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Conference Paper: Crackle detection and classification based on matched waveletanalysis

TitleCrackle detection and classification based on matched waveletanalysis
Authors
KeywordsLung sound
Crackle
Wavelet transform
Issue Date1997
PublisherIEEE.
Citation
The 19th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Chicago, Illinois, USA, 30 October - 2 November 1997, v. 4, p. 1638-1641 How to Cite?
AbstractCrackles have an explosive pattern in the time domain, with a rapid onset and a short duration. The timing, repeatability and shape of crackles are important parameters for diagnosis. Therefore, automatic detection of crackles and their classification have important clinical value. Since crackles have a general characteristic shape, it is obvious that wavelet analysis can be exploited to detect crackles and to classify them. In this paper, we present a new method for crackle detection which is based on a `matched' wavelet transform. We first model crackles as a mathematical function. Then we design a matched wavelet based on this model. Applying a soft threshold to the results of the continuous wavelet transform to suppress noise further, the optimal scale can be obtained. Crackles can be detected based on the envelope of the signal at an optimal scale, and can be classified based on the energy distribution with scale. Theory, methods and experimental results are given in detail in this paper.
Persistent Identifierhttp://hdl.handle.net/10722/46042
ISSN

 

DC FieldValueLanguage
dc.contributor.authorDu, Men_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorSun, Jen_HK
dc.date.accessioned2007-10-30T06:41:17Z-
dc.date.available2007-10-30T06:41:17Z-
dc.date.issued1997en_HK
dc.identifier.citationThe 19th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Chicago, Illinois, USA, 30 October - 2 November 1997, v. 4, p. 1638-1641en_HK
dc.identifier.issn1557-170Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/46042-
dc.description.abstractCrackles have an explosive pattern in the time domain, with a rapid onset and a short duration. The timing, repeatability and shape of crackles are important parameters for diagnosis. Therefore, automatic detection of crackles and their classification have important clinical value. Since crackles have a general characteristic shape, it is obvious that wavelet analysis can be exploited to detect crackles and to classify them. In this paper, we present a new method for crackle detection which is based on a `matched' wavelet transform. We first model crackles as a mathematical function. Then we design a matched wavelet based on this model. Applying a soft threshold to the results of the continuous wavelet transform to suppress noise further, the optimal scale can be obtained. Crackles can be detected based on the envelope of the signal at an optimal scale, and can be classified based on the energy distribution with scale. Theory, methods and experimental results are given in detail in this paper.en_HK
dc.format.extent317459 bytes-
dc.format.extent13817 bytes-
dc.format.extent8841 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectLung sounden_HK
dc.subjectCrackleen_HK
dc.subjectWavelet transformen_HK
dc.titleCrackle detection and classification based on matched waveletanalysisen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1557-170X&volume=4&spage=1638&epage=1641&date=1997&atitle=Crackle+detection+and+classification+based+on+matched+waveletanalysisen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/IEMBS.1997.757031en_HK
dc.identifier.hkuros34614-

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