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Characterizing EMG data using machine-learning tools
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DOI：10.1016/j.compbiomed.2014.04.018.png)
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Effective electromyographic (EMG) signal characterization is critical in the diagnosis of neuromuscular disorders. Machine-learning based pattern classification algorithms are commonly used to produce such characterizations. Several classifiers have been investigated to develop accurate and computationally efficient strategies for EMG signal characterization. This paper provides a critical review of some of the classification methodologies used in EMG characterization, and presents the state-of-the-art accomplishments in this field, emphasizing neuro-muscular pathology. The techniques studied are grouped by their methodology, and a summary of the salient findings associated with each method is presented. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
EMG electromyography
EMG characterization
Machine learning
Classification
Neuromuscular disease
Myopathy
Neuropathy
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