classification of respiratory sounds - uni-stuttgart.de · classi cation of respiratory sounds...

Outline Introduction Methods to Detect Adventitious Lung Sounds FDA Future Prospects MARS

Classification of Respiratory Sounds

Simon Muller

Institut fur Stochastik und AnwendungenUniversitat Stuttgart

CLEOS-ProjektRobert-Bosch-Krankenhaus

4. Marz 2012

Simon Muller



1 IntroductionThe origin of lung soundsLocations of sound recording

Vesicular breath soundTracheal breath soundSummary

Abnormal breath soundAdventitious sound

Summary

2 Methods to Detect Adventitious Lung SoundsSeparation and classification of crackles (fine and coarse)Separation and Classifcation of Wheeze (rhonchi, stridor, . . .)

3 FDA

4 Future Prospects

5 MARS

Simon Muller



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 turbulenceof air at the level of lobar or segmental bronchi

In smaller bronchi the gas velocity decreases and the flow islaminar ⇒ the point of origin is located in the larger air paths

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Locations of sound recording

Locations of sound recording ([2], [3], [4])

Respiratory sounds have different characteristics depending onthe location of recording and ventilation cycle (expiration orinspiration)

The locations of recording can be divided into two classes: thetrachea and the chest (front and back)

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Anterior

Abbildung: Red: Area of the trachea, Green: The chest.

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Vesicular breath sound ([2], [3], [4])

Sound is filtered by the lung and the chest wall (low-passfilter)

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 respiratorymuscles and heart

Inspiration phase is louder and has much higher frequencycomponents than expiration phase

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Abbildung: Spectrum of an inspiration and expiration phase recorded atthe trachea (the sound is taken from [6]).

Simon Muller




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 variesgreatly among the subjects

Tracheal sound has a direct connection to the flow of air ⇒inspiration and expiration phases

Simon Muller




Abbildung: Spectrum of an inspiration and expiration phase recorded atthe chest (the sound is taken from [6]).

Simon Muller




Summary

Vesicular sound has higher diagnostic value than trachealsound, since this part of the lung is affected by serious lungdiseases

Tracheal sound can be used to detect inspiration/expirationphases and eventually to estimate the flow of air⇒ it may help to record lung sounds at both locations

Simon Muller



Abnormal breath sound

Bronchial sound ([2], [3])

Similar to tracheal sound

Bronchial sound is typical for many diseases, for example

AsthmaChronic bronchitis

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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 durationthan fine crackles

Fine crackles instead are present in higher frequencies

Pitch range is from 10 to 2000 Hz and duration < 20 ms

Simon Muller




Crackle sound ([2], [3])

Occurrence and number of the crackles are an indicator of thetype of disease

Crackles can be found in many diseases, for example

Heart congestion failurePneumoniaBronchiectasisPulmonary fibrosisChronic diffuse parenchymal lung disease

They are an early sign for respiratory diseases, since finecrackles are originated in small air paths.

Simon Muller




Abbildung: Time/Frequency plane of a 16 year old boy with tuberculosis(the sound is taken from [7]).

Simon Muller




Wheeze

Wheeze can be heard at the chest (low-pass filtered) and thetrachea (unfiltered)

Wheeze is a high-pitched sound (dominant frequency at 400Hz or more) with a duration > 250 ms

Is heard at expiration

Wheeze can be found at many diseases, for example

Congestive heart failureAsthmaPneumoniaChronic bronchitiesEmphysemaBrochiectasis

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Abbildung: Time/Frequency plane of a 8 year old boy with asthma (thesound is taken from [7]).

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Squawk

Is a short wheeze

Squawk can be found for example in Pneumonia

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Rhonchi

Similar to wheeze, but dominant frequency is about 200 Hz orless

Usually rhonchi occur at airway narrows

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Stridor

Loud wheeze and high pitched

Usually stridor can be found for example in Upper airwayobstruction

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Abbildung: Time/Frequency plane of a 15 month old girl with croup (thesound is taken from [7]).

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Pleural rub sound

Sounds like running two pieces of leather against each other

Pleural surface inflammation is a typical disease for this sound

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Summary

Summary

Many diseases can be classified by these adventitious sounds

Next section deals with methods to extract them

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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 forautomatic separation of crackles from vesicular sounds

Why does this technique work?

Explosive peaks (crackles) have larger coefficients over manywavelet levelsThe coefficients of the background (vesicular sound) decreasewith increasing scale [10].

Simon Muller




Abbildung: This picture shows an example of the WPST-NST filter ([9]).

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Abbildung: Here we can see that the WPST-NST filter has just littleaffect to stationary signals. ([9]).

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Abbildung: Close-up view of a crackle, Initial Deflection Width (IDW)and Two-Cycle Duration (2CD) ([8], [1]).

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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

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Optimal classification is achieved with the matched waveletmethod ([8], [11])

Continuous wavelet transform is defined by

CWTx(τ, s) =1

s

∞∫−∞

x(t)Ψ(t − τ

s)dt

Ψ(t) is the mother wavelet and 1s Ψ( t−τs ) the wavelet basis

function, s is called the scale, and τ is a translation in thetime axis

The next two pictures show the energy distribution of fine andcoarse crackles versus the scale s

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Abbildung: Energy distribution of fine crackles ([11]).

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Abbildung: Energy distribution of coarse crackles ([11]).

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Abbildung: Schema of the crackle separation and classification algorithm([8]).

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Summary of crackle detection and classification [9]

Xiaoguang and Bahoura achieved a classification rate of93.9% 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. vianumber and character of the crackles, occurence (inspirationor/and expiration))

Simon Muller



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 forautomatic 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 sinusoidials(t) = sin(ωst + φ)In [12] the real Morlet mother wavelet is chosen as

ψ(t) =√πfb exp ∆

(− t2

fb

)cos(2πf0t)

Because of this choice, one can easily get the time and scaleinformation from the scalogram, Sc(a, b) = |CWTx(a, b)|2.

Simon Muller




Abbildung: (i) Lung sound with wheeze, (ii) spectrogram, (iii) scalogram([12]).

Simon Muller




Abbildung: The schema of this detection and separation algorithm ([12]).

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Abbildung: An example of this method ([12]).

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Summary of wheeze detection and classification

There exists an algorithm to detect and seperate wheeze fromnormal lung sound

The method also works for rhonchi and stridor

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Summary

There are methods which are able to separate and classifyadventitious sounds from lung sound

With the extracted features we are able to classify differentkinds of lung diseases

Simon Muller



FDA - Functional Data Analysis

This chapter deals with a newly developed method fromspeech classification

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Functional Classification in a separable Hilbert Space

Let X be a random variable in a separable Hilbert space withlabel Y = {0, 1}Let (Xi ,Yi ) be n independent copies of the pair (X ,Y )

We know Xi =∞∑k=1

(Xi , uk)uk with a complete orthonormal

system (ONS) (ui )∞i=1 of the Hilbert space

Let our Hilbert space be L2[a, b], then we can use thetrigonometric basis as the complete ONS (ui )

∞i=1

Let Pd be a projection operator, which maps a functionalvariable to a d-dimensional subspace of the L2[a, b] (forexample the first d Fourier coefficients)

Simon Muller



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

φk,d =

0, ifk∑

i=11[Yi (x)=0] ≥

k∑i=1

1[Yi (x)=1]

1, otherwise

Set Ynew = φk,d

Biau et al. applied this method in speech recognition ([15]).

Simon Muller



Abbildung: Waveform of the words boat and goat. This classifierachieved an error rate of 0.21 in this example ([?]).

Simon Muller



Summary

Other basis functions can be used, e.g. wavelet basis orB-splines

There exist some similar methods to the k-NN classifier, e.g.SVM

These methods seem promising in lung sound classification

Simon Muller



Future prospects

It seems possible to develop a low-cost automatedclassification software in the near future, which supports adoctor in diagnosis of lung diseases.

Simon Muller



Marburg Respiratory Sounds (MARS) database ([14])

Database contained in the year 2003 about 5000 soundrecordings from different diseases

All lung diseases were validated by three experts

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Abbildung: Database structure of MARS ([14]).

Simon Muller



Sovijarvi, A. R. A., Vandershoot, J., Earis, J.E.Standardization of computerized respiratory sound analysisCORSA Project, 2000

Sovijarvi, A. R. A., Malmberg, L. P., Charbonneau,Vandershoot, J., Dalmasso, F., Sacco, C., Rossi, M., Earis, J.E.Characteristics of breath sounds and adventitious respiratorysoundsEur Respir Rev 2000; 10: 77, 591-596

Moussavi, ZahraFundmentals of Respiratory Sounds and AnalysisMorgan & Claypool, ISBN 1598290975, 2006

Pasterkamp, H., Kraman, S. E., Wodicka, G. R.

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Respiratory Sounds - Advances Beyond the StethoscopeAm J Respir Care Med Vol. 156. pp. 974-987, 1997

Bats, B., Berger, M., Muhlhauser, I..Klinische Untersuchung des Patienten.Schattauer, 2. Auflage, ISBN 3794512731, 1993.

3M Littmann Stethoscope Edition. 20 Examples of Cardiacand Pulmonary Auscultation3M, 1996

http://lungdiseases.about.com/

Lu, Xiaoguang, Bahoura, Mohammed

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An Automatic System For Crackles Detection AndClassification.IEEE CCECE/CCGEI, 2006

Bahoura, Mohammed, Lu, XiaoguangSeparation of Crackles from Vesicular Sounds using WaveletPacket Transform

Hadjileontiadis, L. J., Panas, S.M.Separation of discontinuous adventitious sounds using awavelet-based filterIEEE 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 MatchedWavelet Analysis

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Proceedings - 19th International Conference - IEEE/EMBSOct. 30 - Nov. 2, 1997 Chicago, IL. USA

Styliani A. Taplidou, Leontios. J. Hadjileontiadis, Ilias K.Kitsas, Konstantinos I. Panoulas, Thomas Penzel, VolkerGross, and Stavros M. PanasOn Applying Continuous Wavelet Transform in WheezeAnalysisProceedings of the 26th Annual International Conference ofthe IEEE EMBS, San Francisco, CA, USA, September 1-5,2004

Feng, J. and Sttar, F.A new automated approach for identification of respiratorysoundsMultimedia and Expo, 2007 IEEE International Conference on

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V. Gross, L: J. Hadjileontiadisz, Th. Penzel, U. Koehler, C.VogelmeierMultimedia Database Marburg Respiratory Sounds (MARS)Proceedings of the 25th Annual Intemational Conference ofthe IEEE EMBS Cancun, Mexico - September 17-21, 2003

Biau, G., Bunea, F. and Wegkamp, M. H.Functional Classification in Hilbert SpacesIEEE Transactions on Information Theory, Vol. 51, No. 6, June2005

Devroye, L., Gyorfi, L., Lugosi, G.A Probabilistic Theory of Pattern RecognitionSpringer-Verlag, ISBN 0-387-94618-7, 1996

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