ABSTRACT
A common problem in biomedical signal processing is that of detecting the presence of a wavelet in a noisy signal. A wavelet is considered as any real valued function of time, possessing some structure. The approximate knowledge of the wavelet’s shape can be used to select typical structural features. A more general approach is the one where the approximate knowledge on the wavelet is used to generate a template, which is some average wavelet determined by the a priori knowledge on the signal. Wavelet detection is required in many biomedical signal processing applications. Sophisticated algorithms are available to detect the presence of a wavelet by analyzing its structure. The problem of detecting wavelets in noise is an important problem in communication theory. The Visual evoked responses are assumed to be an aggregate of overlapping wavelets, generated by multiple spatially disparate sources. The various wavelets are unknown in their exact shape and timing.
