本文選題：表面肌電信號(hào)　切入點(diǎn)：運(yùn)動(dòng)單元　出處：《浙江大學(xué)》2014年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：表面肌電信號(hào)(sEMG)通常被認(rèn)為是由肌肉中多個(gè)運(yùn)動(dòng)單元(MU)生成的動(dòng)作電位序列疊加而形成的,信號(hào)能通過(guò)放置在肌肉處皮膚上的電極進(jìn)行測(cè)量。對(duì)表面肌電信號(hào)進(jìn)行分解能夠獲得運(yùn)動(dòng)單元的發(fā)放以及募集的相關(guān)信息,能夠?yàn)樯窠?jīng)肌肉系統(tǒng)的研究和診斷提供重要的依據(jù)。 本文提出了幾種多通道表面肌電信號(hào)的分解方法。圍繞多通道表面肌電信號(hào)的分解,本文的主要工作和研究成果有： 1.提出將卷積核補(bǔ)償(CKC)方法和模糊C均值(FCM)聚類(lèi)方法結(jié)合,進(jìn)行多通道表面肌電信號(hào)的分解。先選出幾個(gè)通道的信號(hào),再用這幾個(gè)通道的信號(hào)得出初始的MU發(fā)放序列,然后用模糊C均值聚類(lèi)方法對(duì)初始的發(fā)放序列一些峰值對(duì)應(yīng)的時(shí)刻進(jìn)行聚類(lèi)處理,最后用CKC方法得到最終的MU發(fā)放序列。仿真結(jié)果表明這種方法與原CKC方法比較,能夠改進(jìn)信號(hào)分解的性能。 2.根據(jù)已有的卷積核補(bǔ)償(CKC)方法,提出了一種新的表面肌電信號(hào)分解方法。該方法結(jié)合自組織映射(SOM)神經(jīng)網(wǎng)絡(luò),首先找出在某一時(shí)刻具有能量活動(dòng)的發(fā)放序列,其次對(duì)這個(gè)發(fā)放序列的一些較大值對(duì)應(yīng)的時(shí)刻用自組織映射神經(jīng)網(wǎng)絡(luò)進(jìn)行聚類(lèi),然后利用聚類(lèi)后的時(shí)刻所對(duì)應(yīng)的多通道測(cè)量信號(hào)的值求出最終的一個(gè)MU發(fā)放序列。仿真信號(hào)測(cè)試得到的結(jié)果表明,所提出的這種方法是有效的。3.提出了一種能夠逼近線性最小均方誤差(LMMSE)估計(jì)量的多通道表面肌電信號(hào)的分解方法。首先用K均值對(duì)不同時(shí)刻對(duì)應(yīng)的測(cè)量向量進(jìn)行分類(lèi),然后估算出初始的MU發(fā)放序列。再用一種多步迭代算法對(duì)初始的發(fā)放序列不斷進(jìn)行迭代計(jì)算,得到最終的MU發(fā)放序列。用仿真信號(hào)和真實(shí)的表面肌電信號(hào)對(duì)該方法的性能進(jìn)行了檢驗(yàn)。采用仿真信號(hào)時(shí),即使信噪比達(dá)到-10dB,所有的10個(gè)發(fā)放序列仍能夠以大于90%的準(zhǔn)確率被重建出來(lái)。對(duì)從手部第一背側(cè)骨間肌采用64個(gè)通道電極陣列測(cè)量得到的真實(shí)表面肌電信號(hào)進(jìn)行分解時(shí),多于10個(gè)的MU能夠被成功提取出來(lái)。并用“二源法”對(duì)真實(shí)信號(hào)的分解性能進(jìn)行了驗(yàn)證,從兩個(gè)獨(dú)立的分組中提取出較多的相同MU個(gè)數(shù)和大于92%的相同MU的相同發(fā)放時(shí)刻的百分比,表明了該方法在表面肌電信號(hào)分解中的可靠性。 4.提出了兩種基于測(cè)量信號(hào)相關(guān)的多通道表面肌電信號(hào)的分解方法。一種用Moore-Penrose偽逆構(gòu)建測(cè)量信號(hào)矩陣的相關(guān)矩陣,另一種用奇異值分解(SVD)方法構(gòu)建測(cè)量信號(hào)矩陣的相關(guān)矩陣。由于同一個(gè)MU不同發(fā)放時(shí)刻對(duì)應(yīng)的測(cè)量向量有著一定程度的相似性,因此可以采用特定的迭代優(yōu)化方法逐步增強(qiáng)所選出的測(cè)量向量與構(gòu)建的相關(guān)矩陣之間的相關(guān)性,來(lái)達(dá)到分解信號(hào)的目的。并用仿真信號(hào)和真實(shí)的表面肌電信號(hào)對(duì)提出的方法進(jìn)行了檢驗(yàn)。在仿真信號(hào)進(jìn)行的測(cè)試中,兩種方法都能夠以大于95%的準(zhǔn)確率重建出多于48個(gè)的MU發(fā)放序列。在真實(shí)信號(hào)的測(cè)試中,兩種方法都能夠重建出多于15個(gè)的MU發(fā)放序列。并進(jìn)一步用“二源法”對(duì)真實(shí)信號(hào)的分解結(jié)果進(jìn)行了驗(yàn)證,結(jié)果表明了這兩種方法分解的可靠性。 本論文的研究獲得美國(guó)國(guó)立衛(wèi)生研究院(NIH K99DK082644,NIHROODK082644)的部分資助。
[Abstract]:Surface electromyography (sEMG) is usually considered by a number of muscle unit (MU) series of action potential generated by the superposition and the formation of the signal can be measured by electrodes placed on the skin muscles. The surface EMG signal decomposition to obtain relevant information and to raise the issue of motion unit, can provide an important basis for the study and diagnosis of neuromuscular system.
In this paper, several methods for decomposition of multichannel surface EMG signals are proposed. The main work and research results of this paper are as follows: the decomposition of multichannel surface EMG signals.
1. the convolution kernel compensation (CKC) method and fuzzy C means (FCM) clustering method with decomposition of multi-channel surface EMG signal. Select several channels, then the signal of several channels to get the initial MU release sequence, and then use the fuzzy C mean clustering method on the initial peak firing sequence the corresponding time clustering processing, by using the CKC method, the final MU distribution sequence is obtained. The simulation results show that compared with the original CKC method this method can improve the performance of signal decomposition.
2. according to the existing convolution kernel compensation (CKC) method, put forward a new kind of surface EMG signal decomposition method. This method combines self-organizing map (SOM) neural network, first identify the firing sequence with energy activities at a certain moment, then to release this sequence of some of the larger value corresponding to the time of clustering self organizing mapping neural network, and then use the multi-channel measurement signal corresponding to the clustering moments after the value obtained a MU final firing sequence. Simulation test results show that the proposed method is effective to approximate.3. proposed a linear minimum mean square error (LMMSE) estimation the channel surface EMG signal decomposition method. The amount of the first measurement vector with K mean at different times corresponding to the classification, and then estimate the initial MU release sequence. Then a multi step iterative algorithm to the initial The firing sequence continuous iterative calculation, obtain the final MU firing sequence. The performance of the method by simulation data and real surface EMG signal was tested. Using the simulation signal, even if the signal-to-noise ratio reached -10dB, 10 of all the firing sequence is still able to accurately rate greater than 90% was reconstructed. The decomposition of real surface EMG signal from the hands of the first dorsal interosseous muscle using 64 channel electrode array measurement, more than 10 MU can be successfully extracted. With the "two source" method to validate the decomposition performance of real signals, extract the same percentage more number of MU and more than 92% of the same MU the same time issued from two independent groups, shows the reliability of this method in surface EMG signal decomposition.
4. this paper puts forward two decomposition methods based on the measured signal related to the multi-channel sEMG signal. A correlation matrix with Moore-Penrose pseudo inverse matrix to construct the measurement signal, another with a singular value decomposition (SVD) method to construct the correlation matrix of measurement signal matrix. Since the measurement vector with a MU corresponding to different firing moment there is a certain degree of similarity, so it can gradually increase the correlation between the correlation matrix measurement vector selected and constructed the iterative optimization method used to achieve specific, decomposition of signal. And the simulation signal and real surface EMG signal of the proposed method is tested in the simulation test signal. In the two methods are able to more than 95% accuracy of reconstruction of more than 48 MU. The firing sequence of the true signal test, the two methods can reconstruct more than 15 MU The results of the decomposition of real signals are verified by the two source method. The results show the reliability of the decomposition of the two methods.
The research in this paper is partially funded by the National Institutes of Health (NIH K99DK082644, NIHROODK082644).

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：R741.044;TN911.7

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