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.
| Language | English |
|---|---|
| Pages | 223-228 |
| Number of pages | 6 |
| Journal | Procedia Computer Science |
| Volume | 76 |
| DOIs | |
| Publication status | E-pub ahead of print - 29 Dec 2015 |
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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)
Cite this
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Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron. / Elamvazuthi, I.; Duy, N. H.X.; Ali, Zulfiqar; Su, S. W.; Khan, M. K.A.Ahamed; Parasuraman, S.
In: Procedia Computer Science, Vol. 76, 29.12.2015, p. 223-228.Research output: Contribution to journal › Article
TY - JOUR
T1 - Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron
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
PY - 2015/12/29
Y1 - 2015/12/29
N2 - 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.
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.
KW - Autoregressive method (AR)
KW - Classification
KW - Electromyography (EMG)
KW - Feature Extraction
KW - Multilayer Perceptron (MLP)
KW - Neuromuscular Disease
KW - Root mean square (RMS)
KW - Waveform length (WL) and Mean Absolute Value (MAV)
KW - Zero Crossing (ZC)
UR - http://www.scopus.com/inward/record.url?scp=84962834084&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2015.12.346
DO - 10.1016/j.procs.2015.12.346
M3 - Article
VL - 76
SP - 223
EP - 228
JO - Procedia Computer Science
T2 - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -