Yan Zhiguo, Wang Zhizhong, Xie Hongbo
Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China.
Comput Methods Programs Biomed. 2008 Jun;90(3):275-84. doi: 10.1016/j.cmpb.2008.01.003. Epub 2008 Mar 4.
This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the successive and non-overlapped sub-bands. The energy characteristic of each sub-band is adopted to construct the initial full feature set. For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy. Compared with the extensively used feature reduction methods such as principal component analysis (PCA), sequential forward selection (SFS) and backward elimination (BE) etc., the comparison experiments demonstrate its superiority in terms of time-consuming and classification accuracy. The proposed strategy of feature extraction and reduction is a kind of filter-based algorithms which is independent of the classifier design. Considering the classification performance will vary with the different classifiers, we make the comparison between the fuzzy least squares support vector machines (LS-SVMs) and the conventional widely used neural network classifier. In the further study, our experiments prove that the combination of MI-based feature selection and SVM techniques outperforms other commonly used combination, for example, the PCA and NN. The experiment results show that the diverse motions can be identified with high accuracy by the combination of MI-based feature selection and SVM techniques. Compared with the combination of PCA-based feature selection and the classical Neural Network classifier, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and MI in EMG motion classification.
本文提出了一种基于互信息的有效特征选择方法，用于基于肌电图（EMG）的运动分类任务。利用小波包变换（WPT）将四类运动EMG信号分解为连续且不重叠的子带。采用每个子带的能量特征构建初始完整特征集。为降低计算复杂度，利用互信息（MI）理论在不降低分类精度的情况下获得简化特征集。与广泛使用的特征约简方法（如主成分分析（PCA）、顺序前向选择（SFS）和后向消除（BE）等）相比，对比实验证明了其在耗时和分类精度方面的优越性。所提出的特征提取和约简策略是一种基于滤波器的算法，与分类器设计无关。考虑到分类性能会因不同的分类器而有所不同，我们对模糊最小二乘支持向量机（LS-SVMs）和传统广泛使用的神经网络分类器进行了比较。在进一步的研究中，我们的实验证明基于MI的特征选择和支持向量机技术的组合优于其他常用组合，例如PCA和神经网络（NN）。实验结果表明，基于MI的特征选择和支持向量机技术的组合能够高精度地识别不同的运动。与基于PCA的特征选择和经典神经网络分类器的组合相比，所提出的分类方案的优越性能说明了支持向量机技术与WPT和MI相结合在EMG运动分类中的潜力。
