By Yunfeng Wu
This e-book offers the state of the art applied sciences of knee joint vibroarthrographic sign research for the screening and detection of knee joint accidents. It describes a couple of potent computer-aided tools for research of the nonlinear and nonstationary biomedical signs generated by means of advanced physiological mechanics. This booklet additionally introduces numerous well known desktop studying and trend acceptance algorithms for biomedical sign classifications. The booklet is well-suited for all researchers seeking to higher comprehend knee joint biomechanics and the complicated expertise for vibration arthrometry.
Dr. Yunfeng Wu is an affiliate Professor on the university of knowledge technological know-how and expertise, Xiamen collage, Xiamen, Fujian, China.
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Additional info for Knee Joint Vibroarthrographic Signal Processing and Analysis
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Umapathy K, Krishnan S (2006) Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals. IEEE Trans Biomed Eng 53(3):517–523 84. van Leersum M, Schweitzer ME, Gannon F, Finkel G, Vinitski S, Mitchell DG (1996) Chondromalacia patellae: an in vitro study. Comparison of MR criteria with histologic and macroscopic findings. Skelet Radiol 25(8):727–732 85. Vignon E, Conrozier T, Piperno M, Richard S, Carrillon Y, Fantino O (1999) Radiographic assessment of hip and knee osteoarthritis.
Wu Y, Cai S, Yang S, Zheng F, Xiang N (2013b) Classification of knee joint vibration signals using bivariate feature distribution estimation and maximal posterior probability decision criterion. Entropy 15(4):1375–1387 35. Wu Y, Yang S, Zheng F, Cai S, Lu M, Wu M (2014) Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis. Physiol Meas 35(3):429–439 36. Zhang YT, Frank CB, Rangayyan RM, Bell GD, Ladly KO (1991) Step size optimization of nonstationary adaptive filtering for knee sound analysis.
The Daubechies wavelets are excellent choice for the VAG signal decomposition using the MP algorithm [2], because they belong to a family of orthogonal wavelets that have a support of minimum size for any given number of vanishing moments [4]. Based on the Daubechies wavelets, the MP algorithm may decompose a given signal with appropriate time and scale properties [13]. 3) mD0 where am are the expansion coefficients and M denotes the iterations of decomposition. 4) where hx; dr0 i denotes the inner product (projection).
