Denoising Knee Joint Vibration Signals Using Variational Mode Decomposition

Analysis of knee joint vibration (VAG) signals using signal processing, feature extraction and classification techniques has shown promise for the non-invasive diagnosis of knee joint disorders. However for such techniques to yield reliable results, the digitally acquired signals must be accurately denoised. This paper presents a novel method for denoising VAG signals using variational mode decomposition followed by wiener entropy thresholding and filtering. Standard metrics: mean squared error, mean absolute error, signal to noise ratio, peak signal to noise ratio and CPU consumption time have been calculated to assess the performance our method. Metric: normalized root mean squared error has also been evaluated to estimate the effectiveness of our method in denoising synthetic VAG signals containing additive white gaussian noise. The proposed method yielded a superior performance in denoising raw VAG signals in comparison to previous methods such as wavelet-soft thresholding, empirical mode decomposition-detrended fluctuation analysis and ensemble empirical mode decomposition-filtering. Our method also yielded better performance in denoising synthetic VAG signals in comparison to other methods like wavelet and wavelet packet-soft thresholding, wavelet-matching pursuit algorithm, empirical mode decomposition-detrended fluctuation analysis and ensemble empirical mode decomposition-filtering. The proposed method although computationally more complex, yields the most accurate denoising.