Neural networks in computer aided clinical electromyography
Date
1991ISBN
0-7803-0216-8Publisher
Publ by IEEESource
Proceedings of the Annual Conference on Engineering in Medicine and BiologyProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Volume
13Pages
1458-1459Google Scholar check
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In concentric needle electromyography, quantitative measurements are applied on the motor unit action potentials, which are recorded from the biceps muscle of normal subjects and patients suffering from neuromuscular disorders. An unsupervised learning neural network is employed for the classification of neuromuscular disorders. The results suggest that unsupervised learning has certain advantages in cases where the classes of the training data are unknown in number, or are not easily separated. Higher diagnostic yield is achieved through unsupervised learning, when compared to supervised learning. The significant finding, however, is not the higher diagnostic yield, but the flexibility offered by the unsupervised learning artificial neural networks for producing subcategories of various diseases that cannot be seen by simply studying the motor unit action potentials.