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Optimization of EMG-signal Source Classification based on Adaptive Wavelets K-mean Algorithm

In this paper the optimization of EMG signals segmentation and decomposition based on wavelet representation and k-mean clustering technique is presented. It is shown that wavelet decomposition can be usefull in detecting particular spikes in EMG signals and the presented segmentation algorithm may be useful for the detection of active segments in related MUAP’s action potentials. The algorithms has been tested on the synthetic model signal and on real signals recorded with intramuscular multi-point electrode. The efficiency of EMG signal decomposition and classification with adaptive wavelet algorithm were presented. Single and multiple fibers MUAP patterns were tested and identified. By applying a Debauchies wavelet transformation and k-mean clustering algorithm to localize the action-potential source in the presence of specific neuromuscular diseases like NMI neuropathy, muscular dystrophy and myasthenia gravis (MG), instead of many decomposition and pattern recognition algorithm, wavelets and k-mean clustering have its flexibility for robustly classify and localize the signal stochastic sources with a linear way, in addition to identify the blind source for EMG bioelectric potential.