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Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques

Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques

Feng Jin, Farook Sattar
Copyright: © 2009 |Pages: 10
ISBN13: 9781599048451|ISBN10: 1599048450|EISBN13: 9781599048468
DOI: 10.4018/978-1-59904-845-1.ch039
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MLA

Jin, Feng, and Farook Sattar. "Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques." Encyclopedia of Information Communication Technology, edited by Antonio Cartelli and Marco Palma, IGI Global, 2009, pp. 291-300. https://doi.org/10.4018/978-1-59904-845-1.ch039

APA

Jin, F. & Sattar, F. (2009). Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques. In A. Cartelli & M. Palma (Eds.), Encyclopedia of Information Communication Technology (pp. 291-300). IGI Global. https://doi.org/10.4018/978-1-59904-845-1.ch039

Chicago

Jin, Feng, and Farook Sattar. "Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques." In Encyclopedia of Information Communication Technology, edited by Antonio Cartelli and Marco Palma, 291-300. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-845-1.ch039

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Abstract

Pulmonary auscultation has been the key method to detect and evaluate respiratory dysfunctions for many years. However, auscultation with a stethoscope is a subjective process that depends on the individual’s own hearing, experience, and ability to differentiate between different sounds (Sovijarvi et al, 2000). Therefore, the computerized method for recording and analysis of pulmonary auscultative signals, being an objective way, are recently playing a more and more important role in the evaluation of patients with pulmonary diseases. Noise interference is one of the most influential factors when dealing with respiratory sound recordings. By definition of (Rossi et al, 2000), any sound not directly induced by breathing is regarded as background noise (BN). BN is divided into two types: environmental noise, which consists of continuous noise and transient noise, and nonrespiratory sounds and body sounds (muscle contraction sounds, skin friction, and heart sounds). The adaptive filtering is usually used to reduce the background noise. However, the problem of existing proposed filtering methods are either not able to minimize the interference or provides distortion which is especially undesirable for biomedical signals (Donoho, 1992).

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