Hybrid neural network electromyographic system: Incorporating the WISARD net
Date
1994Publisher
IEEESource
IEEE International Conference on Neural Networks - Conference ProceedingsProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
Volume
6Pages
3478-3483Google Scholar check
Keyword(s):
Metadata
Show full item recordAbstract
Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks trained with the backpropagation, the Kohonen's self-organizing feature maps algorithm, and the genetics based machine learning (GBML) in classifying EMG data has recently been demonstrated. A hybrid diagnostic system was also introduced that combines the above neural network and GBML models. In this paper the WISARD net is applied on the same set of EMG data. The WISARD (Wilkie, Stonham, Aleksander Recognition Device) is an implementation in hardware or software of an n-tuple sampling technique. Results suggest that although the diagnostic performance of the WISARD models is of the order of 80%, that being comparable to the above mentioned three systems, training time has been significantly reduced. In addition, the hardware or software implementation of the WISARD net is simpler than the other three systems.