Download Knee Joint Vibroarthrographic Signal Processing and Analysis by Yunfeng Wu PDF

By Yunfeng Wu

This ebook provides 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 few powerful computer-aided tools for research of the nonlinear and nonstationary biomedical indications generated through complicated physiological mechanics. This publication additionally introduces numerous renowned computing device studying and development acceptance algorithms for biomedical sign classifications. The publication is well-suited for all researchers seeking to greater comprehend knee joint biomechanics and the complex know-how for vibration arthrometry.

Dr. Yunfeng Wu is an affiliate Professor on the institution of data technological know-how and expertise, Xiamen college, Xiamen, Fujian, China.

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Annu Rev Fluid Mech 31(1):417–457 6. Huang NE, Wu MLC, Long SR, Shen SSP, Qu W, Gloersen P, Fan KL (2003) A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc Lond Ser A: Math, Phys Eng Sci 459(2037):2317–2345 7. Kantelhardt JW, Koscielny-Bunde E, Rego HH, Havlin S, Bunde A (2001) Detecting longrange correlations with detrended fluctuation analysis. Physica A 295(3):441–454 8. Kim KS, Seo JH, Kang JU, Song CG (2009) An enhanced algorithm for knee joint sound classification using feature extraction based on time-frequency analysis.

Int J Cogn Inform Nat Intell 3(4): 73–90 30. Wu Y, Rangayyan RM (2011) Noise Cancellation in ECG Signals with an Unbiased Adaptive Filter. In: Wang YX (ed) Transdisciplinary advancements in cognitive mechanisms and human information processing. IGI Global, Hershey, pp 348–366 31. Wu Y, Rangayyan RM, Zhou Y, Ng SC (2009) Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med Eng Phy 31(1):17–26 32. Wu Y, Krishnan S, Rangayyan RM (2010) Computer-aided diagnosis of knee-joint disorders via vibroarthrographic signal analysis: a review.

For the abnormal VAG signal in Fig. 05 in normalized amplitude. The VMS parameter can also be used to measure the signal variations for a half (with regard to a single knee flexion or extension motion) of the VAG signal. The study of Rangayyan and Wu [23] reported the significant difference (twosample Student’s t-test: p < 0:01) of VMS values between the healthy signal group and pathological signal group. Yang et al. 1 0 0 Time (sec) Fig. 2 (a) The amplitude-normalized vibroarthrographic (VAG) signal of a healthy subject.

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