Abstract
Vibroarthrographic (VAG) signals emitted by human knee joints can be used to develop a non-invasive diagnostic tool to detect articular cartilage degeneration. VAG signals are nonstationary and multicomponent in nature; time-frequency distributions (TFDs) provide powerful means to analyze such signals. The objective of this paper is to determine the TFD suitable for identification and extraction of VAG signal features of clinical significance. The TFDs considered are: autoregressive (AR) model-based TFD; the reassigned, smoothed, pseudo-Wigner-Ville (RSPWV) distribution; and a TFD based on signal decomposition using the matching pursuit (MP) algorithm. As the true TFD of a VAG signal is not known, the results of the TFDs were compared based on the expected characteristics using synthetic signals. The MP TFD shows good potential in analyzing multicomponent signals with low signal-to-noise ratio when compared to the AR model-based TFD and the RSPWV method. The TFD techniques were also tested on VAG signals with additional information provided by auscultation and arthroscopy. The results indicate that the MP TFD is the best available TFD to analyze VAG signals.
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