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

Lauer, Richard T.; Stackhouse, Carrie; Shewokis, Patricia A.; Smith, Brian T.; Tucker, Carole A.; McCarthy, James J.

doi:10.1016/j.gaitpost.2006.10.015

Cited by 35 publications

(33 citation statements)

References 40 publications

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“…A number of multivariate statistical methods have been developed for dealing with the complexity and interdependence of gait data [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. While some of these methods focus primarily on identifying gait patterns and relationships among variables, several aim to develop either joint-specific or overall indexes of gait pathology [7,8,10,[12][13][14]19].…”

Section: Introductionmentioning

confidence: 99%

The gait deviation index: A new comprehensive index of gait pathology

2008

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Section: Introductionmentioning

confidence: 99%

The gait deviation index: A new comprehensive index of gait pathology

2008

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“…However, instead of the commonly used frequency transform of sEMG, we resorted to time-scale representation [12,26,27,35,36]. Although it has already been applied to various electrophysiological signals for diagnosis of different pathologies, such as Parkinson's disease [5], cerebral palsy [21] and lower back pain [28], it has never been combined with the entropy measures to investigate the amplitude distributions at different dyadic scales.…”

Section: Introductionmentioning

confidence: 99%

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

Istenič

Kaplanis

Pattichis

et al. 2010

Med Biol Eng Comput

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We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.

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“…The use of this method to investigate dynamic muscle dysfunction in children with cerebral palsy has lead to clear distinctions between this population and the normally developing group (Wakeling et al 2007). Lauer et al (2007) expanded on this and were able to show differences in levels of cocontraction between the less and more involved side.…”

Section: Introductionmentioning

confidence: 89%