Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification

Rizal, Achmad; Hidayat, Risanuri; Nugroho, Hanung Adi

doi:10.1109/ibiomed.2016.7869823

Cited by 15 publications

(20 citation statements)

References 25 publications

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“…The results indicated that Support Vector Machine (SVM) as a classifier could produce the highest accuracy compared to multilayer perceptron and K-NN [3]. Rizal and co-workers used multi-order Tsallis entropy (TE) as a feature extraction method for pulmonary crackle [4]. The reported results showed that TE with the order of 2, 3, and 4 could produce accuracy up to 95.35%.…”

Section: Introductionmentioning

confidence: 90%

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Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

2018

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Abstract. Lung sound is a complex signal produced by the respiratory process. The complex signal has several properties including a chaotic behavior, fractality or self-similarity property. One of lung sounds that arise from abnormalities occurred in the respiratory tract is pulmonary crackle sound. In this study, we tested the degree of self-similarity of pulmonary crackle sound and examined whether the degree of similarity can be used as a feature to differentiate the pulmonary lung crackle sound with normal lung sound. The results showed the sufficient strength of the self-similarity nature of the pulmonary crackle sound. Meanwhile, a test using K-mean clustering produced an accuracy of 87.5% to differentiate between the pulmonary crackle sound and normal lung sound. It can be stated then that it is deemed important to take another feature to obtain higher accuracy. The high self-similarity degree indicates that a pulmonary crackle sound has fractals properties.

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Section: Introductionmentioning

confidence: 90%

“…We used the same lung sound data set were collected from the internet as did in a previous paper [4]. The data consisted of 20 crackle sound data and 20 normal bronchial sounds.…”

Section: Lung Sound Datamentioning

confidence: 99%

Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

2018

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“…where q is non-extensivity order, pi is discrete probability, and W is the microscopic configuration of the system. In this paper we use the order of non-extensivity q = 2 proved to produce the highest accuracy in [31]. Figure 4 shows the results of the multiscale process using the coarse-grained procedure for the normal bronchial and lung sound crackle sound.…”

Section: Tsallis Entropymentioning

confidence: 99%

Comparison of Multiscale Entropy for Lung Sound Classification

Rizal¹,

Hidayat²,

Nugroho³

2018

IJEECS

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Lung sound is a biological signal used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for the automatic lung sound classification. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques are used to measure the signal complexity on biological signal and other physical signals. In this paper, a number of multiscale entropy techniques for the lung sound classification are discussed. The results showed that Multiscale Permutation Entropy (MPE) could produce the highest accuracy of 97.98% for five classes of lung sound data. Results achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. The use of Permutation entropy (PE) on a multiscale scheme was to obtain a better accuracy compared to PE on one scale only

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“…Various entropy calculation methods have been used in lung sound analysis in which Sample entropy was used as a feature for detecting pulmonary sound status using morphological complexities [1]. Meanwhile, Tsallis entropy was used for lung sound analysis in [2] and [3]. Multiscale entropy was reported to be better in distinguishing lung sounds in alveolitis patients rather than spectral or statistical methods [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification

Rizal¹,

Hidayat²,

Nugroho³

2019

ijacsa

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Wavelet Entropy (WE) is one of the entropy measurement methods by means of the discrete wavelet transform (DWT) subband. Some of the developments of WE are wavelet packet entropy (WPE), wavelet time entropy. WPE has several variations such as the Shannon entropy calculation on each subband of WPD that produces 2N entropy or WPE, which yields an entropy value. One of the WPE improvements is multilevel wavelet packet entropy (MWPE), which yields entropy value as much as N decomposition level. In a previous research, MWPE was calculated using Shannon method; hence, in this research MWPE calculation was done using Renyi and Tsallis method. The results showed that MWPE using Shannon calculation could yield the highest accuracy of 97.98% for N = 4 decomposition level. On the other hand, MWPE using Renyi entropy yielded the highest accuracy of 93.94% and the one using Tsallis entropy yielded 57.58% accuracy. Here, the test was performed on five lung sound data classes using multilayer perceptron as the classifier.

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Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification

Cited by 15 publications

References 25 publications

Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

Fractality evaluation for pulmonary crackle sound using the Degree of Self-Similarity

Comparison of Multiscale Entropy for Lung Sound Classification

Comparison of Multilevel Wavelet Packet Entropy using Various Entropy Measurement for Lung Sound Classification

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