Feature enhancement from nonlinear time series using linear-phase and nonlinear-phase time-delay fuzzy combiners

Time-delay fuzzy combiners are used to extract short-term transients embedded in longer-term, nonlinear, periodic signals. Linear-phase structures maintain the phase integrity of these signals and provide transient extraction with reduced phase distortion and lower prediction errors. Inference rule reduction within the fuzzy combiner can lead to greater processing efficiencies but at the expense of higher prediction errors and malformation of the extracted transients. This research is aimed at the decomposition of electroencephalograms to classify waveform transient and background rhythmic characteristics which is useful in managing neurophysiological conditions such as epilepsy