基于非线性尺度小波变换的表面肌电信号的分类

表面肌电信号（Surface EMG,sEMG）是一种复杂的非线性非平稳信号。我们介绍了一种非线性尺度小波变换（Wavelet transform with nonlinear scale，NWT）。由于NWT具有渐进缩短时间分辨率的特点．所以有利于从sEMG信号获得精确的时一频信息。首先，用NWT将sEMG信号（30组前臂内旋和30组外旋的sEMG信号）变换为强度分布（时频分布）．然后，用由主成分分析获得的强度分布特征值构成特征向量．最后，用BP神经网络对两种信号模式的特征向量进行分类识别。结果表明：与两种传统的时频分析方法相比，NWT能够获得较高的正确识别率．同时降低了神经网络计算的复杂度。

Classification of Surface EMG Signal Based on Wavelet Transform with Nonlinear Scale

Surface EMG (sEMG) signal is a complex nonlinear, non-stationary signal. In this paper, wavelet transform with nonlinear scale (NWT) is introduced. Due to the gradual shortening of its time-resolution, NWT is good at extracting the precise time-frequency information from sEMG signal. First, every sEMG signal (30 sets are for forearm supination and 30 sets are for forearm pronation) is transformed into intensity distribution (time-frequency distribution) by NWT. And then the feature vector is determined from the characteristic roots which are obtained from the intensity distribution by principle component analysis. At last, the two patterns of sEMG signals are identified by BP neural network. The results show that the accurate classification rate is higher gained by NWT than by two conventional time-frequency distributions. At the same time, the calculating complexity of neural network is decreased greatly.