The wavelet transform as a tool for recognition of biosignals

The use of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of various signals. The first step in addressing pattern recognition problems is to define a representation that can be used for extracting the information content of signals. The sharp variation points of a signal amplitude are among the meaningful characterizations of the signal. The wavelet transform of the signal is found to be translation variant which makes it difficult for direct application in pattern recognition. However, the zero-crossings of a wavelet transform employing a particular class of wavelets can provide the translation invariant locations of the signal variation points. A zero-crossing representation augmented by the measure of the structure between the two consecutive zero-crossings has been studied by Stephane Mallat. On the basis of this representation, we demonstrate recognition of segments of biosignals embedded in streams of signals. The feasibility of employing zero-crossings of a wavelet transform as a tool in searching for a particular pattern class in the library of biosignals is explored.