基于蚁群算法的表面肌电信号特征选择

摘要

Objective To improve the classification performance of the surface electromyography (Semg) -based prosthesis and reduce the dimensions of features extracted from the Semg signals, a modified ant colony optimization (ACO) was employed to select the best feature subset. Methods The relationship between features and target classes was calculated as the heuristic function and the best feature subset was selected by ACO, and the trained artificial nerve net was utilized to verify the classification performance. Results Ten healthy subjects participated in the experiment on classification of hand and wrist motion using Semg signals. Compared to the principle component analysis (PCA) -based feature subsets, the ACO-reduced feature subsets not only improved the classification accuracy but greatly reduced the number of features in the original feature set, which subsequently simplified the structure of the classifier and reduced the computational cost. Conclusions The proposed method exhibits a great potential in the real-time applications, such as Semg-based prosthesis control.%目的为提高假肢系统对动作信号的识别速度,设计了基于优化蚁群算法(ant colony optimization,ACO)的特征选择法,对表面肌电信号(surface electromyography,sEMG)高维特征向量降维以减少计算负担.方法 以特征与目标类型之间互信息关系作为启发函数,通过蚁群算法选出最佳特征子集,最后用已训练好的人工神经网络检验其分类性能.结果 对10名健康受试者进行了手腕部动作的肌电信号模式分类实验.与传统主成分分析法(principle component analysis,PCA)相比,该算法选出的特征子集提高了识别准确率,并显著降低了原始特征集的特征维数,进而简化分类器的结构,减少计算开销.结论 本方法在实时性要求高的肌电控制假肢等系统中具有良好的应用前景.