Comparison of the techniques used for sgmentation of EMG signals
MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering
Multi-class support vector machine classifier in EMG diagnosis
WSEAS Transactions on Signal Processing
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This paper presents a new approach to the decomposition of electromyographic (EMG) signals. EMG signals consist of a superposition of delayed finite-duration waveforms that carry the information about the firing of different muscle fiber groups. The new approach is based on a communication technical interpretation of the EMG signal. The source is modeled as a signaling system with intersymbol-interference, which encodes a well defined sparse information sequence. This point of view allows a maximum-likelihood (ML) as well as a maximum a posteriori (MAP) estimation of the underlying firing pattern to be made. The high accuracy attainable with the proposed method is illustrated both with measured and artificially generated EMG signals