Machine learning in lung sound analysis: A systematic review

Palaniappan, Rajkumar; Sundaraj, Kenneth; Ahamed, Nizam Uddin

doi:10.1016/j.bbe.2013.07.001

Cited by 148 publications

(64 citation statements)

References 32 publications

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“…Various feature extraction and classification techniques are usually used for analysis of lung sound. A detailed study of different features and classifiers can be found in [6]. Among all the techniques used, the wavelet-based method with its focus on non-stationary lung sounds, has been found useful [3].…”

Section: Introductionmentioning

confidence: 99%

“…Wheeze is an adventitious, continuous and musical breath sound with frequency range usually exceeding 100 Hz. Crackle specifies a discontinuous type adventitious lung sound with a wide frequency range, up to 2000 Hz [6]. The automatic detection of lung sounds involves three major steps: pre-processing, feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of cepstral features for robust lung sound classification

Sengupta

Sahidullah

Saha

2015

2015 Annual IEEE India Conference (INDICON)

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Detection of lung abnormalities by characterizing lung sounds has been a primary step for clinical examination for a pulmonologist. This work focuses on utilization of cepstral features for lung sound analysis and classification. The proposed method incorporates statistical properties of cepstral features along with artificial neural network (ANN) based classification. Experimental results indicate that the proposed features outperform the wavelet-based features and conventional mel-frequency cepstral features. Further analyses have been performed on the proposed features to experimentally optimize the frame size and feature dimensionality. We also look at optimizing number of hidden layer nodes to improve robustness. We have found that the optimized features perform better for a wide range of signalto-noise ratio (SNR) values.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of cepstral features for robust lung sound classification

Sengupta

Sahidullah

Saha

2015

2015 Annual IEEE India Conference (INDICON)

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“…Auscultation is the basic and primary physical examination done by all medical professionals. Auscultation mainly relies on the hearing perception of the medical professional and it may vary for each individual [8]. Hence it is required to develop a computerized respiratory sound analysis system which can accurately detect the respiratory pathology.…”

Section: Introductionmentioning

confidence: 99%

A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

Palaniappan

Sundaraj

et al. 2015

Applied Soft Computing

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“…The digital signal processing method used can be seen from the signal domain (Rizal et al, 2015c), the classification method (Palaniappan et al, 2013), or the case of lung sound analyzed (Shaharum et al, 2012). To achieve a good result, the techniques used must be appropriate to the nature and characteristics of lung sounds.…”

Section: Related Workmentioning

confidence: 99%

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Rizal¹,

Hidayat²,

Nugroho³

2017

American Journal of Applied Sciences

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Lung sound is produced by the respiration process in the human respiratory tract. It contains information about the health of the respiratory organs. Lung sound is non-stationary signals and complex signals. One method for the analysis of non-stationary signals often used for the analysis of lung sounds is Empirical Mode Decomposition (EMD). EMD is used to view the Instantaneous Frequency (IF) of the lung sound to differentiate the types of lung sounds. Features extraction directly on Intrinsic Mode Function (IMF) of EMD result is rarely performed in the lung sound analysis. In this research, the EMD was used to obtain IMF of lung sounds. IMF from lung sounds was then analyzed using the Hjorth descriptors. As a classifier, we used Multilayer Perceptron (MLP) with a three-fold cross validation (3fold CV) for validation. From the test, it was found that activity parameter in the first 10 IMF yielded 98.8% accuracy on five classes of data tested. The proposed method showed the excellence of the measurement of the Hjorth descriptor on IMF for feature extraction in lung sound classification.

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Machine learning in lung sound analysis: A systematic review

Cited by 148 publications

References 32 publications

Optimization of cepstral features for robust lung sound classification

Optimization of cepstral features for robust lung sound classification

A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

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