Adaptive EEG transient event discrimination using dynamic LMS filter weight leakage

Abstract

The EEG is a highly complex and dynamic signal comprising a large ensemble of time-varying, statistical properties. Such diverse signal properties pose significant challenges in processing the EEG. A dynamic weight leakage based LMS adaptive linear predictor has been developed to discriminate for transient events within the EEG, and in particular, epileptiform discharges. The resulting procedure improves the SNR of these events by at least two-fold, leading to greater selectivity in subsequent epileptiform event detection stages.