Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

WOS: 000318003100018In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also for reduction of single-trial numbers in BAEP pattern extraction process. Trial-to-trial variations in auditory evoked potential (AEP) are very important in quantifying dynamical properties of the nervous system and in specifying the group-specific effects in clinical applications. Due to the nonlinear dynamics of the AEP, a nonlinear adaptive filtering technique is considered as a powerful tool for tracking such variations. Therefore, we have designed a wavelet network-based nonlinear adaptive filter (WaNe-NAF) satisfying asymptotic stability in the sense of Lyapunov. The simulation results are verified that the proposed WaNe-NAF can effectively track the trial-to-trial variations. We have also compared the WaNe-NAF with the most widely used ensemble averaging technique using real measured human BAEP data. The WaNe-NAF shows promise for requiring less number of ensembles than conventional ensemble averaging method to attain adequate signal quality. As a result, the proposed filtering system is suggested as a powerful tool in AEP acquisition and processing systems.Scientific and Technological Research Council of Turkey (TUBITAK) [TUBITAK-105E084]This study has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with the project number of TUBITAK-105E084. We also appreciate Dr. Ozcan Ozdamar from University of Miami and his group in Neurosensory Engineering Lab. for obtaining a part of human BAEP data