Application of the CWT for Feature Extraction from VAG Signals in the Diagnosis of Healthy and Unhealthy Joints

Vibro-Arthrographic (VAG) signals provide valuable information about the dynamic behavior of human joints and have been widely used for noninvasive assessment of joint health. In this study, the Continuous Wavelet Transform (CWT) is employed for effective feature extraction from VAG signals to distinguish between healthy and unhealthy joint conditions. Due to the nonstationary and nonlinear nature of VAG signals, traditional time or frequency-domain methods often fail to capture their transient characteristics. The CWT enables joint time-frequency analysis, allowing meaningful patterns related to joint abnormalities to be identified at multiple scales. Statistical and energy-based features are extracted from the wavelet coefficients and used to characterize joint conditions. The extracted features are then analyzed to evaluate their discriminative capability in differentiating healthy joints from pathological ones. Experimental results demonstrate that the proposed CWT-based feature extraction approach enhances the representation of VAG signals and shows strong potential for accurate and reliable joint condition assessment. This method may contribute to the development of automated diagnostic tools for early detection of joint disorders.

کلیدواژه ها:

Vibro-Arthrographic (VAG) signals ، Continuous Wavelet Transform (CWT) ، time-frequency analysis ، healthy and unhealthy joints ، feature extraction