Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron

Elamvazuthi, I; Duy, NHX; Ali, Z; Su, SW; Khan, MKAA; Parasuraman, S

Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron

Elamvazuthi, I Duy, NHX Ali, Z Su, SW

Khan, MKAA Parasuraman, S

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Publication Type:: Conference Proceeding
Citation:: Procedia Computer Science, 2015, 76 pp. 223 - 228
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Elamvazuthi, I	en_US
dc.contributor.author	Duy, NHX	en_US
dc.contributor.author	Ali, Z	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Khan, MKAA	en_US
dc.contributor.author	Parasuraman, S	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2015, 76 pp. 223 - 228	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123101
dc.description.abstract	Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2015.12.346	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron	en_US
dc.type	Conference Proceeding
utslib.citation.volume	76	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	10 Technology	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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	76	en_US

Abstract:

Electromyography (EMG) signals are the measure of activity in the muscles. The aim of this study is to identify the neuromuscular disease based on EMG signals by means of classification. The neuromuscular diseases that have been identified are myopathy and neuropathy. The classification was carried out using Artificial Neural Network (ANN). There are five feature extraction techniques that were used to extract the signals such as Autoregressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Waveform length (WL) and Mean Absolute Value (MAV). A comparative analysis of these different techniques were carried out based on the results. The Multilayer Perceptron (MLP) was used for carrying out the classification.

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http://hdl.handle.net/10453/123101