Using Symbolic Analysis of Local Field Potentials for Anesthesia Depth Prediction

Depth of anesthesia (DOA) is a very important parameter, determining how the brain responds to various concentrations of anesthetic. Here, we assess the dependence of DOA on the concentration of isoflurane by analyzing local-field potentials (LFPs) recorded in mouse visual cortex. To capture the DOA-related changes, we employed TESPAR and Fourier-based feature extraction techniques, combined with machine learning. In addition, we performed an analysis of bursts present in LFP signals and evaluated their influence on DOA classification. These bursts are characterized by high amplitudes and low frequencies, requiring different analysis approaches: one that removes the high amplitudes and the other one that filters the lower frequencies. We developed procedures that successfully discriminate between different levels of anesthesia. Our study indicates a correlation between increased isoflurane dose and LFP amplitude suppression and also indicates that large amplitude bursts are expressed in the signal in a dose-dependent manner.