本文選題：表面肌電信號(hào)　切入點(diǎn)：經(jīng)驗(yàn)?zāi)B(tài)分解　出處：《儀器儀表學(xué)報(bào)》2014年11期 　論文類型：期刊論文

【摘要】：為了更好地消除混雜在表面肌電信號(hào)(s EMG)中的噪聲,提出一種基于噪聲統(tǒng)計(jì)特性的EMD自相關(guān)消噪方法。首先,對(duì)含噪s EMG信號(hào)進(jìn)行EMD分解,并根據(jù)噪聲統(tǒng)計(jì)特性降低低信噪比的高頻IMF分量的能量后重組信號(hào)。其次,對(duì)重組后的信號(hào)進(jìn)行自相關(guān)函數(shù)特性的EMD分解,并對(duì)自相關(guān)函數(shù)方差低于閾值的高頻IMF分量進(jìn)行小波去噪。最后,把處理后的高頻IMF分量和低頻IMF分量重構(gòu),得到的信號(hào)即為消噪信號(hào)。實(shí)驗(yàn)結(jié)果表明,該方法不僅能更好的消除噪聲,而且在低信噪比情況下有良好表現(xiàn)。
[Abstract]:In order to better eliminate the noise mixed in the surface EMG signal, a EMD autocorrelation denoising method based on the statistical characteristics of noise is proposed. Firstly, the noisy s EMG signal is decomposed by EMD. According to the statistical characteristics of noise, the energy recombination signal of high frequency IMF component with low SNR is reduced. Secondly, the EMD decomposition of autocorrelation function is applied to the reconstructed signal. The high frequency IMF component whose variance of autocorrelation function is lower than the threshold value is de-noised by wavelet transform. Finally, the processed high frequency IMF component and the low frequency IMF component are reconstructed and the signal is de-noised. The experimental results show that, This method not only eliminates noise better, but also performs well under low SNR.
【作者單位】： 杭州電子科技大學(xué)智能控制與機(jī)器人研究所;
【基金】：國(guó)家自然科學(xué)基金(60903084,61172134) 浙江省自然科學(xué)基金(LY13F030017) 浙江省科技計(jì)劃(2014C33105)項(xiàng)目資助
【分類號(hào)】：TN911.4

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