Institute of Information Theories and Applications FOI ITHEA
Abstract:
Neural Networks have been successfully employed in different biomedical settings. They have been
useful for feature extractions from images and biomedical data in a variety of diagnostic applications. In this
paper, they are applied as a diagnostic tool for classifying different levels of gastric electrical uncoupling in
controlled acute experiments on dogs. Data was collected from 16 dogs using six bipolar electrodes inserted into
the serosa of the antral wall. Each dog underwent three recordings under different conditions: (1) basal state, (2)
mild surgically-induced uncoupling, and (3) severe surgically-induced uncoupling. For each condition half-hour
recordings were made. The neural network was implemented according to the Learning Vector Quantization
model. This is a supervised learning model of the Kohonen Self-Organizing Maps. Majority of the recordings
collected from the dogs were used for network training. Remaining recordings served as a testing tool to examine
the validity of the training procedure. Approximately 90% of the dogs from the neural network training set were
classified properly. However, only 31% of the dogs not included in the training process were accurately
diagnosed. The poor neural-network based diagnosis of recordings that did not participate in the training process
might have been caused by inappropriate representation of input data. Previous research has suggested
characterizing signals according to certain features of the recorded data. This method, if employed, would reduce
the noise and possibly improve the diagnostic abilities of the neural network.