Respiratory sound analysis in healthy and pathological subjects: A wavelet approach

Sello, Stefano; Strambi, Soo-Kyung; Michele, G. De; Ambrosino, Nicolino

doi:10.1016/j.bspc.2008.02.002

Cited by 27 publications

(15 citation statements)

References 27 publications

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“…The diagnosis through these approaches may be incorrect due to the presence of large cysts and bulla within the lung or plural space, subject clothing, skin folds, and chest wall artifacts. To overcome these drawbacks researchers have developed many efficient and improved algorithms for analyzing the thoracic sounds based on signal processing, pattern recognition, and statistical theorems and combination of this ideas [1], [3], [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain

Mondal

Bhattacharyat²,

Saha

2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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Lung sound (LS) contains information regarding the lungs status. Medical practitioners listen to these sounds using stethoscope and make interpretation. This procedure is known as auscultation which totally depends on the physicians experience and knowledge. There is a probability of misinterpretation due to human factor involved. In this paper, we propose a method based on complexity measuring theorem that can give reliable diagnosis of LS in an automated environment. The developed algorithm detects the lung conditions by calculating the sample entropy value of the frequency spectrum. The results are evaluated through statistical analysis and corroborated by a pulmonologist. The technique could be very useful in developing assisting device for medical professionals.

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Section: Introductionmentioning

confidence: 99%

Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain

Mondal

Bhattacharyat²,

Saha

2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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“…Similarly, Sello et al proposed the application of the wavelet method combined with statistical power distribution method to characterize the frequency power distribution of the unsteady RS signals. [24] Analysis of the findings of this study showed the possibility of extracting useful statistics related to the energy content and its mean frequency distribution, giving quantitative characteristics of the respiratory pattern. Results showed that different power spectra patterns recognize normal from abnormal (unhealthy) patterns.…”

Section: Discussionmentioning

confidence: 85%

An alternative respiratory sounds classification system utilizing artificial neural networks

et al. 2015

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R espiratory sound (RS) is regarded one of the most significant biosignals used to diagnose certain respiratory abnormalities. It is established that breath sounds are created in the larger airways as a result of vibrations that are generated due to air velocity and turbulence. RSs are known to be highly non-stationary and non-linear due to variations in the airflow rate and airflow volumes during the respiratory cycle. The non-linearity of the RS stems mainly from the complex turbulent flow dynamics and its structural interaction with the larger airway walls.RSs detected from the chest wall and mouth may be classified as normal and adventitious sounds. Owing to the presence of adventitious (abnormal) sounds, such signals convey valuable information pertaining to the underlying malfunctioning of the pulmonary system. On the other hand, normal sounds include those generated by healthy Original Article An Alternative Respiratory Sounds Classification System Utilizing Artificial Neural NetworksRami J. Oweis, Enas W. Abdulhay, Amer Khayal, Areen AwadBackground: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods:This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results:The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques. (Biomed J 2015;38:153-161)

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“…The literatures record and list several attempts as using the Mahalanobis distance, autoregressive coefficients, k‐nearest neighbour and quadratic classifier, amplitude and frequency parameters with regression line slopes, neural network‐based classification technique using subphase features like autoregressive coefficients, prediction error and the ratio of expiration and inspiration duration, stochastic classification using time and frequency domain features, maximum likelihood approach and classifier for classification of normal and crackle sounds with host of other efforts. They include work with neural network classifier and wavelet domain features, computer‐aided system, pattern recognition‐based model matching algorithm and a multisensor breath sound mapping device, spectral analysis method and also a vibration response imaging‐based method…”

Section: Discussionmentioning

confidence: 99%

“…Beyond the traditional phonopneumography, several developments employed advancement in the computer technologies, statistical signal processing and machinelearning algorithms. Several methods based on time, frequency and the analyses of time-frequency domain [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] have been attempted earlier, but none of them could be reasonably successful.…”

Section: Introductionmentioning

confidence: 99%

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease

Bhattacharyya¹,

Mondal

Dey³

et al. 2015

Respirology

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Background and objective: Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. Methods: We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. Results: The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. Conclusions: The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition.

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Respiratory sound analysis in healthy and pathological subjects: A wavelet approach

Cited by 27 publications

References 27 publications

Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain

Diagnosing of the lungs status using morphological anomalies of the signals in transformed domain

An alternative respiratory sounds classification system utilizing artificial neural networks

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease

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