Traditional analysis methods are designed and optimized to handle signals that include a single class of patterns such as pure harmonics (Fourier Representation/ dictionary) or piece-wise constant functions (Wavelets Representation/ dictionary), in simplified and unreal case, simple operations such as threshold calculations or filtering in the appropriate representation space, can be very effective for separation of signal and noise (denoising), decomposition into basic components, pattern detection and more. However, such basic operations that use a single representation method usually yield mediocre results when applied to real complex biological signals as mentioned above especially in the case where the Signal to Noise Ratio (SNR) is very low. Recent trends in digital signal processing (DSP) use the novel idea of merging several different representation methods to create a so called over-complete dictionary, examples of this approach include the Matching Pursuit algorithm, described by Mallat et al (12) and the Basis Pursuit algorithm, described by Donoho et. al (13).