基于自排序熵的表面肌电信号特征提取方法

摘要

针对人体表面肌电信号的非平稳、非线性特点，提出一种基于排序熵和自互信息的自排序熵指标，定量描述表面肌电信号的内在动力学特性，实现肢体不同运动状态下肌电信号非线性特征的有效刻画。进行健康受试者上肢肘关节不同弯曲角度下表面肌电采集实验，计算其自排序熵指标并运用支持向量机进行动作识别，通过与已有表面肌电特征指标的对比分析，验证文中方法的有效性。%Due to the nonstationarity and the nonlinearity of the surface electromyogram (sEMG), a method based on the permutation entropy and auto mutual information is proposed to quantitatively describe the internal dynamic features of the sEMG and realize the description of nonlinear characteristics under different motion states. Experiments are carried out to acquire the sEMG data of elbow joint at different bending angles. The auto permutation entropies of the signals are calculated and used as the inputs of support vector machines to identify different motion states. The validity of the proposed method is verified by the comparative analysis of auto permutation entropy and other indexes describing the sEMG features.