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Denoising Knee Joint Vibration Signals Using Variational Mode Decomposition

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Information Systems Design and Intelligent Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 433))

Abstract

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.

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Correspondence to Aditya Sundar .

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Sundar, A., Das, C., Pahwa, V. (2016). Denoising Knee Joint Vibration Signals Using Variational Mode Decomposition. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 433. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2755-7_74

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  • DOI: https://doi.org/10.1007/978-81-322-2755-7_74

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2753-3

  • Online ISBN: 978-81-322-2755-7

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