USING NEURAL NETWORKS TO IMPROVE CLASSIFICATION - APPLICATION TO BRAIN MATURATION

The knowledge acquisition problem is one of the most difficult issues in elaborating a medical expert system. This is more true in the context of automated brain signal diagnosis. This kind of knowledge does not lend itself to be represented in a classical rule-based system and is not easily put in quantitative terms by the specialists. Artificial neural networks (ANNs) provide a useful alternative for capturing this information. In this work, an application of ANNs to brain maturation prediction is presented. The problem is essentially a supervised classification. A case data base consisting of data extracted from electroencephalographic (EEG) signals and diagnoses carried out by an expert neurologist serves to test the ability of several statistical classifiers and several kinds of ANNs in reproducing the expert results. There is also a discussion on how to integrate ANNs in a higher-level knowledge-based system for brain signal interpretation.