Extraction and Screening of Knee Joint Vibroarthrographic Signals Using Independent Component Analysis and Empirical Mode Decomposition Method

• Main Authors: Jien-Chen Chen, 陳錦城
• Other Authors: Pi-Cheng Tung
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/55718451429778470488

博士 === 國立中央大學 === 機械工程學系 === 101 === A phenomenon can be found which abnormal joint sound arises from knee joint disorder during knee motion in the clinical diagnosis. The knee joint could produce vibration signals from a standing position to a squatting position, and the vibration arthrometry（VAM）could diagnose the disorders of the knee joint by analyzing these vibration signals. In this study we will apply VAM to the patients of the normal and degenerative arthritis. Because VAM is a noninvasive diagnostic tool, it has great potential. The main methods in the thesis we apply VAM to the vibration signals of the normal and degenerative arthritis, utilizing Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) to establish the mathematical model after adaptive decomposition, and try to find out the characteristic parameters of the vibration signals in these diseases. Vibration arthrometry (VAM) provided a noninvasive, simple, and cheap clinic tool for diagnosing knee joints. Appropriate therapy can be given to the patients with correct diagnosis. The performance of the combined ICA/HHT technique is verified experimentally. The experimental results show that proposed ICA/HHT approach has better recognition performance than that obtained using other traditional methods.