title: Extraction of short-latency evoked potentials using a combination of wavelets and evolutionary algorithms.
abstract: 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.
authors.affiliation: School of Technology and Design, University College Northampton, NN2 6JD, UK. scott.turner@northampton.ac.uk
authors.forename: S
authors.initials: S
authors.lastname: Turner
authors.forename: P
authors.initials: P
authors.lastname: Picton
authors.forename: J
authors.initials: J
authors.lastname: Campbell
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D004594:Electrophysiology / Q000379:methods*; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D006801:Humans; D008475:Median Nerve / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013116:Spinal Cord / Q000502:physiology; D013269:Stochastic Processes