Time-frequency signal feature extraction and screening of knee joint vibroarthrographic signals using the matching pursuit method

Nonstationary features of knee joint vibroarthrographic (VAG) signals were extracted from their time-frequency distributions (TFDs) obtained using the matching pursuit method. Features computed as marginal calculations of the TFDs were instantaneous energy, instantaneous energy spread, instantaneous mean frequency, and instantaneous mean frequency spread. The features carry information about the combined time-frequency dynamics of the signals. The mean and standard deviation of the features were also computed, and each VAG signal was represented by a set of just 8 parameters. The method was tested on 37 VAG signals (19 normal and 18 abnormal) with no restriction on the type of articular cartilage pathology. Discriminant analysis of the parameters showed an accuracy of 89.5% at the training stage and 77.8% at the test stage. Compared to the authors' previous methods, the proposed method does not need any joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of articular cartilage pathology.