Tracking electroencephalographic changes using distributions of linear models: Application to propofol-based depth of anesthesia monitoring

Abstract

Objective: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. Methods: Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average - ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a..