Zhang Xu, Zhou Ping
Biomedical Engineering Program, University of Science and Technology of China, Hefei, Anhui, China.
Biomedical Engineering Program, University of Science and Technology of China, Hefei, Anhui, China Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, and TIRR Memorial Hermann Research Center, Houston, Texas, USA.
J Healthc Eng. 2014;5(3):261-73. doi: 10.1260/2040-2295.5.3.261.
This study presents a novel feature extraction method for myoelectric pattern recognition using a multivariate extension of empirical mode decomposition (EMD), namely multivariate EMD (MEMD). The method processes multiple surface electromyogram (EMG) channels simultaneously rather than in a channel-by-channel manner. From mode-aligned intrinsic mode functions (IMFs, representing signal components over multiple scales) derived from the MEMD analysis, normalized amplitude distributions of the same-mode/scale IMFs across different channels were calculated as features, which serve to reveal the underlying relationship in the aligned intrinsic scales across multiple muscles. The proposed method was assessed for identification of 18 different functional movement patterns via 27-channel surface EMG signals recorded from the paretic forearm muscles of 12 subjects with hemiparetic stroke. With a linear discriminant classifier, the proposed MEMD based feature set resulted in an average error rate of 4.61 ± 4.70% for classification of all the different movements, significantly lower than that of the conventional time-domain feature set (7.14 ± 6.15%, p < 0.05). The results indicate that the MEMD based feature extraction of multi-channel surface EMG data provides a promising approach to modeling of muscle couplings and identification of different myoelectric patterns.
本研究提出了一种用于肌电模式识别的新型特征提取方法，该方法使用经验模态分解（EMD）的多元扩展，即多元经验模态分解（MEMD）。该方法同时处理多个表面肌电图（EMG）通道，而不是逐个通道处理。从MEMD分析得出的模式对齐本征模态函数（IMF，代表多个尺度上的信号分量）中，计算不同通道上同模式/尺度IMF的归一化幅度分布作为特征，这些特征用于揭示多个肌肉在对齐本征尺度上的潜在关系。通过从12名偏瘫中风患者的患侧前臂肌肉记录的27通道表面EMG信号，对所提出的方法进行了评估，以识别18种不同的功能运动模式。使用线性判别分类器，基于MEMD提出的特征集在所有不同运动分类中的平均错误率为4.61±4.70%，显著低于传统时域特征集的错误率（7.14±6.15%，p<0.05）。结果表明，基于MEMD的多通道表面EMG数据特征提取为肌肉耦合建模和不同肌电模式识别提供了一种有前景的方法。
