Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders

Naik, GR; Selvan, SE; Nguyen, HT

Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders

Naik, GR

Selvan, SE Nguyen, HT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24 (7), pp. 734 - 743
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Selvan, SE	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24 (7), pp. 734 - 743	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37225
dc.description.abstract	© 2001-2011 IEEE. An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2015.2454503	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Neuromuscular Diseases	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Data Interpretation, Statistical	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Muscle Contraction	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Young Adult	en_US
dc.title	Single-channel EMG classification with ensemble-empirical-mode-decomposition-based ICA for diagnosing neuromuscular disorders	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	24	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

© 2001-2011 IEEE. An accurate and computationally efficient quantitative analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Since it is often the case that the measured signals are the mixtures of electric potentials that emanate from surrounding muscles (sources), many EMG signal processing approaches rely on linear source separation techniques such as the independent component analysis (ICA). Nevertheless, naive implementations of ICA algorithms do not comply with the task of extracting the underlying sources from a single-channel EMG measurement. In this respect, the present work focuses on a classification method for neuromuscular disorders that deals with the data recorded using a single-channel EMG sensor. The ensemble empirical mode decomposition algorithm decomposes the single-channel EMG signal into a set of noise-canceled intrinsic mode functions, which in turn are separated by the FastICA algorithm. A reduced set of five time domain features extracted from the separated components are classified using the linear discriminant analysis, and the classification results are fine-tuned with a majority voting scheme. The performance of the proposed method has been validated with a clinical EMG database, which reports a higher classification accuracy (98%). The outcome of this study encourages possible extension of this approach to real settings to assist the clinicians in making correct diagnosis of neuromuscular disorders.

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http://hdl.handle.net/10453/37225