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Most of the biomedical signals gathered from various electronic devices contain a certain amount of noise based on the external atmosphere, the working condition of the devices and the activity of the clinician handling the observation section. Analysing such signals may result in an ambiguous prediction of the patients’ diagnosis. Thus, the eradication of noises in the biosignal waves gets a tremendous attraction in the medical field. Hence, a specific aim of this study is to handle two perfect and precise methods of clearing the defectiveness in the electromyography (EMG) signals which are termed stationary wavelet transform (SWT) and fractional linear prediction (FLP). Once we get the filtered signal from these two techniques, we can calculate the fractal dimension of the denoised EMG signals for obtaining the accurate way for offering medication. In this regard, the present study primarily reveals the comparison between fractal dimensions that are gained through the following specified methods, Higuchi, Katz, rescaled-range analysis, relative dispersion and power spectral density. Ultimately, these dimensions are compared to show a clear-cut result by considering both the filtering mechanisms. For the comparison of statistical data of an undertaken sample, mathematics provides an estimating procedure called the ANOVA test. This paper also deals with some of the statistical measures for comparing the denoising methods and results to attain a better one. As a consequence, we have concluded that the FLP filtering method is more reliable than the SWT technique, based on the fractal dimension forecasted via the five mechanisms.

A comparative study on estimation of fractal dimension of EMG signal using SWT and FLP