EVALUATION OF SAVITZKY-GOLAY FILTERING APPLIED TO VIBROARTOGRAPHIC SIGNALS ACQUIRED AT THE KNEE LEVEL AND ITS IMPACT ON THE CREST FACTOR

Denisa ȘMADICI, Viorel PALEU

Abstract


Arthrosis is a degenerative disease that affects the vast majority of the population, especially the elderly and overweight people. Often the condition is diagnosed in its advanced stages when pain and swelling appear. The diagnostic methods currently used are largely invasive and not standardized. Non-invasive and standardized methods are needed for the vibroartographic (VAG) diagnosis of this disorder, requiring new signal processing methods for denoising and selecting useful features of the VAG signal. Therefore, this paper aims to review current diagnostic methods, analyze the viability of Savitzky-Golay filtering and the Crest factor in separating abnormal from normal knee joint status for a database of eighty-nine signals reported in the literature, and outline future research directions for the development of a viable technique that can be used to diagnose osteoarthritis in new, non-invasive ways. Our findings prove that SG filtering and the Crest factor cannot separate normal and abnormal VAG signals, as previously suggested by some authors. Analysis of the Calgary confident database, which is widely validated in the literature, could help us in mapping new effective research directions.


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References


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