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Unsupervised Bayesian EMG decomposition algorithm using tabu search

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4 Author(s)
Di Ge ; Institut de Recherche en Communications et Cybernétique de Nantes (UMR CNRS 6597), Ecole Centrale de Nantes, Université de Nantes, CNRS, 1, rue de la Noë, BP 92101, F44321 Cedex 3, France ; Eric Le Carpentier ; Dario Farina ; Jerome Idier

We introduce here the statistical model of the electromyographic (EMG) signal decomposition problem and then propose an algorithm with a Bayesian approach followed by its simulation data test results. We consider the classical problem of the multi-source impulse discharge separation from intramuscular EMG signals in the case where the impulse responses (motor unit action potentials, MUAPs) are supposed to be known to a certain degree. The main contribution of this work is the proposal of a fully unsupervised EMG decomposition algorithm that exploits both the signal model likelihood and the regularity of the motor unit discharge patterns in a Bayesian framework. The latter, though well-proven properties in the past, is essentially used as auxiliary information in an interactive procedure [1] involving human interventions. Another contribution consists of using the Tabu metaheuristics to solve the NP-hard problem over the complete search space of overlapped MUAP, unlike the existing methods that either performed on the restrained search spaces [2, 3] due to complexity or based on recursive algorithms [4, 1] with certain trial strategy and residual threshold estimations.

Published in:

2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies

Date of Conference:

25-28 Oct. 2008