Auto-mutual information function of the EEG as a measure of depth of anesthesia

Monitoring the depth of anesthesia (DOA) is necessary in order to decrease the incident of awareness in anesthesia and to prevent delays in the recovery phase. In the last decades a number of noninvasive methods have been proposed for the analysis of the electroencephalogram (EEG) for monitoring DOA. The objective of this work was to apply auto mutual information function (AMIF) to EEGs of patients under anesthesia in order to find variables able to characterize the following 4 states: awake, sedated, anesthetized and burst suppression episodes. The results show that the single and combined AMIF parameters were able to correctly classify the states in the range 72.2%-94.1% and 61.1%-100%, respectively.