Tonal Index in Digital Recognition
of Lung Auscultation
Marcin Wiśniewski ,Tomasz Zieliński
Signal Processing Algorithms, Architectures,Arrangements, and
Applications Conference Proceedings (SPA), 2011
Presenter : Kun-Han Jhan
Advisor : Dr. Chun-Ju Hou
Date : 2012.12.26
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Outline
Introduction
 Lung sounds
 Wheezes Descriptors
 Testing methodology
 Experiments
 Conclusions
 References

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Introduction

Asthma
◦ Secretion or mucus 
◦ Muscle contraction
◦ Main indicator
 An appearance of wheezes in breath cycle
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Introduction

Lungs auscultation
◦ A non-invasive test in asthma
◦ Evaluate the stage of the disease
◦ Evaluate the level of wheeze appearance

The main problem of lungs auscultation
◦ Depend on doctor’s experience
◦ Subjective
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Introduction

The advantage of digital lung auscultation
◦ Objective and unambiguous
◦ Telemedicine
 Doctor can see the results without necessity of direct
meeting
 Patients not have to go to the hospital
 Increase the comfort of patient’s life
 Decrease stress in direct meetings with doctors
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Lung sound
Lung
sound

Location
◦ Trachea
◦ Bronchus
◦ Alveoli

Characteristics
◦ Like a noise
◦ Frequency rang : 20 ~ 1.6kHz
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Lung sound

Wheeze
◦ A single or multi tone sound
 Duration > 80 ms
 Frequency: 100 ~ 1k Hz
◦ Mixed normal lung sounds with wheezes
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Wheezes Descriptors

Features
◦
◦
◦
◦
◦
Kurtosis (K)
Spectral Peaks Entropy (SPE)
Frequency Ratio (FR)
Spectral Flatness (SF)
Tonal index
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Wheezes Descriptors

Kurtosis (K)
◦ Measure a level of peakedness of a probability
distribution in time domain
眾 中μ
x : input signal
數位
μ : mean
數
2
σ : variance
◦ k = 3 (noisy signal with normal or sub-gaussian
distribution)
◦ k > 3 (the signal with wheezes)
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Wheezes Descriptors

Spectral peaks entropy (SPE)
◦ Frequency domain
Cn : peak value of frequency spectrum

N
n 1
Cn : total sum of these peaks
Entropy:
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Wheezes Descriptors

Frequency ratio
◦ Frequency feature
◦ The signal with wheezes has higher values of this
ratio than normal lung sounds
◦ The area under the power spectral density of ROI
◦ The area of total power spectral density
◦ FR descriptor was modified and tested once again as
a Energy Ratio (ER) descriptor
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Wheezes Descriptors

Spectral flatness
◦ A signal feature defined in frequency domain
◦ A ratio of geometrical and arithmetical mean
values
N


N 1
k 0
n 1
k 0
x(k ) : geometrical mean value
x(k )
N
: arithmetical mean value
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Wheezes Descriptors

Tonal index
◦ A spectral feature
◦ MPEG psychoacoustic model
◦ FFT module r and phase pw
w
rw  2rw (t 1)  rw (t  2)
pw  2 pw (t 1)  pw (t  2)
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Testing methodology

Tonal signals simulation
 Artificial wheezes: multi-frequency signals with
random three frequencies (100~1200Hz)
 Normal breathing signals

Features testing
 Signal samples: 1024 points
 Add white Gaussian noise with different SNR scale
 Sampling frequency: 8 KHz

Recognition
 SVM(Support Vector Machine)
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Experiments

The modeled wheezes
◦ Artificial noise with normal
◦ Training signals with different gains

To recognition process
◦ 100 samples
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Experiments
Artificial signals
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Experiments
Artificial signals
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Experiments

Hybrid data
◦ Artificial wheezes added to the normal lung
sounds taken from chest auscultation.
◦ 8 kHz/16-bit recorder
◦ Panasonic WM-61 microphones

To recognition process
◦ 28 samples
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Experiments
Hybrid signals
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Experiments
Hybrid signals
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Experiments

Artificial data
◦ TI
◦ SPE.

Hybrid data
◦ TI

In both study the FR shows the worst
effectiveness.
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Conclusions
The tonal index is more sensitive to
tonality in noisy signals reaches full
efficiency in lower SNR as well.
 Increasing number of features in algorithm
not necessarily improves effectiveness of
recognition.
 The best result reaches the algorithm with
2 features{TI,ER}– 94.2% and {K,TI}– 94.6%
effectiveness.

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References

[1] Aydore, S.; Sen, I.; Kahya, Y.P.; Mihcak, M.K., Classification
of respiratory signals by linear analysis, Engineering in Medicine and
Biology Society, 2009. EMBC 2009. Annual International Conference
of the IEEE Publication Year: 2009, pp. 2617 - 2620

[2] Jianmin Zhang; Wee Ser; Jufeng Yu; Zhang, T.T.; A Novel Wheeze
Detection Method for WearableMonitoring Systems Intelligent Ubiquitous
Computing and Education, 2009 International Symposium on Publication
Year: 2009 pp. 331 - 334

[3] A.H. Gray, J.D. Markel, A spectral-flatness measure for studying the
autocorrelation method of linear prediction of speech analysis, IEEE Trans.
Acoust. Speech Signal Process., 1974, 22, pp. 207–217

[4] H. Pasterkamp, S.S. Kraman, G. R. Wodicka, Respiratory Sounds.
Advances Beyond the Stethoscope, Am. J. Respir. Crit. Care Med., Volume
156, Number 3, pp. 974-987, September 1997
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