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

I. Elamvazuthi; N. H.X. Duy; Zulfiqar Ali; S. W. Su; M. K.A.Ahamed Khan; S. Parasuraman

doi:10.1016/j.procs.2015.12.346

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

I. Elamvazuthi, N. H.X. Duy, Zulfiqar Ali, S. W. Su, M. K.A.Ahamed Khan, S. Parasuraman

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

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.

Original language	English
Pages (from-to)	223-228
Number of pages	6
Journal	Procedia Computer Science
Volume	76
DOIs	https://doi.org/10.1016/j.procs.2015.12.346
Publication status	E-pub ahead of print - 29 Dec 2015

Keywords

Autoregressive method (AR)
Classification
Electromyography (EMG)
Feature Extraction
Multilayer Perceptron (MLP)
Neuromuscular Disease
Root mean square (RMS)
Waveform length (WL) and Mean Absolute Value (MAV)
Zero Crossing (ZC)

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10.1016/j.procs.2015.12.346

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Cite this

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title = "Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron",

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.",

keywords = "Autoregressive method (AR), Classification, Electromyography (EMG), Feature Extraction, Multilayer Perceptron (MLP), Neuromuscular Disease, Root mean square (RMS), Waveform length (WL) and Mean Absolute Value (MAV), Zero Crossing (ZC)",

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AU - Elamvazuthi, I.

AU - Duy, N. H.X.

AU - Ali, Zulfiqar

AU - Su, S. W.

AU - Khan, M. K.A.Ahamed

AU - Parasuraman, S.

N1 - Author started at Ulster 2019

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AB - 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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