Limitations in the Rapid Extraction of Evoked Potentials Using Parametric Modeling

Abstract

The rapid extraction of variations in evoked potentials (EPs) is of great clinical importance. Parametric modeling using autoregression with an exogenous input (ARX) and robust evoked potential estimator (REPE) are commonly used methods for extracting EPs over the conventional moving time average. However, a systematic study of the efficacy of these methods, using known synthetic EPs, has not been performed. Therefore, the current study evaluates the restrictions of these methods in the presence of known and systematic variations in EP component latency and signal-to-noise ratios (SNR). In the context of rapid extraction, variations of wave V of the auditory brainstem in response to stimulus intensity were considered. While the REPE methods were better able to recover the simulated model of the EP, morphology and the latency of the ARX-estimated EPs was a closer match to the actual EP than than that of the REPE-estimated EPs. We, therefore, concluded that ARX rapid extraction would perform better with regards to the rapid tracking of latency variations. By tracking simulated and empirically induced latency variations, we conclude that rapid EP extraction using ARX modeling is only capable of extracting latency variations of an EP in relatively high SNRs and, therefore, should be used with caution in low-noise environments. In particular, it is not a suitable method for the rapid extraction of early EP components such as the auditory brainstem potential.