2019
DOI: 10.7150/ijbs.29863
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A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network

Abstract: In this paper, a method of characteristic extraction and recognition on lung sounds is given. Wavelet de-noised method is adopted to reduce noise of collected lung sounds and extract wavelet characteristic coefficients of the de-noised lung sounds by wavelet decomposition. Considering the problem that lung sounds characteristic vectors are of high dimensions after wavelet decomposition and reconstruction, a new method is proposed to transform the characteristic vectors from reconstructed signals into reconstru… Show more

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Cited by 52 publications
(26 citation statements)
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“…LS is decomposed into level 1 approximation (0 Hz ~ 11,025 Hz) and detail coefficients (11,025 Hz ~ 22,050 Hz) by the mother wavelet. The Coiflets5 wavelet [ 19 ] is used as a mother wavelet due to its morphological resemblance i.e. shape with LS signal to perform denoising.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…LS is decomposed into level 1 approximation (0 Hz ~ 11,025 Hz) and detail coefficients (11,025 Hz ~ 22,050 Hz) by the mother wavelet. The Coiflets5 wavelet [ 19 ] is used as a mother wavelet due to its morphological resemblance i.e. shape with LS signal to perform denoising.…”
Section: Methodsmentioning
confidence: 99%
“…The thresholding of skewness, kurtosis, and statistical analysis is performed to recognize the pneumonia subjects from cough analysis. The researchers are reluctant to claim the maximum authenticity of the proposed system as a large data set could change the thresholding estimates to differentiate pneumonia and normal subjects [ 17 , 18 , 19 ]. In [ 20 ], the statistical analysis of COPD and pneumonia LS is performed to design a detection system for multiple lung diseases.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Dalgacık analizi düşük frekans bilgisinin daha önemli olduğu durumlar için büyük zaman aralıklarının, yüksek frekans bilgisinin daha önemli olduğu durumlar için de daha küçük zaman aralıklarının kullanımına izin veren değişik boyutlarda bölgelere sahip bir pencereleme tekniğidir [17]. Bu yöntemde yaklaşım katsayıları (cA) ve detay katsayıları (cD) olmak üzere iki kısım elde edilmiştir.…”
Section: öZnitelik çıKarmaunclassified
“…Considering the non-stationarity in the lung sound signals, researchers have used multiresolution approaches of signal processing in [19,20,21]. The techniques based on wavelet transform have been employed to analyze and classify lung sound signals into the normal and adventitious category [22,23,24]. The nonlinear signal processing techniques such as Lyapunov exponent [25], fractal dimension [26,27], and approximate entropy [28] have proved to be useful in providing valuable diagnostic information.…”
Section: Introductionmentioning
confidence: 99%