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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Critical Reviews™ in Biomedical Engineering
SJR: 0.26 SNIP: 0.375 CiteScore™: 1.4

ISSN Печать: 0278-940X
ISSN Онлайн: 1943-619X

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Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.v38.i2.60
pages 201-224

Computer-Aided Diagnosis of Knee-Joint Disorders via Vibroarthrographic Signal Analysis: A Review

Yunfeng Wu
Xiamen University
Sridhar Krishnan
Department of Electrical and Computer Engineering, Ryerson University 350, Victoria Street, Toronto, ON M5B 2k3, Canada
Rangaraj M. Rangayyan
Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada

Краткое описание

The knee is the lower-extremity joint that supports nearly the entire weight of the human body. It is susceptible to osteoarthritis and other knee-joint disorders caused by degeneration or loss of articular cartilage. The detection of a knee-joint abnormality at an early stage is important, because it helps increase therapeutic options that may slow down the degenerative process. Imaging-based arthrographic modalities can provide anatomical images of the joint cartilage surfaces, but fail to demonstrate the functional integrity of the cartilage. Knee-joint auscultation, by means of recording the vibroarthrographic (VAG) signal during bending motion of a knee, could be used to develop a noninvasive diagnostic tool. Computer-aided analysis of VAG signals could provide quantitative indices for screening of degenerative conditions of the cartilage surface and staging of osteoarthritis. In addition, the diagnosis of knee-joint pathology by means of VAG signal analysis may reduce the number of semi-invasive diagnostic arthroscopic examinations. This article reviews studies related to VAG signal analysis, first summarizing the pilot studies that demonstrated the diagnostic potential of knee-joint auscultation for the detection of degenerative diseases, and then describing the details of recent progress in analysis of VAG signals using temporal analysis, frequency-domain analysis, time-frequency analysis, and statistical modeling. The decision-making methods used in the related studies are summarized, followed by a comparison of the diagnostic performance achieved by different pattern classifiers. The final section is a perspective on the future and further development of VAG signal analysis.

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