Transient discrimination in nonlinear time series using linear-phase time-delay neural networks

A linear-phase adaptive nonlinear predictor is presented which allows the adaptive discrimination of short-term transient signals from longer-term periodic signals whilst maintaining the phase integrity of both. A linear-phase time-delay neural network is used in an adaptive predictor configuration and is adaptively trained as a predictor for signals arising from nonlinear processes. The targeted application of this technique is the decomposition of electroencephalograms to classify waveform transient and background rhythmic characteristics which is useful in managing neurophysiological conditions such as epilepsy