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Feature selection for motor unit potential train characterization
Author(s) -
Abdelmaseeh Meena,
Smith Benn,
Stashuk Daniel
Publication year - 2014
Publication title -
muscle and nerve
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.025
H-Index - 145
eISSN - 1097-4598
pISSN - 0148-639X
DOI - 10.1002/mus.23977
Subject(s) - motor unit , pattern recognition (psychology) , feature (linguistics) , feature selection , artificial intelligence , stability (learning theory) , computer science , selection (genetic algorithm) , neuroscience , machine learning , psychology , linguistics , philosophy
: Ten new features of motor unit potential (MUP) morphology and stability are proposed. These new features, along with 8 traditional features, are grouped into 5 aspects: size, shape, global complexity, local complexity, and stability. Methods : We used sequential forward and backward search strategies to select subsets of these 18 features to discriminate accurately between muscles whose MUPs are predominantly neurogenic, myopathic, or normal. Results and Conclusions : Results based on 8102 motor unit potential trains (MUPTs) extracted from 4 different limb muscles ( n = 336 total muscles) demonstrate the usefulness of these newly introduced features and support an aspect‐based grouping of MUPT features. Muscle Nerve 49 : 680–690, 2014