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A time–frequency based electromyographic analysis technique for use in cerebral palsy
Journal article   Peer reviewed

A time–frequency based electromyographic analysis technique for use in cerebral palsy

Richard T. Lauer, Carrie A. Stackhouse, Patricia A. Shewokis, Brian T. Smith, Carole A. Tucker and James McCarthy
Gait & posture, v 26(3), pp 420-427
2007
DOI: https://doi.org/10.1016/j.gaitpost.2006.10.015
PMID: 17161603
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Cerebral palsy Electromyography Motion analysis Wavelet analysis
Surface electromyography (sEMG) is part of an instrumented gait assessment, however, the interpretation of the data in a clinically meaningful manner is often limited to the extraction of individual sEMG characteristics. The purpose of this study was to develop an assessment methodology using sEMG time and frequency characteristics extracted using wavelet analyses to provide clinically relevant information in children with cerebral palsy (CP). A retrospective study was conducted with 37 children (16 children with typical development (TD) and 21 children with spastic CP). sEMG signals were examined from selected musculature of the lower extremities during level ground walking. Wavelet analysis techniques, along with functional principal component analyses, were employed to calculate a sEMG index. The data indicated a grouping in the EMG index based on the level of motor impairment and the clinical diagnosis of spastic hemiplegia or diplegia. Further analyses of the index exhibited moderate to high ( r = −0.43 to −0.74 and r = 0.62–0.65) correlations with the existing gait kinetics, kinematics, and clinical measures of motor impairment, and was sensitive to walking ability according to the Gross Motor Functional Classification Scale (GMFCS). Overall, this methodology may have the potential to provide additional insight into the outcome of a clinical intervention that was not available previously, and may find use as a predictive tool that can be utilized for clinical decision making.

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29 citations in Web of Science
32 citations in Scopus

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Collaboration types
Domestic collaboration
Web of Science research areas
Neurosciences
Orthopedics
Sport Sciences
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