This thesis is centered around the development and application of computationally effective solutions based on artificial neural networks (ANN) for biomedical signal analysis and data mining in medical records. It is important to recall that the ultimate goal of this work in the field of Biomedical Engineering is to provide the clinician with the best possible information needed to make an accurate diagnosis (in our case of myocardial ischemia) and

Complex application domains involve difficult pattern classification problems. The state space of these problems consists of regions that lie near class separation boundaries and require the construction of complex discriminants while for the rest regions the classification task is significantly simpler. The motivation for developing the Supervised Network Self-Organizing Map (SNet-SOM) model is to exploit this fact for designing computationally

The application of neural networks to domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Traditional distances are inadequate to access the differences between the symbolic patterns. This work proposes the utilization of a statistically extracted distance measure in the context of Generalized Radial Basis Function (GRBF) networks. The main