Zhong Jin, Shi Jun, Cai Yin, Zhang Qi
School of Communication and Information Engineering, Shanghai University, Shanghai, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4100-3. doi: 10.1109/IEMBS.2011.6091018.
The rough entropy (RoughEn) is developed based on the rough set theory. It has the advantage of low computational complexity, because there is no parameter to set in RoughEn. In this paper, we characterized the feature of surface electromyography (SEMG) signal with RoughEn and then used support vector machine to classify six different hand motions. The sample entropy, wavelet entropy and approximate entropy were compared with RoughEn to evaluate the performance of characterizing SEMG signals. The experimental results indicated that the RoughEn-based classification outperformed other entropy based methods for recognizing six hand motions from four-channel SEMG signals with the best recognition accuracy of 95.19 ± 2.99%. The results suggest that RoughEn has the potential to be used in the SEMG-based prosthetic control as a method of feature extraction.
粗糙熵（RoughEn）是基于粗糙集理论发展而来的。它具有计算复杂度低的优点，因为在粗糙熵中无需设置参数。在本文中，我们用粗糙熵表征表面肌电信号（SEMG）的特征，然后使用支持向量机对六种不同的手部动作进行分类。将样本熵、小波熵和近似熵与粗糙熵进行比较，以评估表征SEMG信号的性能。实验结果表明，基于粗糙熵的分类在从四通道SEMG信号识别六种手部动作方面优于其他基于熵的方法，最佳识别准确率为95.19±2.99%。结果表明，粗糙熵作为一种特征提取方法，有潜力应用于基于SEMG的假肢控制。
