基于小波变换和多重分形分析的表面肌电信号分析
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摘要
表面肌电信号是从人体骨骼肌表面通过电极记录下来的神经肌肉活动时发放的生物电信号,它反映了神经、肌肉的功能状态。表面肌电信号在临床医学、运动医学、人机工效学、康复医学、神经生理学、电生理学等领域被广泛应用。本文针对假肢控制和肌肉疲劳评价问题,利用小波变换和非线性动力学方法对表面肌电信号进行了深入的分析和研究,所做的主要工作及创新之处如下:
为了尽可能地提高假肢控制时表面肌电信号分类的准确识别率,我们尝试利用局部判别基方法来对表面肌电信号进行分类,该方法的主要思想是根据可分性度量找出具有最大可分性的部分小波包系数作为特征矢量。但考虑到不同人的不同动作的表面肌电信号的能量大小是有差异的,因此本文提出了基于可分性度量和小波包相对能量的最佳小波包方法,并将该方法应用到表面肌电信号的分类问题当中,实验证明该算法比固定尺度的小波包基方法有着更好的效果。在该方法中我们所使用的可分性度量都只是一些简单的距离度量函数,它们并不能给出模式分类问题中的最佳特征。因此,我们对上述方法进行了改进,提出了DB指标(Davies-Bouldin index)和小波包相对能量相结合的最佳小波包方法,并将该方法应用到了表面肌电信号的分类问题当中。和其它现有的方法相比较,该方法在分类准确率上取得了明显的改进。
为了减少假肢控制时对动作进行分类识别的运算时间,本文提出了一种基于离散谐波小波包变换的表面肌电信号分类方法。首先对信号进行离散谐波小波包分解,然后计算信号在各个频带的相对能量并将其作为表面肌电信号的特征。接着,我们采用基于遗传算法和神经网络分类器的特征选择方法对特征空间进行降维处理从而获得了具有最大可分离性的特征。最后,用BP(back propagation)神经网络和获得的具有最大可分离性的特征来评价被提出方法的分类效果。实验结果表明,离散谐波小波包变换方法获得了比时域方法更高的分类准确率,另一方面,和离散一般小波包变换方法相比,该方法节省了更多的计算时间。因此,离散谐波小波包变换方法为肌电假肢系统准确、实时控制提供了新的可行方法。
肌肉疲劳特性的研究在康复医学、运动医学、人机工效学等领域具有重要作用。本文从多重分形的角度来分析了肌肉疲劳过程中的表面肌电信号。目前,我们已经发现在肌肉的静态疲劳的过程当中,表面肌电信号具有多重分形特性,并且肌电信号奇异谱的面积随着肌肉的疲劳有明显地上升趋势。因此,我们可以把奇异谱面积作为静态收缩时肌肉疲劳的评价指标。实验结果表明,当肌肉处于静态收缩期间时,和传统的肌肉疲劳评价指标中值频率相比,我们提出的肌电信号奇异谱面积显示出了更高的变化斜率,故用奇异谱面积做肌肉疲劳评价指标的灵敏度就要更高,这为量化静态肌肉疲劳提供了一个更为可靠的方法。在肌肉做动态收缩时,表面肌电信号仍然具有多重分形特性,同时我们发现随着肌肉的疲劳,肌电信号的奇异谱面积也在增加,因此奇异谱面积也可以作为动态收缩时肌肉疲劳的评价指标。但是和静态收缩相比,动态收缩时奇异谱面积随时间变化的斜率要小很多。我们推测肌肉静态收缩和动态收缩之间的这种差异可能是由于收缩肌肉内的血液流动所引起的。
Surface electromyographic (SEMG) signals can be monitored noninvasively by using electrodes on the skin surface. They are the summation of all motor unit action potentials (MUAP) within the pick-up area of the electrodes, so they provide information of the neuromuscular activities of the examined muscle. In addition, these signals have been widely applied to clinical diagnosis, sports medicine, ergonomics, rehabilitation medicine, neurophysiology and electrophysiology. Aimed at control system for powered prostheses and muscle fatigue assessment, this dissertation deeply investigated SEMG signals using wavelet transform and nonlinear dynamic method. The main and creative work is as follows:
In order to improve the classification accuracy of SEMG signals in control system for powered prostheses, we tried to use local discriminant bases method to classify SEMG signals and the main idea of this method is to employ wavelet packet decomposition coefficients with maximum class separability as the feature vectors. However, considering that the energy of the SEMG signals varies with different subjects and different movements, this dissertation presented the optimal wavelet packet method based on discriminant measures and relative energy representation of wavelet packet and applied this method to SEMG signals classification. Experimental results showed that this approach achieved higher accuracy than fixed scale wavelet packet method. In this method, the discriminant measures which we used are only some simple distance measure function and they can not give the optimal features for pattern recognition problems. So we improved the above method and presented the optimal wavelet packet method based on Davies-Bouldin (DB) index and relative energy representation of wavelet packet, which was then applied to SEMG signals classification. Compared with other existing methods, this one had significant improvement in classification accuracy.
In order to reduce the computation time of classifying SEMG signals in control system for powered prostheses, this dissertation proposed an efficient method to SEMG signals classification based on the discrete harmonic wavelet packet transform (DHWPT). Firstly, the relative energy of the signal in each frequency band calculated after the signal had been decomposed by the DHWPT was used as features of a SEMG signal. Then, the feature selection method based on the genetic algorithm and the neural network classifier were employed to provide the best discriminating features of different categories of movement. In the end, a neural network classifier used these selected features to validate the classification performance of the presented method. Compared with other methods of SEMG signals classification, the DHWPT method possessed higher classification accuracy than the time domain method and saved more computational time than the discrete ordinary wavelet packet transform method. So the DHWPT method was an efficient approach to classifying SEMG signals.
The systematic study of muscle fatigue assessment can provide sight into the physiology of the muscle under investigation as well as the mechanisms of fatigue. This dissertation used multifractal method to analyze SEMG signals in the course of muscle fatigue. At present, we have found that the SEMG signals characterized multifractality during static contractions and the area of the multifractal spectrum of the SEMG signals significantly increased during muscle fatigue. Therefore the area could be used as an indicator of assessing muscle fatigue during static contractions. Compared with the MDF which was the most popular indicator for assessing muscle fatigue, the spectrum area presented here showed higher sensitivity. So the singularity spectrum area was considered to be a more effective indicator than the MDF while estimating muscle fatigue during static contractions. During dynamic contractions, the SEMG signals still characterized multifractality. At the same time, we found that the area of the multifractal spectrum of the SEMG signals also increased during muscle fatigue. Hence the area could also be used as an indicator of assessing muscle fatigue during dynamic contractions. However, the slope of the singularity spectrum area during dynamic contractions is smaller than that during static contractions. We thought that the difference between static contractions and dynamic contractions is caused by the blood flow in the contracting muscle.
为了尽可能地提高假肢控制时表面肌电信号分类的准确识别率,我们尝试利用局部判别基方法来对表面肌电信号进行分类,该方法的主要思想是根据可分性度量找出具有最大可分性的部分小波包系数作为特征矢量。但考虑到不同人的不同动作的表面肌电信号的能量大小是有差异的,因此本文提出了基于可分性度量和小波包相对能量的最佳小波包方法,并将该方法应用到表面肌电信号的分类问题当中,实验证明该算法比固定尺度的小波包基方法有着更好的效果。在该方法中我们所使用的可分性度量都只是一些简单的距离度量函数,它们并不能给出模式分类问题中的最佳特征。因此,我们对上述方法进行了改进,提出了DB指标(Davies-Bouldin index)和小波包相对能量相结合的最佳小波包方法,并将该方法应用到了表面肌电信号的分类问题当中。和其它现有的方法相比较,该方法在分类准确率上取得了明显的改进。
为了减少假肢控制时对动作进行分类识别的运算时间,本文提出了一种基于离散谐波小波包变换的表面肌电信号分类方法。首先对信号进行离散谐波小波包分解,然后计算信号在各个频带的相对能量并将其作为表面肌电信号的特征。接着,我们采用基于遗传算法和神经网络分类器的特征选择方法对特征空间进行降维处理从而获得了具有最大可分离性的特征。最后,用BP(back propagation)神经网络和获得的具有最大可分离性的特征来评价被提出方法的分类效果。实验结果表明,离散谐波小波包变换方法获得了比时域方法更高的分类准确率,另一方面,和离散一般小波包变换方法相比,该方法节省了更多的计算时间。因此,离散谐波小波包变换方法为肌电假肢系统准确、实时控制提供了新的可行方法。
肌肉疲劳特性的研究在康复医学、运动医学、人机工效学等领域具有重要作用。本文从多重分形的角度来分析了肌肉疲劳过程中的表面肌电信号。目前,我们已经发现在肌肉的静态疲劳的过程当中,表面肌电信号具有多重分形特性,并且肌电信号奇异谱的面积随着肌肉的疲劳有明显地上升趋势。因此,我们可以把奇异谱面积作为静态收缩时肌肉疲劳的评价指标。实验结果表明,当肌肉处于静态收缩期间时,和传统的肌肉疲劳评价指标中值频率相比,我们提出的肌电信号奇异谱面积显示出了更高的变化斜率,故用奇异谱面积做肌肉疲劳评价指标的灵敏度就要更高,这为量化静态肌肉疲劳提供了一个更为可靠的方法。在肌肉做动态收缩时,表面肌电信号仍然具有多重分形特性,同时我们发现随着肌肉的疲劳,肌电信号的奇异谱面积也在增加,因此奇异谱面积也可以作为动态收缩时肌肉疲劳的评价指标。但是和静态收缩相比,动态收缩时奇异谱面积随时间变化的斜率要小很多。我们推测肌肉静态收缩和动态收缩之间的这种差异可能是由于收缩肌肉内的血液流动所引起的。
Surface electromyographic (SEMG) signals can be monitored noninvasively by using electrodes on the skin surface. They are the summation of all motor unit action potentials (MUAP) within the pick-up area of the electrodes, so they provide information of the neuromuscular activities of the examined muscle. In addition, these signals have been widely applied to clinical diagnosis, sports medicine, ergonomics, rehabilitation medicine, neurophysiology and electrophysiology. Aimed at control system for powered prostheses and muscle fatigue assessment, this dissertation deeply investigated SEMG signals using wavelet transform and nonlinear dynamic method. The main and creative work is as follows:
In order to improve the classification accuracy of SEMG signals in control system for powered prostheses, we tried to use local discriminant bases method to classify SEMG signals and the main idea of this method is to employ wavelet packet decomposition coefficients with maximum class separability as the feature vectors. However, considering that the energy of the SEMG signals varies with different subjects and different movements, this dissertation presented the optimal wavelet packet method based on discriminant measures and relative energy representation of wavelet packet and applied this method to SEMG signals classification. Experimental results showed that this approach achieved higher accuracy than fixed scale wavelet packet method. In this method, the discriminant measures which we used are only some simple distance measure function and they can not give the optimal features for pattern recognition problems. So we improved the above method and presented the optimal wavelet packet method based on Davies-Bouldin (DB) index and relative energy representation of wavelet packet, which was then applied to SEMG signals classification. Compared with other existing methods, this one had significant improvement in classification accuracy.
In order to reduce the computation time of classifying SEMG signals in control system for powered prostheses, this dissertation proposed an efficient method to SEMG signals classification based on the discrete harmonic wavelet packet transform (DHWPT). Firstly, the relative energy of the signal in each frequency band calculated after the signal had been decomposed by the DHWPT was used as features of a SEMG signal. Then, the feature selection method based on the genetic algorithm and the neural network classifier were employed to provide the best discriminating features of different categories of movement. In the end, a neural network classifier used these selected features to validate the classification performance of the presented method. Compared with other methods of SEMG signals classification, the DHWPT method possessed higher classification accuracy than the time domain method and saved more computational time than the discrete ordinary wavelet packet transform method. So the DHWPT method was an efficient approach to classifying SEMG signals.
The systematic study of muscle fatigue assessment can provide sight into the physiology of the muscle under investigation as well as the mechanisms of fatigue. This dissertation used multifractal method to analyze SEMG signals in the course of muscle fatigue. At present, we have found that the SEMG signals characterized multifractality during static contractions and the area of the multifractal spectrum of the SEMG signals significantly increased during muscle fatigue. Therefore the area could be used as an indicator of assessing muscle fatigue during static contractions. Compared with the MDF which was the most popular indicator for assessing muscle fatigue, the spectrum area presented here showed higher sensitivity. So the singularity spectrum area was considered to be a more effective indicator than the MDF while estimating muscle fatigue during static contractions. During dynamic contractions, the SEMG signals still characterized multifractality. At the same time, we found that the area of the multifractal spectrum of the SEMG signals also increased during muscle fatigue. Hence the area could also be used as an indicator of assessing muscle fatigue during dynamic contractions. However, the slope of the singularity spectrum area during dynamic contractions is smaller than that during static contractions. We thought that the difference between static contractions and dynamic contractions is caused by the blood flow in the contracting muscle.
引文
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