Neural network analysis of the EMG interference pattern

✍ Scribed by E.W. Abel; P.C. Zacharia; A. Forster; T.L. Farrow

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 696 KB
• Volume: 18
• Category: Article
• ISSN: 1350-4533
• DOI: 10.1016/1350-4533(95)00032-1

✦ Synopsis

This paper investigates the perfrmance of artzjicial neural networks for analysing and classifying EMG signals from healthy subjects and patients with myopathic and neuropathic disorders. EMG interference pabrns (IP) were recorded under maximum voluntaq con&action from the right biceps of a total of 50 subjects. Parameters were obtained from the signals using recognized quantification techniques including turns analysis, small segments analysis and frequency analysis. Supervised networks examined were an improved backpropagation network (IBPN), a radial basis network (RBN), and a learning vector quantization network (LVQJ Supervised networks using different combinations of parameters from turns analysis and small segments analysis gave diagnostic yields of 60-80%. Combinations using frequency analysis parameters produced similar results. The performance of unsuperoised Self-Organising Feature Maps (SOFM)

was generally lower than that of the supervised networks. Including personal data (sex and age) did not improve the overall performance.