  Matching a wavelet to class of signals can be of interest in feature
detection and classification based on wavelet representation. The aim of this
work is to provide a quantitative approach to the problem of matching a wavelet
to electrogastrographic (EGG) signals. Visually inspected EGG recordings from
sixteen dogs and six volunteers were submitted to wavelet analysis.
Approximated wavelet-based versions of EGG signals were calculated using Pollen
parameterization of 6-tap wavelet filters and wavelet compression techniques.
Wavelet parameterization values that minimize the approximation error of
compressed EGG signals were sought and considered optimal. The wavelets
generated from the optimal parameterization values were remarkably similar to
the standard Daubechies-3 wavelet.
