基于多波小波的下肢信号去噪

Ghada Kareem
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引用次数: 0

摘要

本文介绍了一种使用Daubechies小波去噪下肢表面肌电信号的不同方法,为了使其正常工作,我们需要尽可能地从该信号中去除许多噪声。以往的工作并不能准确地确定最适合下肢的方法。本文采用不同的阈值方法来计算信噪比的最大值,以确定最佳的去噪方法。并详细介绍了用于减少表面肌电信号噪声的去噪技术。本研究对下肢肌电图的实际应用具有重要意义。本文旨在确定小波变换(Daubechies wavelet)在下肢表面肌电信号中实现最高信噪比的最佳参数。所使用的样本来自11名健康受试者,他们做了3种不同的动作,用4个电极提取信号。为了确定最佳的去噪方法,使用不同的阈值类型、涂抹水平和噪声结构进行计算。实验结果表明,在每个测试信号中,严格的SURE阈值和比例白噪声提供了最高的信噪比,但不同信号的Daubechies水平不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LOWER LIMB SEMG DENOISING USING DAUBECHIES WAVELETS
: This paper represents a different way of denoising lower limb Surface electromyography sEMG signals using Daubechies wavelets Much noise will be needed to remove as we can from this signal for it to function properly. The previous works couldn’t accurately determine the most suitable method to be used for lower limbs. This paper uses different thresholding approaches to calculate the highest value of SNR to identify the best denoising method. And a complete detailed survey of denoising techniques for reducing noise from surface electromyography signals is provided. This research has important implications for the practical application of lower limb EMG. This paper aimed to ascertain what are the most optimal parameters to be applied while using wavelet transform (Daubechies wavelets) to achieve the highest possible SNR in sEMG of the lower limb. The sample that was used came from 11 healthy subjects doing 3 different movements, using 4 electrodes to extract the signal. To identify the best denoising is calculated using different thresholding types, Daubechies levels, and noise structures. The result from this experiment indicates that the hard-rigorous SURE threshold and scaled white noise provide the highest SNR in every signal tested but the Daubechies level differs from one signal to another.
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