不同阈值技术对不同小波肌电信号去噪的影响

R. Thukral, Ashwani Kumar, A. Arora, Gulshan
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引用次数: 9

摘要

肌电图(EMG)最初是用于诊断神经肌肉疾病和异常。临床应用很快就得到了明显的发展,最显著的是在癫痫方面,最后,由于义肢的引入,它最终广为人知,特别是身体动力义肢。肌电图记录在不同的运动中,以了解肌肉的电作用和功能状态,从而识别正常情况下的药物变化。本文的目的是利用小波分解的每个层次上的阈值来去除噪声,如果阈值合适,那么肌电信号的损失机会就会更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of Different Thresholding Techniques for Denoising of EMG Signals by using Different Wavelets
The Electromyogram (EMG) was at first produced for diagnosing the neuro-muscular disorders and abnormalities. Clinical applications before long wound up obvious, most eminently in epilepsy, lastly it ended up well known because of the introduction of prosthetics, explicitly body-powered prosthesis. The EMG recorded amid different movements to know the electrical action and functional state of the muscles which recognizes the medicinal variations from the normal conditions. The aim of this paper is to remove noise using the thresholding values at each and every level of the decomposition using wavelet can be a better technique, if the thresholding values are appropriate so that the loss of the EMG signal chances will be less.
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