Classification of surface EMG signals using harmonic wavelet packet transform
Classification of surface EMG signals using harmonic wavelet packet transform
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DOI：
10.1088/0967-3334/27/12/001
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发表时间：
2006-12-01
影响因子：
3.2
通讯作者：
Wang, Zhizhong
中科院分区：
文献类型：
--
作者：
Wang, Gang;Yan, Zhiguo;Wang, Zhizhong
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。在DHWPT提取了表面肌电信号在各频段的相对能量后，利用遗传算法选择合适的特征对特征进行降维。然后，将选择的特征作为神经网络分类器的输入向量，以区分四种类型的假肢运动。与其他分类方法相比，该方法在实验研究中具有较高的分类精度。此外，该方法还可以节省大量的计算时间，因为DHWPT具有基于快速傅立叶变换的快速算法用于数值实现。
In this paper, an efficient method based on the discrete harmonic wavelet packet transform (DHWPT) is presented to classify surface electromyographic (SEMG) signals. After the relative energy of SEMG signals in each frequency band had been extracted by the DHWPT, a genetic algorithm was utilized to select appropriate features in order to reduce the feature dimensionality. Then, the selected features were used as the input vectors to a neural network classifier to discriminate four types of prosthesis movements. Compared with other classification methods, the proposed method provided high classification accuracy in experimental research. In addition, this method could also save a lot of computational time because the DHWPT has a fast algorithm based on the fast Fourier transform for numerical implementation.