Open AccessJournal Article
Characteristics of breath sounds and adventitious respiratory sounds
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This article is published in European Respiratory Review.The article was published on 2000-01-01 and is currently open access. It has received 210 citations till now. The article focuses on the topics: Respiratory sounds.read more
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Journal ArticleDOI
Neural classification of lung sounds using wavelet coefficients.
TL;DR: A novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN) to evaluate the condition of respiratory system using lung sounds.
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Analysis of Respiratory Sounds: State of the Art
TL;DR: The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed, and a search for new markers to increase the efficiency of decision aid algorithms and tools.
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Wheeze detection based on time-frequency analysis of breath sounds
TL;DR: An automatic technique for wheeze detection and monitoring using spectral analysis and the experimental and testing results justified the efficient performance and high noise robustness of the TF-WD.
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Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes
TL;DR: Experimental results show that the approach based on MFCC coefficients combined to GMM is well adapted to classify respiratory sounds in normal and wheeze classes and an optimized threshold to discriminate the wheezing class from the normal one is proposed.
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Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis
TL;DR: While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes.