Automatic machine classification of patient anaesthesia levelsusing EEG signals

摘要

The authors explore the possibility of using EEG(electroencephalographic) signals for automatic machine classificationof the level of anesthesia that a patient is in. EEG data obtained underdifferent levels of anesthesia have been modeled as an AR(autoregressive) process for that purpose. It is shown that AR modelorder, the AR power spectral density, and the second and fourth momentsof the probability density function of the EEG signals can be used forclassifying the level of anesthesia into low, medium, and high levels