title: Characterizing EMG data using machine-learning tools.
abstract: 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 neuromuscular pathology. The techniques studied are grouped by their methodology, and a summary of the salient findings associated with each method is presented.
authors.affiliation: School of Computer Science, University of Guelph, Guelph, ON, Canada. Electronic address: jyousefi@uoguelph.ca.
authors.forename: Jamileh
authors.initials: J
authors.lastname: Yousefi
authors.affiliation: Mathematics and Computer Science, Mount Allison University, Sackville, NB, Canada; School of Computer Science, University of Guelph, Guelph, ON, Canada. Electronic address: ahamiltonwright@mta.ca.
authors.forename: Andrew
authors.initials: A
authors.lastname: Hamilton-Wright
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D009468:Neuromuscular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted
keywords: Classification; EMG characterization; EMG electromyography; Machine learning; Myopathy; Neuromuscular disease; Neuropathy