Name
Abstract | ||
|---|---|---|
A method for the extraction and classification of individual motor unit action potentials (MUAPs) from needle electromyographic signals is presented. The proposed method automatically decomposes MUAPs and classifies them into normal, neuropathic or myopathic using a two-stage feature-based classifier. The method consists of four steps: (i) preprocessing of EMG recordings, (ii) MUAP clustering and detection of superimposed MUAPs, (iii) feature extraction and (iv) MUAP classification using a two-stage classifier. The proposed method employs Radial Basis Function Artificial Neural Networks and decision trees. It requires minimal use of tuned parameters and is able to provide interpretation for the classification decisions. The approach has been validated on real EMG recordings and an annotated collection of MUAPs. The success rate for MUAP clustering is 96%, while the accuracy for MUAP classification is about 89%. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.compbiomed.2006.11.010 | Comp. in Bio. and Med. |
Keywords | Field | DocType |
Quantitative electromyography,Electromyogram decomposition,MUAP detection and classification,Radial basis function network,Decision trees | Decision tree,Computer vision,Radial basis function network,Pattern recognition,Computer science,Motor unit,Artificial intelligence | Journal |
Volume | Issue | ISSN |
37 | 9 | 0010-4825 |
Citations | PageRank | References |
11 | 0.84 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos D. Katsis | 1 | 33 | 2.44 |
| Themis P Exarchos | 2 | 235 | 24.31 |
| Costas Papaloukas | 3 | 255 | 16.43 |
| Y. Goletsis | 4 | 126 | 16.41 |
| Dimitrios I. Fotiadis | 5 | 941 | 121.32 |
| Ioannis Sarmas | 6 | 11 | 0.84 |