Human magnetocardiogram (MCG) modeling using evolutionary artificial neural networks

In the present work magnetocardiogram (MCG) recordings of normal subjects were analyzed using a hybrid training algorithm. This algorithm combines genetic algorithms and a training method based on the localized Extended Kalman Filter (EKF), in order to evolve the structure and train Multi-Layered Perceptrons (MLP) networks. Our goal is to examine the predictability of the MCG signal on a short predicting horizon.