typical bronchoscopy images for normal and abnormal
cases.
(a)
(b)
Figure 5: (a) Normal Case. (b) Abnormal Cases
Usually the procedure is performed after patient is sedated with nose or mouth numbed.
A small needle is inserted into a vein so that additional medications can be given. Patient
will be connected to a heart and blood pressure monitor and will be given extra oxygen
through the nose during the exam. If needed, additional sedation may be given through the
needle in the vein. Then physician inserts a bronchoscope through the nose or mouth and
into the windpipe of the patient. A small canal in the device allows tissue and fluid samples
to be collected, if required.
Research work has been carried out and is going on to improve the procedure of
brochoscopy. Prominent works amongst them have been reviewed here. Mellor, J.R.et al,
(1995), presented an image processing technique for reconstructing the bronchial tree from
continuous volume scanning data, and representing it as an image that closely resembles
bronchoscopy [47]. In their work Bricault I. et al., (1998), has presented a procedure for
computer assisted transbronchial biopsy [48]. Oliveira P.P.D.M. Jr. et al., (2000), used
computer graphics techniques to develop a virtual endoscopic system for non-invasive
bronchoscopy for the cases where biopsy is not required [49]. In their work, Kourelea, S. et
al, (2000), have established the utility of virtual bronchoscopy over fiberoptic bronchoscopy
for the patients with hemoptysis [50]. The work of Mori K. et al, (2000), describes a method
for the automated anatomical labeling of the bronchial branch, necessary for realizing a
computer-aided diagnosis system, extracted from a three-dimensional (3-D) chest X-ray
CT image and its relevance to a virtual bronchoscopy system [51]. Kiraly, A.P. et al.,
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
53
(2004), developed a lung-cancer estimation system, which facilitates virtual bronchoscopic
three dimensional multidetector computed-tomography image analysis and track on image
guided bronchoscopy [52]. Cebral J.R. et al, (2004), have suggested that virtual bronchoscopy
can be used to execute aerodynamic computation in anatomically realistic models by computing the pressure and flow patterns in a human airway noninvasively to determine region of
a stenosis [53]. Chung A.J.et al, (2006), has offered an image-based technique for virtual
bronchoscope with photo-realistic rendering. The proposed method is based on recovering
bidirectional reflectance distribution function (BRDF) parameters in an surroundings where
the option of viewing position, directions, and illumination circumstances are limited. [54].
Sang Joon Park et al, (2008), established a new process for thickness-mapped virtual
bronchoscopy with an analysis of airway information by a developed algorithm. It is indicated
in this paper that this approach is suitable, precise, automatic and well visual in approximating
airway wall thickness [55]. Wilson S. et al., (2005), suggested a procedure that deals with
the problem of measuring and navigating through the airway using a bronchoscope during
bronchoscopy, without the need of any additional data, localization systems or other external
components. It provides a range of numerical measurements and inferences to physicians
in real time, using standard computer hardware [56]. Rai L. et al., (2006), have described a
fast robust method that provides three dimensional computed tomography image based
image guidance during live bronchoscopy to improve the success rate in the assessment of
lung cancer [57]. Negahdar M. et al, (2006), have developed an automated path planning
method appropriate for quantitative airway analysis and proper virtual navigation, which
allows virtual bronchoscopic 3D multidetector computed tomography image analysis and
follow on image-guided bronchoscopy [58]. Bountris P. et al, (2009), presented an intelligent
computing system based on combined texture features, feature selection methods and
classification models, for improved classification of suspicious areas of the bronchial mucosa,
in order to decrease the rate of false positive findings, to increase the specificity and sensitivity
of autofluorescence bronchoscopy and enhance the overall diagnostic value of the autofluorescence bronchoscopy method [59]. Graham M.W. et al, (2010), have presented a robust
method of segmentation of the peripheral airway tree from a 3-D multidetector computed
tomography chest scan for planning of peripheral bronchoscopy [60]. Xu Di et al., (2010),
have proposed a feature-based 2D/3D registration method for the image fusion between the
datasets of the two imaging modalities to improve X-ray guided bronchoscopy of peripheral
pulmonary lesions, airways and nodules [61].
Advantages of Bronchoscopy
Bronchoscopy is one of the important diagnosis and treatment procedure for diseases of the
respiratory system. The advantages of bronchoscopy are listed below.
• Internal images of the respiratory system are obtained with the help of bronchoscopy.
These pictures can be recorded for further analysis.
• An improved technique of virtual bronchoscopy is non-invasive.
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• For diagnosis of certain disorders, bronchoscopy is used to collect tissue and fluid
samples from respiratory system.
• In addition to diagnosis, bronchoscopy is also used for the treatment of certain disorders
of respiratory system.
Limitations of Bronchoscopy
Bronchoscopy is a complicated diagnostic and treatment procedure for the diseases of the
respiratory system. It has following limitations.
• It is a complex diagnostic technique and requires initial setup.
• Bronchoscopy is an invasive technique and hence complications due to it may occur.
• Standard bronchoscopy provides live video information of inner side of the lung
airways but it does not give any information of the outer side of the airways.
• View obtained during bronchoscopy can be easily obscured which requires removal
of the bronchoscope for cleansing and reinsertion [46].
• Bronchoscopy is performed in a clinical environment by a specially trained physician
bronchoscopist for interpreting the results and is assisted by a specially trained healthcare professional to avoid any serious consequence due to human errror [62, 63].
• The adverse effects of flexible bronchoscopy may be attributable to the sedation, the
local anaesthesia or the procedure. They may include collapsed lung, air leak from
lung, bleeding, an allergic reaction, hoarseness, and fever. Mortality from the procedure
is about 0.02%. [63, 64]. Cardiovascular complications are well known to occur as a
result of premedications, topical anaesthetics and the hypoxemia engendered during
the procedures [65-69].
• Home and long term monitoring is not possible by bronchoscopy and distant monitoring
and diagnosis cannot be done by this technique.
• Infection in respiratory system can occur by the bronchoscope. Hence it is essential to
clean and disinfect all instruments before use.
• Entire lung particularly the peripheral airways cannot be examined using this technique.
• Even after the bronchoscopy test, the patient is being kept in observation for any chest
pain, difficulty in breathing, or more flecks of blood in the phlegm. It is normal to
cough up a small amount of blood for 1 to 2 days after the procedure. Blood pressure,
pulse, and breathing rate will be checked and a chest x-ray may be taken prior to the
discharge. One cannot eat or drink anything before 6 hours and 2 hours after the
bronchoscopy procedure.
• For children and mentally challenged persons bronchoscopy is not convenient and
can be difficult to perform.
Bronchoscopy gives the images of the respiratory system. Thus there is lot of scope for
image processing techniques which may reduce some limitations of this technique.
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
55
D. Lung Auscultation Technique
In this technique lung sounds are heard using a stethoscope and are used for the prediction
of the lung health. Use of a conventional stethoscope to listen lung sound makes lung
auscultation technique simple, easy to use and most popular non-invasive method for
diagnosis. Presently conventional stethoscope is being used by medical professionals for
preliminary diagnosis of pulmonary diseases. It is a very common and handy tool [3, 4, 5].
The sound heard with the help of stethoscope depends on three important factors. They are
vibration present at the chest wall, the perception of sound by the human ear (psychoacoustics) and the acoustics of the stethoscope itself. The perception of the sound is a
composite process but it basically consists of three factors. They are the ability to focus on
particular frequencies, the presence of different higher frequencies which gives the
auscultated respiratory sounds their typical nature, and the fact that low frequency
components of a sound may cover components of a higher frequency. The critical factor in
determining the usefulness of the acoustic stethoscope is its reliance on the expertise and
experience of the physician to distinguish between normal and pathological case [70].
The invention of the basic stethoscope was done by Laennec RTH when he came up
with a hollow tube of wood, 3.5cm in diameter and 25cm long in 1818 [3,71]. Rober Bowles
invented the diaphragm of the stethoscope. Later on Sprague combined the bell with the
diaphragm, in one chest piece, in 1926. Aubrey Leatham, at St. George’s Hospital, in London,
designed a binaural stethoscope with a bell which had two components, big and small. In
1961 Littmann redesigned the stethoscope devised by Sprague. It was easier to use and
much lighter. This is the conventional mechanical stethoscope being used nowadays by
most of the physicians [72]. Now electronic stethoscopes are available that records the
breath sound [73, 74]. But some limitations of electronic stethoscope are their expensive
retail price, the common problem of background noise, heart sound interference and most
of them were unable to interface with the computers earlier. With the recent development
in technology, these shortcomings are getting minimized. Now some electronic stethoscopes
are available with ambient noise filtering [75, 76]. Woywodt A. et al., (2004), developed an
electronic stethoscope which can be interfaced to a laptop. This setup was used for the
teaching of cardiac auscultation [77]. Here we would like to define the technique ‘of listening
respiratory sound using an electronic stethoscope for analysis and diagnosis of diseases
related to respiration system as ‘Phonopulmonography’ (PPG) to differentiate it from
conventional lung auscultation technique. In the previous work of one of the author of this
paper it has been concluded that PPG technique is the best technique for diagnosis of
pulmonary disease [78].
Lot of research work has been done since invention of stethoscope and is still going on
to improve lung auscultation technique to make it the best technique for the diagnosis of
pulmonary diseases. Some of these works has been revived here. Raymond L Murphy,
(1981), has suggested a multidisciplinary effort to understand the mechanism of generation
of normal breath sounds and adventitious sounds to devise an improved non-invasive
diagnostic auscultation tool [79]. Giuliana Benedetto et al., (1988), have used three micro-
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International Journal of Computational Intelligence and Bioinformatics
phones simultaneously, arranged in three different subsequent configurations to show that
the acoustical characteristics of adventitious lung sounds change significantly when the
location of auscultation is changed [80]. In his review work M.J. Mussell, (1992) have
highlighted the need of standardization in recording and analysis of respiratory sounds so
that different works in this field can be compared [81]. Bettencourt PE et al., (1994), have
applied the techniques of lung sound mapping and time-expanded waveform analysis to
investigate the usefulness of chest auscultation in pulmonary disease [82]. In their review,
P. Piirila et al., (1995), discussed about the origin, auscultation, recording and analysis of
crackles from the point of view of clinical significance of crackles for detecting the pulmonary
disorders [83]. Martin Kompis et al., (1995), have studied the similarity of breath sounds
recorded simultaneously at different sites on the chest as a function of frequency and intermicrophone distance [84]. Volker Gross et al., (2000), have examined the relationship
between lung sounds, age, and gender and found that these changes were too small to be
clinically relevant and hence there is no need to consider a patient’s age during automated
lung auscultation [85]. Yasemin P. Kahya et al., (2003), have compared the performances
of k-NN (k-nearest neighbours) classifiers with different feature sets evaluated from
respiratory sound data obtained from healthy and pathological subjects [86]. Ipek Sen et
al., (2003), has designed and implemented five channel analog amplifier and filter stage for
acquisition of pulmonary sound signal [87]. Huseyin Polat et al., (2004), have designed a
computer based measurement and analysis system for pulmonary auscultation sounds using
the software package DaisyLAB [88]. Anderson et al., (2001), have suggested that, even
without specialized equipment, mobile phones can noninvasively monitor tracheal breath
sounds in patients who suffer exercise induced asthma and other chronic airway diseases
[89]. In their work Fragasso G et al., (2003), have established that an internet based system
permits even delicate and sensitive diagnostic processes such as heart and lung auscultation
to be conducted remotely [90]. Inan Guler et al., (2005), have presented a study for neural
networks and genetic algorithm approach intended to aid in lung sound classification and it
was used to predict the presence or absence of adventitious sounds [91]. In their work
Yasemin P. Kahya et al., (2006) used different feature sets in conjunction with k-NN and
artificial neural network classifiers to address the classification problem of respiratory sound
signals [92]. In their work Styliani A. Taplidou et al., (2006), have captured and analyzed
the nonlinear characteristics of asthmatic wheezes, reflected in the quadrature phase coupling
of their harmonics, as they evolved over time within the breathing cycle [93]. Saiful Huq et
al., (2007), have investigated the difference between the average power and log-variance of
the band-pass filtered tracheal breath sound of inspiratory and expiratory phase [94]. GwoChing Chang et al., (2008), have investigated the effect of various noise conditions over
lung sound feature representations and found that bispectrum diagonal slices was more
immune to noise [95]. In his study, Zumray Dokur, (2008), suggested a novel incremental
supervised neural network for classification of nine different respiratory sounds and the
classification performances of this network has been compared with that of multi-layer
perceptron and grow and learn network [96]. Claudia C. Ceresa et al., (2008), have established
that the auscultation technique is the most suitable procedure in the diagnosis of the
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
57
respiratory disease [97]. N. Gavriely et al., (1994), have examined and established that if
lung sounds analysis is considered with spirometry then it enhances the sensitivity of
detection of pulmonary diseases by objective tests, and gives an early sign of lung disease
that was not detected by spirometry alone [98]. A B Bohadana et al, (1995), have shown
that during bronchial provocation testing the precise classification of wheeze can be useful
in avoiding or shortening the test due to deduced relationship between wheeze and airways
hyper responsiveness [99]. D Faistauer et al, (2005), have developed a model for normal
lung sound generation based on a discretization of air flow in particle-like elements to
explain the origin of the lung sound [100]. H. Pasterkamp et al., (1999), have studied and
analyzed the effect of ambient sounds, generated during breathing that reaches a sensor at
the chest surface by the transmission from mouth and nose through air in the room, rather
than through the airways, lungs and chest wall [101].
Work also has been carried out specifically to minimize or cancel the heart sound from
lung sound signal. Recursive least squares adaptive noise cancellation and wavelet transform
based adaptive noise cancellation have been applied and compared by January Gnitecki et
al., (2005), for heart sound reduction from lung sounds recordings [102]. Jen-Chien et al.,
(2006), used fast independent component analysis algorithm to separate heart sound and
lung sound to reduce human factor in telemedicine and home care system [103]. M.T.
Pourazad et al., (2006), have proposed a method of heart sound cancellation from lung
sound recordings using time-frequency filtering and found that it successfully removes
heart sound from lung sound signals while preserving the original fundamental components
of the lung sounds [104]. Daniel Flores-Tapia et al., (2007), have presented a novel method
for heart sound cancellation from lung sound recordings using multiscale product of the
wavelet coefficients of original signal to detect the segments of heart sound in it [105].
Thato Tsalaile et al., (2007), have demonstrated a technique for separating heart sound
from lung sound signal using adaptive line enhancement [106]. In their study, F. Jin et al.,
(2008), have worked on the problem of heart sounds localization from single channel
respiratory sounds recordings by applying wavelet-based localization scheme [107]. T E
Ayoob Khan et al., (2010), in their work have implemented a virtual instrument for heart
sound cancellation from lung sound recordings using the advanced signal processing toolkit
of LabVIEW 8.2 [108]. Yuan-Hsiang Lin et al., (2010), developed a digital auscultation
system with a USB port for data transmission and a PC-based graphic user interface for
waveform display and data recording. This system would be useful for aiding the auscultation
diagnosis and telemedicine applications [109]. Mayorga P. et al., (2010), have proposed
acoustic evaluation methodology based on the Gaussian Mixed Models to assist in broader
analysis, identification, and diagnosis of asthma [110]. Zhen Wang et al, (2009), used a
non-invasive computer aided acoustic based imaging procedure to establish that the patients
with acute dyspnea due to obstructive airway disease had distinguishable features in their
respiratory sound [111]. A fully automated method was developed by Yadollahi A. et al.,
(2010), for the detection and monitoring of obstructive sleep apnoea that uses tracheal
breathing sound along with blood oxygen saturation level [112]. Alice Jones et al., (1998),
have compared three type of stethoscope chest pieces and demonstrated that their acoustic
International Journal of Computational Intelligence and Bioinformatics
58
performance is different, with the Littmann chest piece showing the maximum gain in
intensity and seems to be most suitable for lung sound auscultation [113]. Y. J. Jeon et al.,
(1999), have developed a new auscultation system for the recording of the breath sound
from the trachea. They compared the parameters extracted from breath sound spectrum
with the pulmonary function test results and established that normal and diseased patient
can be detected using breath sound [114]. Grenier M C et al., (1998), in a study of comparing
three acoustic and three electronic stethoscopes has shown that the advantage of an acoustic
stethoscope is in its robustness and ergonomic design, but they attenuate the transmission
of sound proportional to frequency whereas the electronic stethoscope amplifies the acoustic
signal which produces a more uniform frequency response but they face the problem of
background noise. Hence the overall conclusion was that it is possible to design a better
stethoscope which includes the advantages of both the electronic and acoustic stethoscopes
[115]. Using advanced soft computing tools with wireless technology, it is possible to use
PPG for home monitoring, long term monitoring and distant monitoring and diagnosis.
With the help of recent advancement in technology, phonopulmonography can be made
fully automated diagnostic tool.
The lung sounds are of two types namely the normal lung sound and the adventitious
lung sound (abnormal lung sound) [116]. Typical waveforms for lung sounds for normal
case and an abnormal lung sound wave is shown in Figure 6.
(a)
(b)
Figure 6: (a) Normal Vesicular Lung Sound. (b) High Pitched Crackles
Research work also has been carried out to find out the location and reason for the
generation of respiratory sound. A broadly accepted theory is as follows. When the laminar
flow of gas in the airways of breathing system at a certain critical velocity breaks up, small
parcels of gas begin to move in random direction, sometimes even in the opposite direction
of the flow. This gives rise to the turbulent flow where the energy transfer between colliding
parcels of gas is associated with transient pressure variation. The breath sound is generated
due to this rapid fluctuation of gas pressure. The critical flow velocity at which turbulence
begins depends on the interaction of inertia of parcels of gas and frictional resistance of
viscosity. The interaction of these two forces is expressed as Reynolds Number and can be
calculated from the following formula:
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
59
Reynolds Number = (Flow velocity × Tube diameter × Gas density) ÷ Gas viscosity
In a straight pipe turbulence occurs at Reynolds Number of about 2000. Turbulence
also depends upon obstacle in flow path and abrupt change in the direction of the flow
[117-121]. Abnormal lung sounds are generally associated with narrowing or closure of the
airway by contraction of bronchial smooth muscle, viscid mucus, abnormal narrowing of
airway due to scarring, foreign bodies and tumors. The correlation between forward and
lateral pressure in bronchi and flow velocity in bronchi is known as the venturi or Bernoulli
effect. The venturi effect and dynamic compression of the bronchi play an important role in
generating wheezing. Sudden reopening of closed airways determines change in the gas
pressure which results in crackling sound [122-125]. In another work Steve S. Kraman,
(1983), have employed a computerized lung sound measurement technique to study the
sites of origin and amplitudes of these sounds [126]. In order to explain the acoustic
phenomena during lung auscultation, V.I. Korenbaum et al., (2003), have proposed an
acoustic model to establish that the contributions of both air-borne and structure-borne
sound are significant in the transmission of respiratory noise to the chest wall [127].
For lung auscultation technique different sounds with their characteristics and possible
disease is presented in Table 2 [128, 129].
Table 2
Lung Sounds, Characteristics and Disease/Indication
Sound
Characteristics
Disease/Indications
Frequency
range (Hz)
Vesicular
breath
sounds
Relatively soft, low pitched;
inspiratory phase is markedly
longer than expiratory phase;
Expiration is much quieter than
inspiration; No pause between
inspiration and expiration.
Normal
150 to 500
peak freq200 to 250
Tracheal
breath
sounds
Very loud, very high pitched and
have a harsh, hollow quality;
Expiratory phase is slightly longer
than inspiratory phase
Normal
Upto 1200
Bronchial
breath
sounds
(over the
chest wall
but not on
manubrium
sternum)
Relatively loud, high pitched;
Resembles the sound of air blowing
through the hollow pipe; Expiratory
phase is louder and longer than
inspiratory phase; Distinct pause
between inspiratory and expiratory
phases
Pneumonia, Lung fibrosis,
(Consolidated lung tissue)
Upto 600
to 1000
Table 2 Contd.
60
International Journal of Computational Intelligence and Bioinformatics
Crackles
Short, explosive, nonmusical
sounds; (Laennec compared
it to the sound produced by
heating salt in a frying pan);
High pitched crackles are known
as fine crackles and low pitched
crackles are known as coarse
crackles; Crackles are due to
sudden opening of very small
airways
Severe airway obstruction;
Chronic bronchitis, Asthma,
Emphysema, Restrictive;
pulmonary disease; Interstitial
fibrosis, Asbestosis, Pneumonia,
Pulmonary congestion of heart
failure, Left ventricular failure,
Kidney failure, Pulmonary
sarcoidosis, Scleroderma,
Rheumatoid lung
Wheeze
(Rhonchus)
Wheeze is also known as Rhonchus, Malignacy, Foreign body, Asthma,
is a musical sound and is produced
All types of obstructive disease,
by a bronchus narrowed to the point COPD,
of closure; The musical character is
determined by the spectrum of frequencies that make up the sound; The
lowest frequency (Fundamental),
sets the pitch of the wheeze;
60-2000
Stridor
A loud musical sound of constant
Obstruction of central airways such
pitch; Most prominent during
as trachea or larynx, Laryngeal
inspiration; Its intensity distinguishes tumors, Tracheal stenosis,
striodor from a monophonic wheeze;
Stridor can be heard very well at a
distance from the patient unlike in the
case of wheeze; Stridor comes from
obstruction of central airways such as
trachea or larynx whereas wheeze is
produced in more peripheral airways
Peak
freq1000
Squawk
A distinctive inspiratory musical
sound (almost invariably accompanied by inspiratory crackles);
Diffuse pulmonary fibrosis with
hypersensitivity pneumonitis
100 to 2000
Duration
rarely
exceeds
400ms
Advantages of Lung Auscultation Technique
The lung auscultation technique is the most used technique for diagnosis of pulmonary
diseases. Its advantages are listed below.
• It is a very simple, safest yet accurate diagnostic technique for pulmonary diseases.
No initial setup is required for the procedure.
• Lung auscultation technique is non-invasive in nature.
• Instrument is compact, portable and easy to handle.
• Recording, reproducing and replaying is possible with the help of lung auscultation
technique in PPG configuration.
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
61
• Home monitoring, long term monitoring and distant monitoring and diagnosis can be
done with PPG.
• PPG provides automated diagnostic technique for diseases of respiratory system which
in turn eliminates human error factor.
• PPG does not require a clinical environment or the supervision of the experienced
physician for interpreting the results.
• It is convenient for children and mentally challenged persons also.
Limitation of Lung Auscultation Technique
Auscultation with a conventional mechanical and electronic stethoscope has many
limitations. They are as follows.
• The diagnosis of pulmonary disease using a conventional stethoscope is a subjective
process that depends on the individual’s own hearing, experience and ability to
differentiate between different sound patterns. Hence it becomes more dependent on
the expertise and experience of the physician which increases the probability of human
error in the diagnosis.
• It is not easy to produce quantitative measurements or make a permanent record of an
examination in documentary form using a conventional stethoscope.
• Long-term and distant monitoring or correlation of respiratory sound with other
physiological signals is also difficult using a conventional stethoscope.
• The conventional stethoscope has a good frequency response only up to 120Hz and
tends to attenuates frequency components of the lung sound signal above about 120Hz
whereas respiratory sounds ranges up to around 2KHz [4, 117, 130].
• Background noise should be as low as possible to minimize the interference while
acquiring respiratory sound signal by an electronic stethoscope.
• Various sound signals generated in body such as heart sound signal, abdominal sound
signal etc. acts as noise when they mix up with respiratory sound signal during
acquisition with the help of an electronic stethoscope.
• Electronic stethoscope should be stationary during acquisition of respiratory sound
signal to avoid shear noise generated due to friction between body and chest piece of
the stethoscope.
If the limitations of the lung auscultation technique are reduced then it has the potential
to become the best technique for diagnosis of pulmonary disease.
3. COMPARATIVE ANALYSIS AND RESULT
The purpose of the comparative analysis is to find out a simple, noninvasive yet efficient
diagnostic tool for pulmonary disease. There viability for home monitoring, long term
monitoring and distant monitoring and diagnosis was also studied. The analysis was carried
out using different parameters such as simplicity, safety, economy etc. From the above
International Journal of Computational Intelligence and Bioinformatics
62
findings and literature review following conclusions has been drawn. Lung auscultation
technique is the simplest of all techniques where as other techniques require special training
and can only be performed in a clinical environment under the supervision of the experienced
physician for interpreting their results. The risk involved in bronchoscopy is highest amongst
these techniques as it is an invasive process. Lung auscultation is very cost effective as the
instrument is cheap and does not require any setup. Accuracy of the diagnosis is more for
lung auscultation technique in phonopulmonography configuration. With the advancement
in the soft computing, lung auscultation technique in phonopulmonography configuration
and to some extent spirometry can be used for home monitoring. In some pulmonary disease
long term monitoring is required which can be done only by lung auscultation technique.
Distant monitoring and diagnosis is also possible by using lung auscultation technique
(PPG). Home monitoring and distant monitoring are particularly very important and useful
for rural area where people are bit reluctant to visit a hospital. Lung auscultation technique
(PPG) requires minimal expertise and with soft computing it can be reduced further.
Compactness, handling and portability of lung auscultation technique (PPG) instruments
are most convenient among all the techniques. From this analysis it is clear that the lung
auscultation technique with phonopulmonography configuration is the best technique for
detection of pulmonary diseases. The result of comparative analysis of diagnostic techniques
for pulmonary disease is summarized in Table 3.
Table 3
Result of Comparative Analysis of Diagnostic Techniques for Pulmonary Disease
Techniques ⇒
Parameters ⇓
Lung auscultation
with PPG
Spirometry
Roentgenography
Bronchoscopy
Simplicity/
minimal expertise
Simplest with
minimal expertise
Require
expertise
Require
expertise
Complex &
require expertise
Safety
Safest
Safe
Risk of
radiation
Risk involved
is highest
Economy
Most cost
effective
Moderate
cost
Expensive
Expensive
Accuracy
Most accurate
Accurate
Less accurate
Accurate
Home/distant
monitoring
Possible
Possible
Not
possible
Not
possible
Long term
monitoring
Possible
Not
possible
Not
possible
Not
possible
Recording and
reproducing
the data
Possible
Possible
Possible
Possible
Replaying the
recorded data
Possible
Not
possible
Not
possible
Possible
Most
convenient
Convenient
Less
convenient
Most difficult to
handle
Compactness
/Handling/portability
A Review and Comparative Analysis of Techniques for Diagnosis of Pulmonary Diseases
63
4. CONCLUSION
Pulmonary diseases are one of the important causes of deaths worldwide. To control the
casualties due to pulmonary diseases, their diagnosis should be more effective and accurate.
From the descriptions and findings, it is clear that spirometry, roentgenography and
bronchoscopy can only be performed properly in a clinical environment under the supervision
of the experienced physician for interpreting their results and moreover these techniques
are complicated. Woywodt et al., believed that the electronic stethoscope has the potential
to become efficient diagnostic tool if it is exploited in the right way [78]. Phonopulmonography has the potential to eliminate the limitations of lung auscultation technique. Home
and distant monitoring as well as long term monitoring in the case of pulmonary disease is
possible using lung auscultation technique with PPG configuration and hence it is an efficient
and best diagnostic tool for respiratory systems disease.
ACKNOLEDGEMENT
The authors wish to thank all the physicians and technicians working in government and
private hospitals who have extended their valuable suggestions and guidance in the study.
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