Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Classification of Respiratory Sounds
Simon Müller
Institut für Stochastik und Anwendungen
Universität Stuttgart
CLEOS-Projekt
Robert-Bosch-Krankenhaus
4. März 2012
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
1
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Introduction
The origin of lung sounds
Locations of sound recording
Vesicular breath sound
Tracheal breath sound
Summary
Abnormal breath sound
Adventitious sound
2
3
4
5
Summary
Methods to Detect Adventitious Lung Sounds
Separation and classification of crackles (fine and coarse)
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
FDA
Future Prospects
MARS
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
The origin of lung sounds
The origin of the lung sounds ([2], [3], [4])
The origin of lung sounds is not yet completely clear
The lung itself cannot generate sounds, if there is no airflow
(minimum of a flow is required)
It is assumed that the breath sound is induced by turbulence
of air at the level of lobar or segmental bronchi
In smaller bronchi the gas velocity decreases and the flow is
laminar ⇒ the point of origin is located in the larger air paths
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Locations of sound recording ([2], [3], [4])
Respiratory sounds have different characteristics depending on
the location of recording and ventilation cycle (expiration or
inspiration)
The locations of recording can be divided into two classes: the
trachea and the chest (front and back)
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Anterior
Abbildung: Red: Area of the trachea, Green: The chest.
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Vesicular breath sound ([2], [3], [4])
Sound is filtered by the lung and the chest wall (low-pass
filter)
Sound is soft and low-pitched
Pitch depends on the anatomy of the patient (e.g. age, BMI,
physique)
Frequency band contains also components of respiratory
muscles and heart
Inspiration phase is louder and has much higher frequency
components than expiration phase
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Abbildung: Spectrum of an inspiration and expiration phase recorded at
the trachea (the sound is taken from [6]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Tracheal breath sound ([2], [3], [4])
Sound is not or little filtered
Sound is loud and high-pitched (up to 2300 Hz)
Difference of the power of inspiration and expiration varies
greatly among the subjects
Tracheal sound has a direct connection to the flow of air ⇒
inspiration and expiration phases
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Abbildung: Spectrum of an inspiration and expiration phase recorded at
the chest (the sound is taken from [6]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Locations of sound recording
Summary
Vesicular sound has higher diagnostic value than tracheal
sound, since this part of the lung is affected by serious lung
diseases
Tracheal sound can be used to detect inspiration/expiration
phases and eventually to estimate the flow of air
⇒ it may help to record lung sounds at both locations
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Bronchial sound ([2], [3])
Similar to tracheal sound
Bronchial sound is typical for many diseases, for example
Asthma
Chronic bronchitis
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Crackle sound ([2], [3])
Dicontinuous lung sound
Character is explosive and of transient nature
Crackles can be separated into two classes: fine and coarse
Coarse crackles are of less intensity and of longer duration
than fine crackles
Fine crackles instead are present in higher frequencies
Pitch range is from 10 to 2000 Hz and duration < 20 ms
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Crackle sound ([2], [3])
Occurrence and number of the crackles are an indicator of the
type of disease
Crackles can be found in many diseases, for example
Heart congestion failure
Pneumonia
Bronchiectasis
Pulmonary fibrosis
Chronic diffuse parenchymal lung disease
They are an early sign for respiratory diseases, since fine
crackles are originated in small air paths.
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Abbildung: Time/Frequency plane of a 16 year old boy with tuberculosis
(the sound is taken from [7]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Wheeze
Wheeze can be heard at the chest (low-pass filtered) and the
trachea (unfiltered)
Wheeze is a high-pitched sound (dominant frequency at 400
Hz or more) with a duration > 250 ms
Is heard at expiration
Wheeze can be found at many diseases, for example
Congestive heart failure
Asthma
Pneumonia
Chronic bronchities
Emphysema
Brochiectasis
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Abbildung: Time/Frequency plane of a 8 year old boy with asthma (the
sound is taken from [7]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Abnormal breath sound
Squawk
Is a short wheeze
Squawk can be found for example in Pneumonia
Simon Müller
Classification of Respiratory Sounds
Future Prospects
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Rhonchi
Similar to wheeze, but dominant frequency is about 200 Hz or
less
Usually rhonchi occur at airway narrows
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Stridor
Loud wheeze and high pitched
Usually stridor can be found for example in Upper airway
obstruction
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Abbildung: Time/Frequency plane of a 15 month old girl with croup (the
sound is taken from [7]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abnormal breath sound
Pleural rub sound
Sounds like running two pieces of leather against each other
Pleural surface inflammation is a typical disease for this sound
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Summary
Summary
Many diseases can be classified by these adventitious sounds
Next section deals with methods to extract them
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Separation of the crackles from vesicular sound [9]
A filter based on the wavelet packet transform is used for
automatic separation of crackles from vesicular sounds
Why does this technique work?
Explosive peaks (crackles) have larger coefficients over many
wavelet levels
The coefficients of the background (vesicular sound) decrease
with increasing scale [10].
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: This picture shows an example of the WPST-NST filter ([9]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: Here we can see that the WPST-NST filter has just little
affect to stationary signals. ([9]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: Close-up view of a crackle, Initial Deflection Width (IDW)
and Two-Cycle Duration (2CD) ([8], [1]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Classification of crackles [9]
Next we need a classification of the crackles (fine or coarse)
Fine crackle: average duration of IDW and 2CD is 0.7 and
< 10 ms
Coarse crackle: average duration of IDW and 2CD is 1.5 and
> 10 ms
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Optimal classification is achieved with the matched wavelet
method ([8], [11])
Continuous wavelet transform is defined by
1
CWTx (τ, s) =
s
Z∞
x(t)Ψ(
t −τ
)dt
s
−∞
Ψ(t) is the mother wavelet and 1s Ψ( t−τ
s ) the wavelet basis
function, s is called the scale, and τ is a translation in the
time axis
The next two pictures show the energy distribution of fine and
coarse crackles versus the scale s
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: Energy distribution of fine crackles ([11]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: Energy distribution of coarse crackles ([11]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Abbildung: Schema of the crackle separation and classification algorithm
([8]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and classification of crackles (fine and coarse)
Summary of crackle detection and classification [9]
Xiaoguang and Bahoura achieved a classification rate of
93.9% with this wavelet based method ([8])
It is a state of the art algorithm to detect and classify crackles
The information can be used to classify lung diseases (e.g. via
number and character of the crackles, occurence (inspiration
or/and expiration))
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Separation of the wheeze from vesicular/tracheal sound [12]
A filter based on the continuous wavelet transform is used for
automatic separation of wheeze from vesicular/tracheal sound
Why does this technique work?
Lung sound can be split into two sounds x(t) = s(t) + v (t)
(wheeze and vesicular/tracheal sound)
A characteristic of the wheeze s(t) is that it is sinusoidial
s(t) = sin(ωs t + φ)
In [12] the real Morlet mother wavelet is chosen as
2
p
t
cos(2πf0 t)
ψ(t) = πfb exp ∆ −
fb
Because of this choice, one can easily get the time and scale
information from the scalogram, Sc (a, b) = |CWTx (a, b)|2 .
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Abbildung: (i) Lung sound with wheeze, (ii) spectrogram, (iii) scalogram
([12]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Abbildung: The schema of this detection and separation algorithm ([12]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Abbildung: An example of this method ([12]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Summary of wheeze detection and classification
There exists an algorithm to detect and seperate wheeze from
normal lung sound
The method also works for rhonchi and stridor
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)
Summary
There are methods which are able to separate and classify
adventitious sounds from lung sound
With the extracted features we are able to classify different
kinds of lung diseases
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
FDA - Functional Data Analysis
This chapter deals with a newly developed method from
speech classification
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Functional Classification in a separable Hilbert Space
Let X be a random variable in a separable Hilbert space with
label Y = {0, 1}
Let (Xi , Yi ) be n independent copies of the pair (X , Y )
∞
P
(Xi , uk )uk with a complete orthonormal
We know Xi =
k=1
system (ONS) (ui )∞
i=1 of the Hilbert space
Let our Hilbert space be L2 [a, b], then we can use the
trigonometric basis as the complete ONS (ui )∞
i=1
Let Pd be a projection operator, which maps a functional
variable to a d-dimensional subspace of the L2 [a, b] (for
example the first d Fourier coefficients)
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Functional Classification in a separable Hilbert Space
New functional variable Xnew with unknown label Ynew
Apply Pd to Xnew
Estimate Ynew via the k nearest neighbors of Xnew

k
k
P
P

0, if
1[Yi (x)=0] ≥
1[Yi (x)=1]
φk,d =
i=1
i=1

1, otherwise
Set Ynew = φk,d
Biau et al. applied this method in speech recognition ([15]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abbildung: Waveform of the words boat and goat. This classifier
achieved an error rate of 0.21 in this example ([?]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Summary
Other basis functions can be used, e.g. wavelet basis or
B-splines
There exist some similar methods to the k-NN classifier, e.g.
SVM
These methods seem promising in lung sound classification
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Future prospects
It seems possible to develop a low-cost automated
classification software in the near future, which supports a
doctor in diagnosis of lung diseases.
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Marburg Respiratory Sounds (MARS) database ([14])
Database contained in the year 2003 about 5000 sound
recordings from different diseases
All lung diseases were validated by three experts
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Abbildung: Database structure of MARS ([14]).
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Sovijärvi, A. R. A., Vandershoot, J., Earis, J.E.
Standardization of computerized respiratory sound analysis
CORSA Project, 2000
Sovijärvi, A. R. A., Malmberg, L. P., Charbonneau,
Vandershoot, J., Dalmasso, F., Sacco, C., Rossi, M., Earis, J.
E.
Characteristics of breath sounds and adventitious respiratory
sounds
Eur Respir Rev 2000; 10: 77, 591-596
Moussavi, Zahra
Fundmentals of Respiratory Sounds and Analysis
Morgan & Claypool, ISBN 1598290975, 2006
Pasterkamp, H., Kraman, S. E., Wodicka, G. R.
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Respiratory Sounds - Advances Beyond the Stethoscope
Am J Respir Care Med Vol. 156. pp. 974-987, 1997
Bats, B., Berger, M., Mühlhauser, I..
Klinische Untersuchung des Patienten.
Schattauer, 2. Auflage, ISBN 3794512731, 1993.
3M Littmann Stethoscope Edition. 20 Examples of Cardiac
and Pulmonary Auscultation
3M, 1996
http://lungdiseases.about.com/
Lu, Xiaoguang, Bahoura, Mohammed
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
An Automatic System For Crackles Detection And
Classification.
IEEE CCECE/CCGEI, 2006
Bahoura, Mohammed, Lu, Xiaoguang
Separation of Crackles from Vesicular Sounds using Wavelet
Packet Transform
Hadjileontiadis, L. J., Panas, S.M.
Separation of discontinuous adventitious sounds using a
wavelet-based filter
IEEE Trans Biomed Eng, vol. 44, no. 12, pp. 1269-1281, 1997
Du, M., Chan, F.H.Y., Lam, F.K. and Sun, J.
Crackle Detection and Classification Based on Matched
Wavelet Analysis
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
Proceedings - 19th International Conference - IEEE/EMBS
Oct. 30 - Nov. 2, 1997 Chicago, IL. USA
Styliani A. Taplidou, Leontios. J. Hadjileontiadis, Ilias K.
Kitsas, Konstantinos I. Panoulas, Thomas Penzel, Volker
Gross, and Stavros M. Panas
On Applying Continuous Wavelet Transform in Wheeze
Analysis
Proceedings of the 26th Annual International Conference of
the IEEE EMBS, San Francisco, CA, USA, September 1-5,
2004
Feng, J. and Sttar, F.
A new automated approach for identification of respiratory
sounds
Multimedia and Expo, 2007 IEEE International Conference on
Simon Müller
Classification of Respiratory Sounds
MARS
Outline
Introduction
Methods to Detect Adventitious Lung Sounds
FDA
Future Prospects
V. Gross, L: J. Hadjileontiadisz, Th. Penzel, U. Koehler, C.
Vogelmeier
Multimedia Database Marburg Respiratory Sounds (MARS)
Proceedings of the 25th Annual Intemational Conference of
the IEEE EMBS Cancun, Mexico - September 17-21, 2003
Biau, G., Bunea, F. and Wegkamp, M. H.
Functional Classification in Hilbert Spaces
IEEE Transactions on Information Theory, Vol. 51, No. 6, June
2005
Devroye, L., Györfi, L., Lugosi, G.
A Probabilistic Theory of Pattern Recognition
Springer-Verlag, ISBN 0-387-94618-7, 1996
Simon Müller
Classification of Respiratory Sounds
MARS