[Study on the classification of motor unit action potentials from single-channel surface EMG signal based on the wavelet analysis].

A method of motor unit action potentials (MUAP) detection and classification was introduced to explore the firing information of recruited motor units in the neural muscular system. Based on the continuous wavelet transform, the first order Hermite-Rodriguez (HR) function was used as the mother wavelet, and the binary hypothesis testing algorithm was combined to detect and localize the MUAP waveforms in the surface electromyography (sEMG) signal. Then, the fuzzy k-means clustering and minimum distance classifying algorithms were applied to the primary clustering of the detected MUAPs. Finally, the template matching method was used to solve the problem of the unclassified waveforms. The experimental results showed that the kinds of MUAP information from the recorded sEMG signal could be acquired by waveform detection and pattern recognition. The proposed method does not require multi-channel sEMG signals; it just utilizes the single channel signal to analyze the MUAPs, and it can improve the decomposition efficiency.