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In this work, we extracted the variation features and performed the pattern classifications for knee joint vibroarthro-graphic (VAG) signal processing. The signal turns count with fixed threshold and coefficient of variation (CV) of envelope energy were used to characterize the intrinsic oscillations of the VAG signals. The Kolmogorov-Smirnov test results indicated the pathological VAG signals possess...
Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant...
Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose the use of several parameters related...
Vibrations emitted from a knee joint during flexion or extension are expected to be associated with pathological conditions in the joint. Externally detected vibroarthrographic (VAG) signals may be useful indicators of roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the joint. Computer-aided analysis of VAG signals could provide quantitative indices...
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