Pulmonary crackle detection using time–frequency and time–scale analysis

Serbes, Görkem; Şakar, C. Okan; Kahya, Yasemin P.; Aydin, Nizamettin

doi:10.1016/j.dsp.2012.12.009

Cited by 77 publications

(30 citation statements)

References 19 publications

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“…In a study reported by Mendes and co-author [2], some Teager features such as energy, information entropy, and local Higuchi fractal dimension were calculated on the nonstationary part of the output of wavelet packed stationary transform-non-stationary transform filter (WPST-NST). In another study, some tests were conducted on the effects of the use of the window, wavelet, and machine learning for the pulmonary sound detection in the time-frequency domain and time-scale domain [3]. The results indicated that Support Vector Machine (SVM) as a classifier could produce the highest accuracy compared to multilayer perceptron and K-NN [3].…”

Section: Introductionmentioning

confidence: 99%

“…In another study, some tests were conducted on the effects of the use of the window, wavelet, and machine learning for the pulmonary sound detection in the time-frequency domain and time-scale domain [3]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…Many authors have presented different respiratory crackles filtering (Hadjileontiadis and Panas, 1997;Mastorocostas et al, 2000;Sankur et al, 1996;Tolias et al, 1997), feature extraction (CharlestonVillalobos et al, 2007;Ponte et al, 2013;Yeginer and Kahya, 2009;Yeginer and Kahya, 2010), Volume 31, Number 2, p. 148-159, 2015 and classification techniques (Abbas and Fahim, 2010;Charleston-Villalobos et al, 2011;Chen and Chou, 2014;Dokur, 2009;Içer and Gengec, 2014;Kandaswamy et al, 2004;Lu and Bahoura, 2008;Pesu et al, 1998;Serbes et al, 2013;Xie et al, 2012;Yeginer and Kahya, 2005;Zhenzhen et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Ponte et al (2013) characterized crackles by obtaining their maximum frequency by applying discrete pseudo Wigner-Ville distribution. Serbes et al (2013) extracted various feature sets and classified crackles using dual-tree complex wavelet transform, SVM, k-nearest neighbor (kNN), and multilayer perceptron (MLP). Zhenzhen et al (2012) proposed a time-domain processing method to extract features of crackles based on the Fractional Hilbert Transform theories.…”

Section: Introductionmentioning

confidence: 99%

Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

et al. 2015

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Introduction: Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods: In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results: Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion: DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.

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“…Morphologically, crackles are explosive and transient. The inherent properties of pulmonary crackles such as timing, epochs of occurrence, and pitch can be used in the diagnosis of a variety of pulmonary diseases such as pneumonia, bronchiectasis, fibrosing alveolitis and asbestosis [1].…”

Section: Introductionmentioning

confidence: 99%

The detection of crackles based on mathematical morphology in spectrogram analysis

Zhang

Wang

Han

et al. 2015

THC

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Abstract. BACKGROUND:Crackles are very common abnormal breath sounds in the lung and can be used to diagnose pulmonary diseases. OBJECTIVE: In this study, a method is proposed for the detection of adventitious transient sounds from normal breath sounds. METHODS: This method automatically recognizes crackles based on the extraction and analysis of spectral information from digitally recorded lung sounds. Various mathematical morphology feature sets were extracted through wavelet spectrogram analysis on pulmonary signals. In order to evaluate the effects of different wavelets types on crackle detection, different wavelets were tested. RESULTS:The results showed that the proposed method achieved an 86% accuracy in the detection of crackles. CONCLUSIONS: The spectrograms of the crackles in the lung exhibit irregular ellipse image features. For lung sound analysis, this is a useful feature that can be used for the immediate recognition and analysis of crackles.

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Pulmonary crackle detection using time–frequency and time–scale analysis

Cited by 77 publications

References 19 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

Pulmonary crackle characterization: approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction

The detection of crackles based on mathematical morphology in spectrogram analysis

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