Influence of wavelet boundary conditions on the classification of biological signals

Doctors utilized Brainstem Auditory Evoked Potentials (BAEP) to diagnose patients with multiple sclerosis. We use eight coefficients of each of the several wavelet transforms of the BAEP signals to train an artificial neural network with radial basis functions. We study how the boundary conditions used to determine the wavelet transforms affect the maximum number of correct diagnoses. Using this information, we establish the best strategy to avoid misleading information created by the boundary conditions