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 | |
| 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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Cite this
Elamvazuthi, I., Duy, N. H. X., Ali, Z., Su, S. W., Khan, M. K. A. A., & Parasuraman, S. (2015). Electromyography (EMG) based Classification of Neuromuscular Disorders using Multi-Layer Perceptron. Procedia Computer Science, 76, 223-228. https://doi.org/10.1016/j.procs.2015.12.346