A new method for the extraction and classification of single motor unit action potentials from surface EMG signals.

It has been shown that multi-channel surface EMG allows assessment of anatomical and physiological single motor unit (MU) properties. To get this information, the action potentials of single MUs should be extracted from the interference EMG signals. This study describes an automatic system for the detection and classification of MU action potentials from multi-channel surface EMG signals. The methods for the identification and extraction of action potentials from the raw signals and for their clustering into the MUs to which they belong are described. The segmentation phase is based on the matched Continuous Wavelet Transform (CWT) while the classification is performed by a multi-channel neural network that is a modified version of the multi-channel Adaptive Resonance Theory networks. The neural network can adapt to slow changes in the shape of the MU action potentials. The method does not require any interaction of the operator. The technique proposed was validated on simulated signals, at different levels of force, generated by a structure based surface EMG model. The MUs identified from the simulated signals covered almost the entire recruitment curve. Thus, the proposed algorithm was able to identify a MU sample representative of the muscle. Results on experimental signals recorded from different muscles and conditions are reported, showing the possibility of investigating anatomical and physiological properties of the detected MUs in a variety of practical cases. The main limitation of the approach is that complete firing patterns can be obtained only in specific cases due to MU action potential superpositions.