Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of responses to produce one averaged signal. In this paper a post-processing technique is shown which uses a combination of wavelets and evolutionary algorithms to produce a representative waveform with fewer responses. The most suitable wavelets and a set of weights are selected by an evolutionary algorithm to form a filter bank, which enhances the extraction of evoked potentials from noisy recordings.
Evoked Potentials are electrical Signals produced by the body in response to a Stimulus. In general these Signals are noisy with a low Signal to noise ratio. In this Paper a method is proposed that uses sets of filters, whose tut-off frequencies are selected by an evolutionary algorithm. An evolutionary algorithm was investigated to limit the assumptions that were made about the Signals. The set of filters separately filter the evoked Potentials, and are combined as a weighted sum of the filter Outputs. The evolutionary algorithm also selects the weights. Inputs to the filters are sets of averaged Signal, 4 or 10 Signals per average. Even though there is likely to be variations between the Signals, this process tan improve the extraction of Potentials.