The current standard method (radioscintigraphy) for the diagnosis of delayed gastric emptying (GE) of a solid meal involves radiation exposure and considerable expense. Based on combining genetic algorithms with the cascade correlation learning architecture, a neural network approach is proposed for the diagnosis of delayed GE from electrogastrograms (EGGs). EGGs were measured by placing surface electrodes on the abdominal skin over the stomach in 152 patients with suspected gastric motility disorders for 30 min in the fasting state and for 2 h after a standard test meal. The GE rate of the stomach was simultaneously monitored after the meal using radioscintigraphy. Five spectral parameters of EGG data in each patient were used as the inputs to a classifier. The classifier was designed by using genetic algorithms in conjunction with the cascade correlation learning architecture. The main advantage of this technique over the back-propagation (BP) for supervised learning is that it can automatically develop the architecture of neural networks to give a suitable network size for a specific problem. The resulted neural network with three hidden units exhibits 83% correct classification for the EGG data, and has comparable performance with the BP network. This study demonstrates the potential of the neural network approach based on combined genetic algorithms with cascade correlation for diagnosis of gastric emptying from the EGG. Copyright (C) 2000 IPEM.
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