Abstract：To increase the recognition accuracy of action surface electromyography (SEMG) signal, a method combining the maximal Lyapunov exponent and multiscale analysis was proposed. Considering the nonlinear and nonstationary characteristic of SEMG, a multi-scale maximal Lyapunov exponent (MSMLE) feature was introduced and applied to the pattern recognition of six types forearm action SEMG signals. First step was to decomposite original signal using HilbertHuang transform (HHT), known as multiscale decomposition. Then, MSMLE was calculated by nonlinear time series analysis method. At last, eigenvector MSMLE was input into support vector machine (SVM) for recognition. The mean recognition accuracy reached 97.5%, which was 3.9% greater than that obtained from maximal Lyapunov exponent of original signal. Results showed that the proposed method was effective and precise in the pattern recognition of action SEMG signals.
邹晓阳 雷敏*. 基于多尺度最大李雅普诺夫指数的表面肌电信号模式识别[J]. 中国生物医学工程学报, 2012, 31(1): 7-12.
ZOU Xiao Yang LEI Min*. Pattern Recognition of Surface Electromyography Signal Based on Multi-Scale Maximal Lyapunov Exponent. journal1, 2012, 31(1): 7-12.